system

Abstract

The system according to this embodiment aims to provide an environment in which employees can easily ask questions and seek advice regarding their work. [Solution] The system according to the embodiment comprises a learning unit, a reception unit, and an answering unit. The learning unit learns knowledge specific to the operations of each department and headquarters. The reception unit receives questions and consultations from employees. The answering unit provides answers to questions and consultations received by the reception unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for employees to easily ask questions or consult about work, and there is a risk of reducing work efficiency.

[0005] The system according to the embodiment aims to provide an environment in which employees can easily ask questions or consult about work.

Means for Solving the Problems

[0006] The system according to the embodiment includes a learning unit, a reception unit, and a response unit. The learning unit learns knowledge specialized for the work of each general affairs department and head office. The reception unit receives questions and consultations from employees. The response unit provides answers to the questions and consultations received by the reception unit.

Effects of the Invention

[0007] The system according to this embodiment can provide an environment in which employees can easily ask questions and seek advice regarding their work. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The employee support system according to an embodiment of the present invention is a system designed to improve employee productivity by leveraging the advantages of both teleworking and in-office work. This employee support system learns knowledge specific to the operations of each department and headquarters, and functions as a platform where employees can easily engage in conversations, casual chats, and ask questions when needed. Specifically, the employee support system learns knowledge using generative AI and provides appropriate answers to employee questions and consultations. Employees can ask questions and seek advice from the employee support system via chat or voice input, and the employee support system provides immediate answers. This makes it easier for employees to concentrate on their work and improves work efficiency. It can also compensate for the disadvantages of remote work and reduce employee stress. For example, when an employee enters a work-related question via chat, the generative AI provides an appropriate answer to that question. The employee support system also accepts consultations from employees via voice input, and the generative AI provides appropriate advice to those consultations. Furthermore, the employee support system can estimate the emotions of employees and adjust the learning content and the expression of answers based on the estimated emotions. This allows the employee support system to provide support tailored to the individual needs of employees. This allows the employee support system to improve work efficiency by providing appropriate answers to employee questions and concerns.

[0029] The employee support system according to this embodiment comprises a learning unit, a reception unit, and an answering unit. The learning unit learns knowledge specific to the operations of each department and headquarters. The learning unit learns knowledge related to the operations of each department and headquarters, for example, using a generative AI. The generative AI analyzes documents related to operations using a text generation AI (e.g., LLM) and learns important information. The learning unit can also improve the accuracy of its learning by referring to past business data. For example, the learning unit refers to past project data and learns knowledge related to similar operations. The reception unit receives questions and consultations from employees. The reception unit receives questions and consultations from employees through, for example, chat or voice input. Chat receives questions from employees using, for example, a text-based chat system. Voice input receives consultations from employees using, for example, speech recognition technology. The answering unit provides answers to questions and consultations received by the reception unit. The answering unit provides appropriate answers to questions and consultations using, for example, a generative AI. The generative AI generates answers to questions using, for example, a text generation AI. Furthermore, the response unit can provide immediate answers to employee questions and consultations. For example, the response unit can provide immediate answers to questions using real-time processing. As a result, the employee support system according to this embodiment can improve work efficiency by providing appropriate answers to employee questions and consultations.

[0030] The learning unit learns knowledge specific to the operations of each department and headquarters. For example, the learning unit uses generative AI to learn knowledge related to the operations of each department and headquarters. The generative AI analyzes documents related to operations using text generation AI (e.g., LLM), extracts important information, and learns from it. Specifically, the generative AI receives a large amount of document data related to operations as input and analyzes the content of the documents using natural language processing technology. In the analysis process, it extracts keywords and phrases from the documents and evaluates their relationships to identify important knowledge in the operations. For example, when analyzing documents related to project management, the generative AI extracts important elements such as project progress, risk management, and resource allocation, and learns from them. The generative AI can also summarize the content of the documents and concisely summarize the important points. This allows the learning unit to efficiently learn vast amounts of information related to operations and quickly acquire the necessary knowledge. Furthermore, the learning unit can also improve the accuracy of its learning by referring to past operations data. For example, the learning unit can refer to past project data and learn knowledge related to similar operations. Past data includes successful and unsuccessful project case studies and problem-solving processes. By analyzing this data, the learning department can gain more practical and useful knowledge. By accumulating and continuously updating this knowledge, the learning department can always maintain up-to-date business knowledge. This allows the learning department to quickly and accurately provide employees with the business knowledge they need, contributing to improved efficiency and quality in operations.

[0031] The reception department receives questions and consultations from employees. The reception department accepts questions and consultations from employees through methods such as chat and voice input. Chat, for example, uses a text-based chat system to receive employee questions. Specifically, employees can ask questions and seek advice by sending text messages using a dedicated chat application. The chat system receives messages in real time and forwards them to the reception department. Voice input, for example, uses speech recognition technology to receive employee consultations. Employees can ask questions and seek advice by voice using a dedicated voice input device. Speech recognition technology converts the voice data into text data and forwards it to the reception department. This allows the reception department to receive employee questions and consultations quickly and efficiently. Furthermore, the reception department can categorize the received questions and consultations and forward them to the appropriate department or person in charge. For example, technical questions are forwarded to the technical support department, and consultations regarding business processes are forwarded to the business improvement department. This ensures that employee questions and consultations are promptly addressed by the appropriate experts. The reception department also records the content of questions and consultations and stores them in a database for later reference. This allows for quick responses to similar issues by referring to past questions and consultations. Through these functions, the reception department can provide prompt and appropriate responses to employee questions and consultations, contributing to increased operational efficiency and employee satisfaction.

