system

Abstract

The system according to this embodiment aims to allow new employees, transferees, and employees performing unfamiliar tasks to freely ask questions about their work and learn. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a management unit. The reception unit receives questions from employees. The generation unit generates answers to the questions received by the reception unit. The provision unit provides the answers generated by the generation unit to the employees. The management unit manages the learning progress of employees.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for newly recruited employees, transferred employees, and employees with inexperienced work to freely ask questions about work and learn.

[0005] The system according to the embodiment aims to enable newly recruited employees, transferred employees, and employees with inexperienced work to freely ask questions about work and learn.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a management unit. The reception unit receives questions from employees. The generation unit generates answers to the questions received by the reception unit. The provision unit provides the answers generated by the generation unit to the employees. The management unit manages the learning progress of employees. [Effects of the Invention]

[0007] The system according to this embodiment allows new employees, transferees, and employees performing unfamiliar tasks to freely ask questions about their work and learn. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant system according to an embodiment of the present invention is a system that allows new employees, transferees, and employees in unfamiliar tasks to freely ask questions about their work as many times as needed and to learn. In this AI assistant system, employees input work-related questions into the AI, the AI ​​generates answers to those questions, and provides them to the employees. Furthermore, the AI ​​manages the employees' learning progress and provides guidance and supplementary information according to their progress. This mechanism allows employees to ask questions anytime, 24 hours a day, 365 days a year, and to learn without feeling any psychological burden. In addition, the AI ​​can be customized to suit the specific needs of each company, and by providing standardized information, variations in the quality of instruction can be eliminated. This enables companies to efficiently develop human resources. For example, an employee inputs a work-related question into the AI. The AI ​​generates answers to those questions and provides them to the employees. Furthermore, the AI ​​manages the employees' learning progress and provides guidance and supplementary information according to their progress. This allows employees to ask questions anytime, 24 hours a day, 365 days a year, and to learn without feeling any psychological burden. Furthermore, the AI ​​can be customized to suit the specific needs of each company, and by providing standardized information, variations in the quality of instruction can be eliminated. This allows companies to efficiently develop their talent. The talent development and training market is growing rapidly, with corporate investment in talent development increasing year after year. The global talent management market has reached billions of dollars, and the AI-powered training and education market is expanding similarly. In particular, with the rise of remote and hybrid work, the need for AI assistant systems that provide learning support regardless of location is increasing. AI assistant systems are learning support AI that can be used 24 / 7, 365 days a year, anywhere, without relying on human instructors. Because they can be customized to the specific needs of each company, they provide instruction tailored to the individual needs of employees, enabling efficient and flexible talent development. Entering this market now offers a significant competitive advantage as an innovative solution addressing the challenges of modern work styles and talent development. This allows AI assistant systems to provide quick and accurate answers to employee questions and manage learning progress.

[0029] The AI ​​assistant system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a management unit. The reception unit receives questions from employees. Employee questions include, but are not limited to, questions in text format, voice format, or on specific topics. For example, employees can input questions in text format into the reception unit. The reception unit can also receive questions in voice format. Furthermore, the reception unit can also receive questions on specific topics. The generation unit generates answers to questions received by the reception unit. The generation unit generates answers to questions using, for example, AI. The generation unit can generate answers to questions using, for example, natural language processing techniques. The generation unit can also generate answers to questions using machine learning techniques. Furthermore, the generation unit can also generate answers to questions using rule-based algorithms. The provision unit provides the answers generated by the generation unit to the employee. The provision unit can provide answers in, for example, text format. The provision unit can also provide answers in voice format. Furthermore, the provision unit can also provide answers that include visual information. The management unit manages the employee's learning progress. The management department can, for example, evaluate the learning progress of employees. Furthermore, the management department can provide guidance and supplementary information tailored to the employee's learning progress. In addition, the management department can record and analyze the employee's learning progress. This allows the AI ​​assistant system, according to the embodiment, to provide quick and accurate answers to employee questions and manage their learning progress.