[0032] The response department provides answers to questions and consultations received by the reception department. The response department uses, for example, generative AI to provide appropriate answers to questions and consultations. Specifically, the generative AI analyzes the content of questions and consultations forwarded from the reception department and generates appropriate answers. The generative AI utilizes natural language processing technology to accurately understand the intent of the question and generates the optimal answer based on that understanding. For example, for questions regarding business processes, it provides relevant business procedures and best practices; for technical questions, it provides specific solutions and reference materials. The generative AI can also refer to a database of past questions and answers and generate answers based on past answers to similar questions. This allows the response department to provide quick and accurate answers to questions and consultations. Furthermore, the response department can provide immediate answers to employee questions and consultations. For example, the response department uses real-time processing to provide immediate answers to questions. This allows employees to obtain answers quickly without waiting, improving work efficiency. The response department, utilizing AI-generated responses, can respond quickly and accurately to a vast number of questions and inquiries. Furthermore, the response department can continuously evaluate and improve the quality of the responses it provides. For example, by collecting feedback from employees and reviewing the content and delivery method of responses, it can provide higher-quality support. This enables the response department to respond quickly and appropriately to employee questions and inquiries, contributing to increased operational efficiency and employee satisfaction.

[0033] The learning unit can learn knowledge specific to the operations of each department and headquarters using generative AI. For example, the learning unit learns knowledge related to the operations of each department and headquarters using generative AI. The generative AI analyzes documents related to operations using, for example, text generation AI (e.g., LLM), extracts important information, and learns from it. The learning unit can also improve the accuracy of its learning by referring to past operational data. For example, the learning unit refers to past project data and learns knowledge related to similar operations. This improves the accuracy of learning by using generative AI.

[0034] The reception desk can receive employee questions and consultations through chat or voice input. For example, the reception desk can receive employee questions via chat. Chat can be, for example, a text-based chat system. The reception desk can also receive employee consultations through voice input. Voice input can be, for example, a voice recognition technology. This makes it easy for employees to use the service by accepting questions and consultations through chat or voice input. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can receive employee questions using a chat system, and AI can analyze those questions and provide appropriate responses.

[0035] The response unit can provide appropriate answers to questions and consultations received by the reception unit using a generation AI. For example, the response unit uses a generation AI to provide appropriate answers to questions and consultations. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate answers to questions. Furthermore, the response unit can improve the accuracy of its answers to questions and consultations by referring to past data. For example, the response unit analyzes past question and answer data to provide the optimal answer. This improves the accuracy of providing appropriate answers by using a generation AI.

[0036] The response unit can provide immediate answers to employee questions and inquiries. For example, the response unit uses real-time processing to provide immediate answers to questions. This improves employee work efficiency by providing immediate answers. Some or all of the processing described above in the response unit may be performed using AI, or not. For example, the response unit can provide immediate answers using an AI model that generates answers to questions in real time.

[0037] The response unit can provide advice to improve employee productivity. For example, the response unit provides advice on improving employee productivity. This advice may concern time management methods or efficient work procedures. By providing productivity-enhancing advice, the efficiency of employees' work will improve. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can provide appropriate advice using an AI model that generates advice on improving employee productivity.

[0038] The response section can provide stress reduction advice to compensate for the disadvantages of remote work. For example, the response section can provide stress reduction advice to compensate for the disadvantages of remote work. The advice may include, for example, relaxation techniques or methods of mental health care. By providing stress reduction advice, employee stress is reduced. Some or all of the above processing in the response section may be performed using, for example, AI, or not using AI. For example, the response section can provide appropriate advice using an AI model that generates stress reduction advice.

[0039] The learning unit can improve the accuracy of its learning by referring to past business data. For example, the learning unit can refer to past project data to learn knowledge about similar tasks. It can also analyze past business reports to learn frequently occurring problems and solutions. Furthermore, the learning unit can refer to past meeting records to learn important agenda items and decisions. This improves the accuracy of learning by referring to past business data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input past business data into a generative AI and have the generative AI generate the learning content.

[0040] The learning unit can dynamically change the scope of knowledge to be learned according to the progress of the work. For example, the learning unit can prioritize learning the necessary knowledge according to the progress of the project. Furthermore, if an urgent task arises, the learning unit can immediately learn the knowledge related to that task. In addition, the learning unit can predict and learn the knowledge that will be needed next, according to the progress of the work. This allows for timely learning of necessary knowledge by changing the scope of knowledge according to the progress of the work. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input work progress data into a generative AI and have the generative AI dynamically change the learning scope.

[0041] The learning department can customize the scope of knowledge to be learned according to the employee's position and experience. For example, the learning department can prioritize basic knowledge for new employees. It can also prioritize specialized knowledge for mid-career employees. Furthermore, it can prioritize leadership and management knowledge for managers. By customizing the scope of knowledge according to the employee's position and experience, more appropriate learning becomes possible. Some or all of the above processing in the learning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning department can input employee position and experience data into a generative AI and have the generative AI perform the customization of the learning scope.