[0030] The reception desk receives questions from employees. These questions may include, but are not limited to, text-based, voice-based, or topical questions. For example, employees can input questions in text format. The reception desk can also accept questions in voice format. Furthermore, the reception desk can accept questions on specific topics. Specifically, the reception desk uses natural language processing technology to analyze the text and voice input from employees and understand the intent of the questions. For example, in the case of voice input, speech recognition technology is used to convert the voice to text, and then natural language processing technology is used to analyze the question content. This ensures that the reception desk accurately understands and appropriately processes questions regardless of the format in which they are submitted. The reception desk also has a function to categorize questions appropriately based on their content. For example, categorizing questions into various categories such as technical questions, questions about work procedures, and questions about employee benefits allows for efficient subsequent processing. Furthermore, the reception desk can determine the priority of questions and be configured to respond quickly to urgent questions. This not only ensures that employee questions are handled quickly and accurately, but also improves the overall efficiency of the system.

[0031] The generation unit generates answers to questions received by the reception unit. The generation unit can generate answers using, for example, AI. It can also generate answers using, for example, natural language processing technology. Furthermore, it can generate answers using machine learning technology. In addition, it can generate answers using rule-based algorithms. Specifically, the generation unit combines multiple AI technologies to generate the optimal answer depending on the content of the question. For example, it analyzes the intent of the question using natural language processing technology, and then uses a machine learning model to search for the optimal answer from past data and knowledge bases. It can also generate answers based on specific conditions or patterns using rule-based algorithms. This allows the generation unit to provide quick and accurate answers to employee questions. Furthermore, the generation unit has the ability to evaluate the quality of the generated answers and make corrections or additions as needed. For example, if the generated answer is incomplete, it searches for additional information to complete the answer. The generation unit can also continuously improve the accuracy and quality of answers based on employee feedback. This allows the generation unit to consistently provide high-quality answers and improve employee satisfaction.

[0032] The delivery unit provides employees with answers generated by the generation unit. The delivery unit can provide answers in, for example, text format. It can also provide answers in audio format. Furthermore, it can provide answers that include visual information. Specifically, the delivery unit has multiple output methods to provide answers to employee questions in the appropriate format. For example, text-format answers are provided via chat windows or email. Audio answers are generated using speech synthesis technology and delivered as voice messages to the employee's device. Answers that include visual information are provided in multimedia formats, including graphs, charts, and images. This allows the delivery unit to provide answers in the most optimal format according to the employee's needs and circumstances. Furthermore, the delivery unit can collect feedback from employees after providing answers and use it to improve the quality and delivery method of the answers. For example, it has a function to allow employees to rate their satisfaction with the provided answers, and adjusts the algorithms of the generation and delivery units based on these ratings. The delivery unit also records the history of answers provided, making it easy for employees to search past questions and answers for later reference. This allows the service department to provide employees with quick and appropriate answers, improving the overall user experience of the system.

[0033] The management department manages employee learning progress. For example, the management department can evaluate employee learning progress. It can also provide guidance and supplementary information tailored to employee learning progress. Furthermore, the management department can record and analyze employee learning progress. Specifically, the management department evaluates learning progress based on the history of questions asked and answers received by employees through the system. For example, it analyzes what kinds of questions employees ask most often and in which areas they lack knowledge, and creates individual learning plans based on the results. The management department also provides appropriate guidance and supplementary information according to employee learning progress. For example, it can suggest detailed materials on specific topics or relevant training programs. Furthermore, the management department continuously records employee learning progress and evaluates long-term learning effectiveness. This helps support employee skill improvement and knowledge retention. Through these functions, the management department can improve employee learning efficiency and raise the overall knowledge level of the organization. Additionally, based on employee learning data, the management department can identify areas for improvement in the organization's overall training programs and develop more effective training plans. This allows the management department to comprehensively manage employees' learning progress and support the growth of the entire organization.

[0034] The information delivery unit can provide customized content tailored to each company. For example, it can provide customized information based on the company's industry. For example, it can provide information on manufacturing processes to a manufacturing company. It can also provide customized information based on an employee's job title. For example, it can provide information on management duties to a manager. Furthermore, it can provide customized information based on specific job duties. For example, it can provide information on sales strategies to a sales representative. This allows for the provision of customized information tailored to each company. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input data on the company's industry and employee job titles into a generating AI and have the generating AI generate customized information.

[0035] The service provider can provide standardized information. For example, it can provide information based on industry standards. For example, it can provide information based on IT industry standards. The service provider can also provide information based on internal company standards. For example, it can provide information based on internal company procedures. Furthermore, the service provider can provide information based on specific guidelines. For example, it can provide information based on ISO standards. By providing standardized information, variations in the quality of instruction can be eliminated. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on industry standards and internal company standards into a generating AI and have the generating AI perform the generation of standardized information.

[0036] The management department can provide guidance and supplementary information tailored to employees' learning progress. For example, the management department can evaluate employees' learning progress and provide guidance accordingly. For instance, if an employee completes a specific learning item, the management department can provide guidance on the next item. The management department can also provide supplementary information tailored to employees' learning progress. For example, if an employee is having difficulty understanding something, additional learning materials can be provided. Furthermore, the management department can record and analyze employees' learning progress. For example, the effectiveness of learning can be evaluated based on employee learning progress data. This enables efficient learning by providing guidance and supplementary information tailored to employees' learning progress. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input employee learning progress data into a generating AI and have the generating AI provide guidance and supplementary information tailored to the progress.

[0037] The reception desk can analyze an employee's past question history and select the optimal reception method. For example, the reception desk can automatically suggest relevant questions based on the content of questions the employee has frequently asked in the past. The reception desk can also prioritize suggesting question formats (text, voice, etc.) that the employee has used in the past. Furthermore, the reception desk can suggest the optimal reception method for a specific time of day based on the employee's past question history. In this way, the optimal reception method can be selected by analyzing the employee's past question history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the employee's past question history data into a generating AI and have the generating AI select the optimal reception method.

[0038] The reception desk can filter questions based on the employee's current work situation and areas of interest. For example, the reception desk can prioritize questions related to projects the employee is currently working on. It can also filter and accept relevant questions based on the employee's areas of interest. Furthermore, the reception desk can prioritize questions of high urgency depending on the employee's work situation. This allows for the priority of receiving highly relevant questions by filtering them based on the employee's current work situation and areas of interest. 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 work situation data and areas of interest data into a generating AI and have the generating AI perform the question filtering.

[0039] The reception desk can prioritize questions based on their relevance, taking into account the employee's geographical location. For example, if an employee is in a specific office, the reception desk can prioritize questions related to that office. Similarly, if an employee is on a business trip, the reception desk can prioritize questions related to their destination. Furthermore, if an employee is working remotely, the reception desk can prioritize questions related to their home. This allows for the prioritization of highly relevant questions by considering the employee's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk could input employee geographical location data into a generating AI and have the AI ​​determine the priority of most relevant questions.

[0040] The reception desk can analyze an employee's social media activity when receiving a question and accept relevant questions. For example, the reception desk can accept relevant questions based on information shared by the employee on social media. It can also accept questions related to the employee's areas of interest based on their social media activity. Furthermore, the reception desk can analyze the content of an employee's social media posts and accept relevant questions. This allows the reception desk to prioritize the acceptance of relevant questions by analyzing the employee's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input employee social media activity data into a generating AI and have the generating AI perform the task of accepting relevant questions.