[0042] The learning unit can adjust the scope of knowledge it learns in accordance with seasonal fluctuations in business operations. For example, during the busy year-end period, the learning unit prioritizes learning knowledge related to efficient business processing. It can also prioritize learning knowledge related to new business processes at the start of a new fiscal year. Furthermore, the learning unit can learn knowledge related to specific seasonal tasks at the appropriate time. This allows for timely learning of necessary knowledge by adjusting the scope of knowledge in accordance with seasonal fluctuations in business operations. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without one. For example, the learning unit can input seasonal fluctuation data of business operations into a generative AI and have the generative AI adjust the learning scope.

[0043] The reception department can classify the content of incoming questions and consultations by referring to past data and provide appropriate responses. For example, the reception department can analyze past question data and automatically classify frequently asked questions. It can also refer to past consultation content and suggest appropriate response methods. Furthermore, the reception department can classify the content of questions and consultations by category based on past data. This allows for appropriate responses by classifying questions and consultations by referring to past data. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input past question data into AI and have the AI ​​perform the question classification.

[0044] The reception department can prioritize the content of the questions and consultations it receives according to the progress of its work. For example, the reception department can prioritize urgent questions and consultations. It can also prioritize important questions and consultations according to the progress of its work. Furthermore, the reception department can predict and respond to the next questions and consultations that will be needed as the work progresses. In this way, by prioritizing questions and consultations according to the progress of the work, important questions and consultations can be addressed preferentially. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input work progress data into AI and have the AI ​​determine the priority of questions and consultations.

[0045] The reception desk can filter the content of questions and consultations received according to the employee's position and experience. For example, the reception desk can provide basic answers to questions from new employees. It can also provide specialized answers to questions from mid-career employees. Furthermore, it can provide answers related to leadership and management to questions from managers. By filtering questions and consultations according to the employee's position and experience, more appropriate responses can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input employee position and experience data into the AI ​​and have the AI ​​perform the filtering of questions and consultations.

[0046] The reception department can adjust the content of questions and consultations it receives according to seasonal fluctuations in business operations. For example, during the busy year-end period, the reception department can prioritize questions related to efficient business processes. Similarly, at the start of a new fiscal year, the reception department can prioritize questions related to new business processes. Furthermore, the reception department can address questions related to specific seasonal tasks at the appropriate time. This allows for timely responses to necessary questions and consultations by adjusting the content of questions and consultations according to seasonal fluctuations in business operations. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department could input seasonal fluctuation data into the AI ​​and have the AI ​​adjust the content of questions and consultations.

[0047] The answering unit can improve the accuracy of the answers it provides by referring to past data. For example, the answering unit can analyze past question and answer data to provide the optimal answer. It can also refer to past consultation content and suggest appropriate response methods. Furthermore, based on past data, the answering unit can classify the content of questions and consultations into categories and provide the optimal answer. This improves the accuracy of the answers by referring to past data. Some or all of the above processing in the answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the answering unit can input past question data into a generative AI and have the generative AI generate the answers.

[0048] The response unit can dynamically change the content of the responses it provides according to the progress of the work. For example, the response unit can prioritize providing the necessary responses according to the progress of the project. Furthermore, if an urgent task arises, the response unit can immediately provide the relevant responses. In addition, the response unit can predict and provide the next necessary responses according to the progress of the work. This allows for the timely provision of necessary responses by changing the content of the responses according to the progress of the work. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the response unit can input work progress data into a generating AI and have the generating AI dynamically change the content of the responses.

[0049] The response unit can customize the content of the answers it provides according to the employee's position and experience. For example, the response unit can provide basic answers to new employees. It can also provide specialized answers to mid-career employees. Furthermore, it can provide answers related to leadership and management to managers. By customizing the content of the answers according to the employee's position and experience, more appropriate answers are provided. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input employee position and experience data into a generative AI and have the generative AI perform the customization of the answer content.

[0050] The response unit can adjust the content of the responses it provides according to seasonal fluctuations in business operations. For example, during the busy year-end period, the response unit can prioritize providing responses related to efficient business processing. It can also prioritize providing responses related to new business processes at the start of a new fiscal year. Furthermore, the response unit can provide responses related to specific seasonal tasks at appropriate times. This allows for timely provision of necessary responses by adjusting the content of responses according to seasonal fluctuations in business operations. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the response unit can input seasonal fluctuation data of business operations into a generating AI and have the generating AI adjust the content of the responses.

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

[0052] The employee support system can monitor employees' health status and adjust support based on that status. For example, if an employee reports feeling unwell, the system can provide advice encouraging them to rest. It can also suggest that employees take appropriate breaks if they are working long hours. Furthermore, it can analyze employee health data and provide advice for maintaining good health. This allows for support tailored to each employee's health condition, thereby promoting employee well-being and improving work efficiency.

[0053] An employee support system can assess employees' skill levels and provide training programs tailored to those levels. For example, it can offer basic training to new employees and specialized training to mid-career employees. It can also provide leadership and management training to managers. Furthermore, it can periodically assess employees' skill levels and update training programs accordingly. This allows for employee growth and improved work efficiency by providing training that matches each employee's skill level.