[0041] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate detailed answers for high-importance questions. It can also generate concise answers for low-importance questions. Furthermore, the generation unit can generate answers with an appropriate level of detail depending on the importance of the question. In this way, by adjusting the level of detail in the answer based on the importance of the question, it is possible to provide answers with an appropriate level of detail. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the answer.

[0042] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply a specialized algorithm to technical questions to generate answers. It can also apply a simpler algorithm to general questions to generate answers. Furthermore, the generation unit can select the optimal generation algorithm depending on the question category to generate answers. This allows for the provision of optimal answers by applying different generation algorithms depending on the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select the optimal generation algorithm.

[0043] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit can determine the priority of answers based on the time period in which the questions were submitted. The generation unit can also generate answers at an appropriate time depending on when the questions were submitted. Furthermore, the generation unit can determine the optimal order of answers based on when the questions were submitted. This allows for timely provision of answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the question submission time data into a generation AI and have the generation AI perform the determination of answer priority.

[0044] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating the most relevant answers based on the relevance of the questions. The generation unit can also generate answers in an appropriate order according to the relevance of the questions. Furthermore, the generation unit can determine the optimal order of answers based on the relevance of the questions. This allows for the priority provision of highly relevant answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into a generation AI and have the generation AI perform the adjustment of the answer order.

[0045] The information delivery unit can adjust the level of detail of the information based on the company's specific customization requirements at the time of delivery. For example, the information delivery unit can provide detailed information based on the company's specific needs. It can also provide information with an appropriate level of detail depending on the company's customization requirements. Furthermore, the information delivery unit can provide optimal information based on the company's specific customization requirements. In this way, appropriate information can be provided by adjusting the level of detail of the information based on the company's specific customization requirements. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the company's customization data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0046] The information provider can apply different information provision algorithms when providing standardized information. For example, the information provider can provide information by applying the optimal information provision algorithm to standardized information. The information provider can also select an appropriate information provision method based on the standardized information. Furthermore, the information provider can apply different information provision algorithms when providing standardized information. This allows for the provision of optimal information by applying different information provision algorithms when providing standardized information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input standardized information data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0047] The information delivery unit can prioritize providing highly relevant information by considering the employee's geographical location. For example, if an employee is in a specific office, the information delivery unit can prioritize providing information related to that office. Furthermore, if an employee is on a business trip, the information delivery unit can prioritize providing information related to their destination. Additionally, if an employee is working remotely, the information delivery unit can prioritize providing information related to their home. This allows for the prioritization of highly relevant information by considering the employee's geographical location. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input employee geographical location data into a generating AI and have the generating AI determine the priority of highly relevant information.

[0048] The information delivery unit can analyze employees' social media activity and provide relevant information at the time of delivery. For example, the information delivery unit can provide relevant information based on information shared by employees on social media. It can also provide information related to areas of interest from employees' social media activity. Furthermore, the information delivery unit can analyze the content of employees' social media posts and provide relevant information. This allows for the priority provision of relevant information by analyzing employees' social media activity. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input employee social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0049] The management department can select the optimal management method when managing learning progress by referring to the employee's past learning history. For example, the management department can select the optimal progress management method based on the employee's past learning history. The management department can also perform appropriate progress management by referring to what the employee has learned in the past. Furthermore, the management department can analyze the employee's past learning history and propose the optimal management method. This allows the management department to select the optimal progress management method by referring to the employee's past learning history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the employee's past learning history data into a generating AI and have the generating AI select the optimal management method.

[0050] The management department can customize management methods based on the employee's current work status when managing learning progress. For example, the management department can provide appropriate progress management methods considering the employee's current work status. The management department can also perform customized progress management according to the employee's work status. Furthermore, the management department can propose the optimal management method based on the employee's current work status. This makes it possible to perform more appropriate progress management by customizing the management method based on the employee's current work status. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input employee work status data into a generating AI and have the generating AI perform the customization of the management method.

[0051] The management department can select the optimal management method when managing learning progress, taking into account the geographical location of employees. For example, if an employee is in a specific office, the management department can prioritize managing learning items related to that office. Similarly, if an employee is on a business trip, the management department can prioritize managing learning items related to the business trip destination. Furthermore, if an employee is working remotely, the management department can prioritize managing learning items related to their home. This allows the management department to select the optimal management method by considering the employee's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For instance, the management department could input employee geographical location data into a generating AI and have the generating AI select the optimal management method.

[0052] The management department can analyze employees' social media activity and propose management strategies when managing learning progress. For example, the management department can manage relevant learning items based on information shared by employees on social media. It can also manage learning items related to areas of interest based on employees' social media activity. Furthermore, the management department can analyze the content of employees' social media posts and manage relevant learning items. This allows for the priority management of relevant learning items by analyzing employees' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input employee social media activity data into a generating AI and have the generating AI propose management strategies.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The AI ​​assistant system may further include a feedback unit. The feedback unit allows employees to input feedback on the provided answers. For example, it can evaluate whether the employee is satisfied with the answer. The feedback unit can also allow employees to input additional questions about the answer. Furthermore, the feedback unit can improve the quality of the answers by analyzing the employee's feedback and providing feedback to the generation unit. This allows for continuous improvement of the AI ​​assistant system's answer quality by utilizing employee feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input employee feedback data into a generation AI and have the generation AI perform the analysis of the feedback.

[0055] The AI ​​assistant system may also include a notification unit. This notification unit can notify employees of important information and updates. For example, it can notify employees of their progress when they complete a new learning item. It can also send reminders when employees are approaching a specific deadline. Furthermore, it can send congratulatory messages when employees achieve a specific goal. This helps maintain learning motivation by providing timely notifications of employee learning progress and important information. Some or all of the above processes in the notification unit may be performed using AI, or not. For example, the notification unit could input employee progress data into a generating AI and have the generating AI generate the notification content.

[0056] The AI ​​assistant system may also include a recommendation section. This recommendation section can recommend relevant learning content and resources based on an employee's learning history and areas of interest. For example, if an employee has shown interest in a particular topic, it can recommend additional learning materials related to that topic. The recommendation section can also suggest items to learn next based on the employee's learning progress. Furthermore, it can analyze an employee's past learning history and suggest the optimal learning path. This personalizes the employee's learning experience and supports efficient learning. Some or all of the processes described above in the recommendation section may be performed using AI, or not. For example, the recommendation section could input employee learning history data into a generating AI and have the generating AI generate recommendations.

[0057] The AI ​​assistant system may also include an evaluation unit. The evaluation unit can assess employees' learning outcomes and provide feedback. For example, it can provide a test to assess an employee's understanding after they have completed a specific learning item. The evaluation unit can also identify areas where additional learning is needed based on the employee's test results and provide supplementary information. Furthermore, the evaluation unit can periodically evaluate employees' learning outcomes and generate progress reports. This allows for an objective assessment of employees' learning outcomes and the provision of necessary feedback, thereby improving the quality of learning. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input employee test result data into a generating AI and have the generating AI generate the evaluation content.

[0058] The AI ​​assistant system may also include a guide section. This guide section can provide guidelines and tutorials to help employees effectively use the system. For example, it can provide a tutorial explaining basic operation methods when a new employee uses the system for the first time. It can also provide step-by-step guidance when employees use specific functions. Furthermore, it can provide FAQs and help documents if employees have questions about using the system. This supports employees in effectively using the system and improves learning efficiency. Some or all of the above processes in the guide section may be performed using AI, or not. For example, the guide section could input employee usage data into a generating AI and have the generating AI generate the guide content.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives employee questions. Employee questions can be in text format, voice format, or on specific topics. For example, employees can type questions in text format, and questions can also be submitted in voice format. Furthermore, questions on specific topics can also be accepted. Step 2: The generation unit generates answers to the questions received by the reception unit. The generation unit uses AI to generate answers to the questions and can utilize natural language processing technology, machine learning technology, and rule-based algorithms. Step 3: The providing unit provides the employee with the response generated by the generating unit. The providing unit can provide responses in text format, audio format, or include visual information. Step 4: The management department manages employee learning progress. The management department can evaluate employee learning progress, provide guidance and supplementary information according to the learning progress, and record and analyze learning progress.