[0054] An employee support system can monitor employees' work status and adjust support based on that status. For example, if an employee is overworked, the system can provide advice to encourage them to rest. It can also provide advice to improve concentration if an employee is not concentrating on their work. Furthermore, it can analyze employees' work status and suggest more efficient work methods. This allows for improved work efficiency by providing support tailored to each employee's work situation.

[0055] An employee support system can understand employees' career goals and provide support tailored to those goals. For example, if an employee is aiming for promotion, it can provide advice on acquiring the necessary skills and knowledge. It can also provide information to assist an employee in their job search if they are considering changing jobs. Furthermore, it can regularly review employees' career goals and update support accordingly. This allows for improved employee growth and satisfaction by providing support aligned with employees' career goals.

[0056] An employee support system can provide support to improve employees' communication skills. For example, if an employee has difficulty communicating, it can advise them on effective communication methods. It can also suggest team-building methods if an employee wants to improve teamwork. Furthermore, it can regularly evaluate employees' communication skills and provide training to improve those skills. This can lead to improved team collaboration and increased work efficiency by enhancing employees' communication skills.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The learning unit acquires knowledge specific to the operations of each department and headquarters. For example, it uses generative AI to analyze documents related to operations, extracting and learning important information. It can also improve the accuracy of its learning by referring to past operational data. Step 2: The reception desk handles employee questions and consultations. For example, it accepts employee questions and consultations via chat or voice input. Chat uses a text-based chat system, and voice input uses speech recognition technology. Step 3: The response unit provides answers to questions and inquiries received by the reception unit. For example, it can use a generative AI to provide appropriate answers to questions and inquiries. The generative AI can generate answers to questions using text generation AI and provide answers immediately using real-time processing.

[0059] (Example of form 2) The employee support system according to an embodiment of the present invention is a system designed to improve employee productivity by leveraging the advantages of both teleworking and in-office work. This employee support system learns knowledge specific to the operations of each department and headquarters, and functions as a platform where employees can easily engage in conversations, casual chats, and ask questions when needed. Specifically, the employee support system learns knowledge using generative AI and provides appropriate answers to employee questions and consultations. Employees can ask questions and seek advice from the employee support system via chat or voice input, and the employee support system provides immediate answers. This makes it easier for employees to concentrate on their work and improves work efficiency. It can also compensate for the disadvantages of remote work and reduce employee stress. For example, when an employee enters a work-related question via chat, the generative AI provides an appropriate answer to that question. The employee support system also accepts consultations from employees via voice input, and the generative AI provides appropriate advice to those consultations. Furthermore, the employee support system can estimate the emotions of employees and adjust the learning content and the expression of answers based on the estimated emotions. This allows the employee support system to provide support tailored to the individual needs of employees. This allows the employee support system to improve work efficiency by providing appropriate answers to employee questions and concerns.

[0060] The employee support system according to this embodiment comprises a learning unit, a reception unit, and an answering unit. The learning unit learns knowledge specific to the operations of each department and headquarters. The learning unit learns knowledge related to the operations of each department and headquarters, for example, using a generative AI. The generative AI analyzes documents related to operations using a text generation AI (e.g., LLM) and learns important information. The learning unit can also improve the accuracy of its learning by referring to past business data. For example, the learning unit refers to past project data and learns knowledge related to similar operations. The reception unit receives questions and consultations from employees. The reception unit receives questions and consultations from employees through, for example, chat or voice input. Chat receives questions from employees using, for example, a text-based chat system. Voice input receives consultations from employees using, for example, speech recognition technology. The answering unit provides answers to questions and consultations received by the reception unit. The answering unit provides appropriate answers to questions and consultations using, for example, a generative AI. The generative AI generates answers to questions using, for example, a text generation AI. Furthermore, the response unit can provide immediate answers to employee questions and consultations. For example, the response unit can provide immediate answers to questions using real-time processing. As a result, the employee support system according to this embodiment can improve work efficiency by providing appropriate answers to employee questions and consultations.

[0061] The learning unit learns knowledge specific to the operations of each department and headquarters. For example, the learning unit uses generative AI to learn knowledge related to the operations of each department and headquarters. The generative AI analyzes documents related to operations using text generation AI (e.g., LLM), extracts important information, and learns from it. Specifically, the generative AI receives a large amount of document data related to operations as input and analyzes the content of the documents using natural language processing technology. In the analysis process, it extracts keywords and phrases from the documents and evaluates their relationships to identify important knowledge in the operations. For example, when analyzing documents related to project management, the generative AI extracts important elements such as project progress, risk management, and resource allocation, and learns from them. The generative AI can also summarize the content of the documents and concisely summarize the important points. This allows the learning unit to efficiently learn vast amounts of information related to operations and quickly acquire the necessary knowledge. Furthermore, the learning unit can also improve the accuracy of its learning by referring to past operations data. For example, the learning unit can refer to past project data and learn knowledge related to similar operations. Past data includes successful and unsuccessful project case studies and problem-solving processes. By analyzing this data, the learning department can gain more practical and useful knowledge. By accumulating and continuously updating this knowledge, the learning department can always maintain up-to-date business knowledge. This allows the learning department to quickly and accurately provide employees with the business knowledge they need, contributing to improved efficiency and quality in operations.

[0062] The reception department receives questions and consultations from employees. The reception department accepts questions and consultations from employees through methods such as chat and voice input. Chat, for example, uses a text-based chat system to receive employee questions. Specifically, employees can ask questions and seek advice by sending text messages using a dedicated chat application. The chat system receives messages in real time and forwards them to the reception department. Voice input, for example, uses speech recognition technology to receive employee consultations. Employees can ask questions and seek advice by voice using a dedicated voice input device. Speech recognition technology converts the voice data into text data and forwards it to the reception department. This allows the reception department to receive employee questions and consultations quickly and efficiently. Furthermore, the reception department can categorize the received questions and consultations and forward them to the appropriate department or person in charge. For example, technical questions are forwarded to the technical support department, and consultations regarding business processes are forwarded to the business improvement department. This ensures that employee questions and consultations are promptly addressed by the appropriate experts. The reception department also records the content of questions and consultations and stores them in a database for later reference. This allows for quick responses to similar issues by referring to past questions and consultations. Through these functions, the reception department can provide prompt and appropriate responses to employee questions and consultations, contributing to increased operational efficiency and employee satisfaction.

[0063] The response department provides answers to questions and consultations received by the reception department. The response department uses, for example, generative AI to provide appropriate answers to questions and consultations. Specifically, the generative AI analyzes the content of questions and consultations forwarded from the reception department and generates appropriate answers. The generative AI utilizes natural language processing technology to accurately understand the intent of the question and generates the optimal answer based on that understanding. For example, for questions regarding business processes, it provides relevant business procedures and best practices; for technical questions, it provides specific solutions and reference materials. The generative AI can also refer to a database of past questions and answers and generate answers based on past answers to similar questions. This allows the response department to provide quick and accurate answers to questions and consultations. Furthermore, the response department can provide immediate answers to employee questions and consultations. For example, the response department uses real-time processing to provide immediate answers to questions. This allows employees to obtain answers quickly without waiting, improving work efficiency. The response department, utilizing AI-generated responses, can respond quickly and accurately to a vast number of questions and inquiries. Furthermore, the response department can continuously evaluate and improve the quality of the responses it provides. For example, by collecting feedback from employees and reviewing the content and delivery method of responses, it can provide higher-quality support. This enables the response department to respond quickly and appropriately to employee questions and inquiries, contributing to increased operational efficiency and employee satisfaction.

[0064] The learning unit can learn knowledge specific to the operations of each department and headquarters using generative AI. For example, the learning unit learns knowledge related to the operations of each department and headquarters using generative AI. The generative AI analyzes documents related to operations using, for example, text generation AI (e.g., LLM), extracts important information, and learns from it. The learning unit can also improve the accuracy of its learning by referring to past operational data. For example, the learning unit refers to past project data and learns knowledge related to similar operations. This improves the accuracy of learning by using generative AI.

[0065] The reception desk can receive employee questions and consultations through chat or voice input. For example, the reception desk can receive employee questions via chat. Chat can be, for example, a text-based chat system. The reception desk can also receive employee consultations through voice input. Voice input can be, for example, a voice recognition technology. This makes it easy for employees to use the service by accepting questions and consultations through chat or voice input. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can receive employee questions using a chat system, and AI can analyze those questions and provide appropriate responses.

[0066] The response unit can provide appropriate answers to questions and consultations received by the reception unit using a generation AI. For example, the response unit uses a generation AI to provide appropriate answers to questions and consultations. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate answers to questions. Furthermore, the response unit can improve the accuracy of its answers to questions and consultations by referring to past data. For example, the response unit analyzes past question and answer data to provide the optimal answer. This improves the accuracy of providing appropriate answers by using a generation AI.

[0067] The response unit can provide immediate answers to employee questions and inquiries. For example, the response unit uses real-time processing to provide immediate answers to questions. This improves employee work efficiency by providing immediate answers. Some or all of the processing described above in the response unit may be performed using AI, or not. For example, the response unit can provide immediate answers using an AI model that generates answers to questions in real time.

[0068] The response unit can provide advice to improve employee productivity. For example, the response unit provides advice on improving employee productivity. This advice may concern time management methods or efficient work procedures. By providing productivity-enhancing advice, the efficiency of employees' work will improve. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can provide appropriate advice using an AI model that generates advice on improving employee productivity.

[0069] The response section can provide stress reduction advice to compensate for the disadvantages of remote work. For example, the response section can provide stress reduction advice to compensate for the disadvantages of remote work. The advice may include, for example, relaxation techniques or methods of mental health care. By providing stress reduction advice, employee stress is reduced. Some or all of the above processing in the response section may be performed using, for example, AI, or not using AI. For example, the response section can provide appropriate advice using an AI model that generates stress reduction advice.

[0070] The learning unit can estimate employees' emotions and adjust learning content based on those emotions. For example, if an employee is stressed, the learning unit will prioritize learning content that promotes relaxation. Conversely, if an employee is highly motivated, the learning unit can also learn challenging content. Furthermore, if an employee is tired, the learning unit can learn simple and easy-to-understand content. This allows for more effective learning by adjusting learning content according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input employee emotion data into a generative AI and have the generative AI adjust the learning content.