[0061] (Example of form 2) The AI ​​assistant system according to an embodiment of the present invention is a system that allows new employees, transferees, and employees in unfamiliar tasks to freely ask questions about their work as many times as needed and to learn. In this AI assistant system, employees input work-related questions into the AI, the AI ​​generates answers to those questions, and provides them to the employees. Furthermore, the AI ​​manages the employees' learning progress and provides guidance and supplementary information according to their progress. This mechanism allows employees to ask questions anytime, 24 hours a day, 365 days a year, and to learn without feeling any psychological burden. In addition, the AI ​​can be customized to suit the specific needs of each company, and by providing standardized information, variations in the quality of instruction can be eliminated. This enables companies to efficiently develop human resources. For example, an employee inputs a work-related question into the AI. The AI ​​generates answers to those questions and provides them to the employees. Furthermore, the AI ​​manages the employees' learning progress and provides guidance and supplementary information according to their progress. This allows employees to ask questions anytime, 24 hours a day, 365 days a year, and to learn without feeling any psychological burden. Furthermore, the AI ​​can be customized to suit the specific needs of each company, and by providing standardized information, variations in the quality of instruction can be eliminated. This allows companies to efficiently develop their talent. The talent development and training market is growing rapidly, with corporate investment in talent development increasing year after year. The global talent management market has reached billions of dollars, and the AI-powered training and education market is expanding similarly. In particular, with the rise of remote and hybrid work, the need for AI assistant systems that provide learning support regardless of location is increasing. AI assistant systems are learning support AI that can be used 24 / 7, 365 days a year, anywhere, without relying on human instructors. Because they can be customized to the specific needs of each company, they provide instruction tailored to the individual needs of employees, enabling efficient and flexible talent development. Entering this market now offers a significant competitive advantage as an innovative solution addressing the challenges of modern work styles and talent development. This allows AI assistant systems to provide quick and accurate answers to employee questions and manage learning progress.

[0062] The AI ​​assistant system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a management unit. The reception unit receives questions from employees. Employee questions include, but are not limited to, questions in text format, voice format, or on specific topics. For example, employees can input questions in text format into the reception unit. The reception unit can also receive questions in voice format. Furthermore, the reception unit can also receive questions on specific topics. The generation unit generates answers to questions received by the reception unit. The generation unit generates answers to questions using, for example, AI. The generation unit can generate answers to questions using, for example, natural language processing techniques. The generation unit can also generate answers to questions using machine learning techniques. Furthermore, the generation unit can also generate answers to questions using rule-based algorithms. The provision unit provides the answers generated by the generation unit to the employee. The provision unit can provide answers in, for example, text format. The provision unit can also provide answers in voice format. Furthermore, the provision unit can also provide answers that include visual information. The management unit manages the employee's learning progress. The management department can, for example, evaluate the learning progress of employees. Furthermore, the management department can provide guidance and supplementary information tailored to the employee's learning progress. In addition, the management department can record and analyze the employee's learning progress. This allows the AI ​​assistant system, according to the embodiment, to provide quick and accurate answers to employee questions and manage their learning progress.