[0071] The learning unit can improve the accuracy of its learning by referring to past business data. For example, the learning unit can refer to past project data to learn knowledge about similar tasks. It can also analyze past business reports to learn frequently occurring problems and solutions. Furthermore, the learning unit can refer to past meeting records to learn important agenda items and decisions. This improves the accuracy of learning by referring to past business data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input past business data into a generative AI and have the generative AI generate the learning content.

[0072] The learning unit can dynamically change the scope of knowledge to be learned according to the progress of the work. For example, the learning unit can prioritize learning the necessary knowledge according to the progress of the project. Furthermore, if an urgent task arises, the learning unit can immediately learn the knowledge related to that task. In addition, the learning unit can predict and learn the knowledge that will be needed next, according to the progress of the work. This allows for timely learning of necessary knowledge by changing the scope of knowledge according to the progress of the work. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input work progress data into a generative AI and have the generative AI dynamically change the learning scope.

[0073] The learning unit can estimate employees' emotions and determine learning priorities based on those estimated emotions. For example, if an employee is stressed, the learning unit will prioritize learning content that promotes relaxation. It can also prioritize challenging content if an employee is highly motivated. Furthermore, if an employee is tired, the learning unit can prioritize learning content that is easy to understand. This allows for more effective learning by prioritizing learning according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can input employee emotion data into a generative AI and have the generative AI determine learning priorities.

[0074] The learning department can customize the scope of knowledge to be learned according to the employee's position and experience. For example, the learning department can prioritize basic knowledge for new employees. It can also prioritize specialized knowledge for mid-career employees. Furthermore, it can prioritize leadership and management knowledge for managers. By customizing the scope of knowledge according to the employee's position and experience, more appropriate learning becomes possible. Some or all of the above processing in the learning department may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning department can input employee position and experience data into a generative AI and have the generative AI perform the customization of the learning scope.

[0075] The learning unit can adjust the scope of knowledge it learns in accordance with seasonal fluctuations in business operations. For example, during the busy year-end period, the learning unit prioritizes learning knowledge related to efficient business processing. It can also prioritize learning knowledge related to new business processes at the start of a new fiscal year. Furthermore, the learning unit can learn knowledge related to specific seasonal tasks at the appropriate time. This allows for timely learning of necessary knowledge by adjusting the scope of knowledge in accordance with seasonal fluctuations in business operations. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without one. For example, the learning unit can input seasonal fluctuation data of business operations into a generative AI and have the generative AI adjust the learning scope.

[0076] The reception desk can estimate an employee's emotions and adjust its response based on the estimated emotions. For example, if an employee is stressed, the reception desk will use gentle language. If an employee is relaxed, the reception desk can also provide a friendly response. Furthermore, if an employee is in a hurry, the reception desk can respond quickly. By adjusting the reception desk's response according to the employee's emotions, more appropriate responses become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input employee emotion data into a generative AI and have the generative AI adjust its response.

[0077] The reception department can classify the content of incoming questions and consultations by referring to past data and provide appropriate responses. For example, the reception department can analyze past question data and automatically classify frequently asked questions. It can also refer to past consultation content and suggest appropriate response methods. Furthermore, the reception department can classify the content of questions and consultations by category based on past data. This allows for appropriate responses by classifying questions and consultations by referring to past data. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input past question data into AI and have the AI ​​perform the question classification.

[0078] The reception department can prioritize the content of the questions and consultations it receives according to the progress of its work. For example, the reception department can prioritize urgent questions and consultations. It can also prioritize important questions and consultations according to the progress of its work. Furthermore, the reception department can predict and respond to the next questions and consultations that will be needed as the work progresses. In this way, by prioritizing questions and consultations according to the progress of the work, important questions and consultations can be addressed preferentially. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input work progress data into AI and have the AI ​​determine the priority of questions and consultations.

[0079] The reception desk can estimate the emotions of employees and adjust the response speed based on the estimated emotions. For example, if an employee is stressed, the reception desk will respond quickly. Conversely, if an employee is relaxed, the reception desk can respond slowly and carefully. Furthermore, if an employee is in a hurry, the reception desk can respond in the shortest possible time. By adjusting the response speed according to the employee's emotions, more appropriate responses become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input employee emotion data into a generative AI and have the generative AI adjust the response speed.

[0080] The reception desk can filter the content of questions and consultations received according to the employee's position and experience. For example, the reception desk can provide basic answers to questions from new employees. It can also provide specialized answers to questions from mid-career employees. Furthermore, it can provide answers related to leadership and management to questions from managers. By filtering questions and consultations according to the employee's position and experience, more appropriate responses can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input employee position and experience data into the AI ​​and have the AI ​​perform the filtering of questions and consultations.

[0081] The reception department can adjust the content of questions and consultations it receives according to seasonal fluctuations in business operations. For example, during the busy year-end period, the reception department can prioritize questions related to efficient business processes. Similarly, at the start of a new fiscal year, the reception department can prioritize questions related to new business processes. Furthermore, the reception department can address questions related to specific seasonal tasks at the appropriate time. This allows for timely responses to necessary questions and consultations by adjusting the content of questions and consultations according to seasonal fluctuations in business operations. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department could input seasonal fluctuation data into the AI ​​and have the AI ​​adjust the content of questions and consultations.

[0082] The response unit can estimate the employee's emotions and adjust the way it expresses its response based on those emotions. For example, if the employee is stressed, the response unit will respond using gentle language. It can also respond in a friendly manner if the employee is relaxed. Furthermore, if the employee is in a hurry, the response unit can provide a concise and quick response. This allows for more appropriate responses by adjusting the expression of the response according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input employee emotion data into a generative AI and have the generative AI adjust the expression of the response.

[0083] The answering unit can improve the accuracy of the answers it provides by referring to past data. For example, the answering unit can analyze past question and answer data to provide the optimal answer. It can also refer to past consultation content and suggest appropriate response methods. Furthermore, based on past data, the answering unit can classify the content of questions and consultations into categories and provide the optimal answer. This improves the accuracy of the answers by referring to past data. Some or all of the above processing in the answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the answering unit can input past question data into a generative AI and have the generative AI generate the answers.

[0084] The response unit can dynamically change the content of the responses it provides according to the progress of the work. For example, the response unit can prioritize providing the necessary responses according to the progress of the project. Furthermore, if an urgent task arises, the response unit can immediately provide the relevant responses. In addition, the response unit can predict and provide the next necessary responses according to the progress of the work. This allows for the timely provision of necessary responses by changing the content of the responses according to the progress of the work. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the response unit can input work progress data into a generating AI and have the generating AI dynamically change the content of the responses.

[0085] The response unit can estimate an employee's emotions and determine the priority of responses based on the estimated emotions. For example, if an employee is stressed, the response unit can provide a quick response. If an employee is relaxed, the response unit can also provide a detailed response. Furthermore, if an employee is in a hurry, the response unit can provide a concise and quick response. This allows for more appropriate responses to be provided by prioritizing responses according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input employee emotion data into a generative AI and have the generative AI determine the priority of responses.

[0086] The response unit can customize the content of the answers it provides according to the employee's position and experience. For example, the response unit can provide basic answers to new employees. It can also provide specialized answers to mid-career employees. Furthermore, it can provide answers related to leadership and management to managers. By customizing the content of the answers according to the employee's position and experience, more appropriate answers are provided. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input employee position and experience data into a generative AI and have the generative AI perform the customization of the answer content.

[0087] The response unit can adjust the content of the responses it provides according to seasonal fluctuations in business operations. For example, during the busy year-end period, the response unit can prioritize providing responses related to efficient business processing. It can also prioritize providing responses related to new business processes at the start of a new fiscal year. Furthermore, the response unit can provide responses related to specific seasonal tasks at appropriate times. This allows for timely provision of necessary responses by adjusting the content of responses according to seasonal fluctuations in business operations. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or without a generating AI. For example, the response unit can input seasonal fluctuation data of business operations into a generating AI and have the generating AI adjust the content of the responses.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The employee support system can monitor employees' health status and adjust support based on that status. For example, if an employee reports feeling unwell, the system can provide advice encouraging them to rest. It can also suggest that employees take appropriate breaks if they are working long hours. Furthermore, it can analyze employee health data and provide advice for maintaining good health. This allows for support tailored to each employee's health condition, thereby promoting employee well-being and improving work efficiency.

[0090] An employee support system can assess employees' skill levels and provide training programs tailored to those levels. For example, it can offer basic training to new employees and specialized training to mid-career employees. It can also provide leadership and management training to managers. Furthermore, it can periodically assess employees' skill levels and update training programs accordingly. This allows for employee growth and improved work efficiency by providing training that matches each employee's skill level.

[0091] An employee support system can monitor employees' work status and adjust support based on that status. For example, if an employee is overworked, the system can provide advice to encourage them to rest. It can also provide advice to improve concentration if an employee is not concentrating on their work. Furthermore, it can analyze employees' work status and suggest more efficient work methods. This allows for improved work efficiency by providing support tailored to each employee's work situation.

[0092] An employee support system can understand employees' career goals and provide support tailored to those goals. For example, if an employee is aiming for promotion, it can provide advice on acquiring the necessary skills and knowledge. It can also provide information to assist an employee in their job search if they are considering changing jobs. Furthermore, it can regularly review employees' career goals and update support accordingly. This allows for improved employee growth and satisfaction by providing support aligned with employees' career goals.

[0093] An employee support system can provide support to improve employees' communication skills. For example, if an employee has difficulty communicating, it can advise them on effective communication methods. It can also suggest team-building methods if an employee wants to improve teamwork. Furthermore, it can regularly evaluate employees' communication skills and provide training to improve those skills. This can lead to improved team collaboration and increased work efficiency by enhancing employees' communication skills.

[0094] The employee support system can estimate employees' emotions and suggest activities for refreshment based on those estimates. For example, if an employee is feeling stressed, it can suggest relaxation activities. If an employee is tired, it can suggest light exercise or stretching. Furthermore, if an employee wants to boost their motivation, it can suggest inspiring activities. By providing refreshment activities tailored to employees' emotions, the system can reduce employee stress and improve work efficiency.

[0095] The employee support system can estimate employees' emotions and provide mental health care support based on those estimates. For example, if an employee is feeling anxious, it can offer counseling opportunities. If an employee is feeling down, it can send encouraging messages. Furthermore, if an employee is relaxed, it can suggest activities to help them refresh. By providing mental health care support tailored to employees' emotions, this system can help maintain their mental health and improve work efficiency.

[0096] An employee support system can estimate an employee's emotions and adjust the feedback method based on those emotions. For example, if an employee is stressed, feedback can be provided using gentle language. If an employee is relaxed, feedback can be provided using friendly language. Furthermore, if an employee is in a hurry, concise and quick feedback can be provided. This allows for more effective feedback by adjusting the feedback method according to the employee's emotions.

[0097] An employee support system can estimate an employee's emotions and provide support to boost their motivation based on those emotions. For example, if an employee is feeling unmotivated, it can send an encouraging message. It can also send a message of praise when an employee achieves a goal. Furthermore, it can send a message of support when an employee is tackling a challenging task. This allows for improved employee morale and work efficiency by providing motivational support tailored to each employee's emotions.

[0098] The employee support system can estimate employees' emotions and provide stress management support based on those estimates. For example, if an employee is feeling stressed, it can suggest relaxation activities. If an employee is tired, it can offer advice to encourage rest. Furthermore, if an employee is relaxed, it can suggest activities to refresh themselves. By providing stress management support tailored to employees' emotions, this system aims to reduce employee stress and improve work efficiency.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The learning unit acquires knowledge specific to the operations of each department and headquarters. For example, it uses generative AI to analyze documents related to operations, extracting and learning important information. It can also improve the accuracy of its learning by referring to past operational data. Step 2: The reception desk handles employee questions and consultations. For example, it accepts employee questions and consultations via chat or voice input. Chat uses a text-based chat system, and voice input uses speech recognition technology. Step 3: The response unit provides answers to questions and inquiries received by the reception unit. For example, it can use a generative AI to provide appropriate answers to questions and inquiries. The generative AI can generate answers to questions using text generation AI and provide answers immediately using real-time processing.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the learning unit, reception unit, and response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns knowledge about the operations of each department and headquarters using generating AI. The reception unit is implemented by the control unit 46A of the smart device 14 and receives questions and consultations from employees via chat or voice input. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate answers to questions and consultations using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the learning unit, reception unit, and response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns knowledge about the operations of each department and headquarters using generating AI. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives questions and consultations from employees via chat or voice input. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate answers to questions and consultations using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the learning unit, reception unit, and response unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns knowledge about the operations of each department and headquarters using generating AI. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives questions and consultations from employees via chat or voice input. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate answers to questions and consultations using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the learning unit, reception unit, and response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns knowledge about the operations of each department and headquarters using generating AI. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and receives questions and consultations from employees via chat or voice input. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides appropriate answers to questions and consultations using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The learning department provides specialized knowledge for each department and headquarters' operations, A reception desk that handles employee questions and consultations, The system includes a response unit that provides answers to questions and consultations received by the reception unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, The generated AI learns specialized knowledge specific to the operations of each department and headquarters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We accept employee questions and consultations via chat and voice input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned response section is, The AI ​​generates appropriate answers to questions and inquiries received by the reception department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned response section is, We provide immediate answers to employee questions and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned response section is, We provide advice to improve employee productivity. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned response section is, We offer advice on stress reduction to compensate for the disadvantages of remote work. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, The system estimates employees' emotions and adjusts learning content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, Improve learning accuracy by referring to past business data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, The scope of knowledge to be learned is dynamically changed according to the progress of the work. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, The system estimates employees' emotions and determines learning priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, Customize the scope of knowledge to be learned according to the employee's position and experience. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, Adjust the scope of knowledge to be learned according to seasonal fluctuations in work. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is The system estimates the emotions of employees and adjusts the receptionist's response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is We classify the content of the questions and consultations we receive by referring to past data and provide appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is Prioritize the types of questions and consultations you receive based on the progress of your work. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is The system estimates the emotions of employees and adjusts the response speed of reception staff based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is The content of the questions and consultations received will be filtered according to the employee's position and experience. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reception unit is The types of questions and consultations we accept will be adjusted according to seasonal fluctuations in our workload. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned response section is, The system estimates employees' emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned response section is, We improve the accuracy of the responses we provide by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned response section is, The content of the responses provided will be dynamically changed according to the progress of the work. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned response section is, The system estimates employees' emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned response section is, Customize the content of the responses provided according to the employee's position and experience. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned response section is, The content of the responses provided will be adjusted according to seasonal fluctuations in business operations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The learning department provides specialized knowledge for each department and headquarters' operations, A reception desk that handles employee questions and consultations, The system includes a response unit that provides answers to questions and consultations received by the reception unit. A system characterized by the following features.

2. The aforementioned learning unit, The AI ​​generates knowledge specific to the operations of each department and headquarters. The system according to feature 1.

3. The aforementioned reception unit is We accept employee questions and consultations via chat and voice input. The system according to feature 1.

4. The aforementioned response section is, The AI ​​generates appropriate answers to questions and inquiries received by the reception unit. The system according to feature 1.

5. The aforementioned response section is, We provide immediate answers to employee questions and concerns. The system according to feature 1.

6. The aforementioned response section is, We provide advice to improve employee productivity. The system according to feature 1.

7. The aforementioned response section is, We offer advice on stress reduction to compensate for the disadvantages of remote work. The system according to feature 1.

8. The aforementioned learning unit, The system estimates employees' emotions and adjusts learning content based on those estimated emotions. The system according to feature 1.

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