[0063] The reception desk receives questions from employees. These questions may include, but are not limited to, text-based, voice-based, or topical questions. For example, employees can input questions in text format. The reception desk can also accept questions in voice format. Furthermore, the reception desk can accept questions on specific topics. Specifically, the reception desk uses natural language processing technology to analyze the text and voice input from employees and understand the intent of the questions. For example, in the case of voice input, speech recognition technology is used to convert the voice to text, and then natural language processing technology is used to analyze the question content. This ensures that the reception desk accurately understands and appropriately processes questions regardless of the format in which they are submitted. The reception desk also has a function to categorize questions appropriately based on their content. For example, categorizing questions into various categories such as technical questions, questions about work procedures, and questions about employee benefits allows for efficient subsequent processing. Furthermore, the reception desk can determine the priority of questions and be configured to respond quickly to urgent questions. This not only ensures that employee questions are handled quickly and accurately, but also improves the overall efficiency of the system.

[0064] The generation unit generates answers to questions received by the reception unit. The generation unit can generate answers using, for example, AI. It can also generate answers using, for example, natural language processing technology. Furthermore, it can generate answers using machine learning technology. In addition, it can generate answers using rule-based algorithms. Specifically, the generation unit combines multiple AI technologies to generate the optimal answer depending on the content of the question. For example, it analyzes the intent of the question using natural language processing technology, and then uses a machine learning model to search for the optimal answer from past data and knowledge bases. It can also generate answers based on specific conditions or patterns using rule-based algorithms. This allows the generation unit to provide quick and accurate answers to employee questions. Furthermore, the generation unit has the ability to evaluate the quality of the generated answers and make corrections or additions as needed. For example, if the generated answer is incomplete, it searches for additional information to complete the answer. The generation unit can also continuously improve the accuracy and quality of answers based on employee feedback. This allows the generation unit to consistently provide high-quality answers and improve employee satisfaction.

[0065] The delivery unit provides employees with answers generated by the generation unit. The delivery unit can provide answers in, for example, text format. It can also provide answers in audio format. Furthermore, it can provide answers that include visual information. Specifically, the delivery unit has multiple output methods to provide answers to employee questions in the appropriate format. For example, text-format answers are provided via chat windows or email. Audio answers are generated using speech synthesis technology and delivered as voice messages to the employee's device. Answers that include visual information are provided in multimedia formats, including graphs, charts, and images. This allows the delivery unit to provide answers in the most optimal format according to the employee's needs and circumstances. Furthermore, the delivery unit can collect feedback from employees after providing answers and use it to improve the quality and delivery method of the answers. For example, it has a function to allow employees to rate their satisfaction with the provided answers, and adjusts the algorithms of the generation and delivery units based on these ratings. The delivery unit also records the history of answers provided, making it easy for employees to search past questions and answers for later reference. This allows the service department to provide employees with quick and appropriate answers, improving the overall user experience of the system.

[0066] The management department manages employee learning progress. For example, the management department can evaluate employee learning progress. It can also provide guidance and supplementary information tailored to employee learning progress. Furthermore, the management department can record and analyze employee learning progress. Specifically, the management department evaluates learning progress based on the history of questions asked and answers received by employees through the system. For example, it analyzes what kinds of questions employees ask most often and in which areas they lack knowledge, and creates individual learning plans based on the results. The management department also provides appropriate guidance and supplementary information according to employee learning progress. For example, it can suggest detailed materials on specific topics or relevant training programs. Furthermore, the management department continuously records employee learning progress and evaluates long-term learning effectiveness. This helps support employee skill improvement and knowledge retention. Through these functions, the management department can improve employee learning efficiency and raise the overall knowledge level of the organization. Additionally, based on employee learning data, the management department can identify areas for improvement in the organization's overall training programs and develop more effective training plans. This allows the management department to comprehensively manage employees' learning progress and support the growth of the entire organization.

[0067] The information delivery unit can provide customized content tailored to each company. For example, it can provide customized information based on the company's industry. For example, it can provide information on manufacturing processes to a manufacturing company. It can also provide customized information based on an employee's job title. For example, it can provide information on management duties to a manager. Furthermore, it can provide customized information based on specific job duties. For example, it can provide information on sales strategies to a sales representative. This allows for the provision of customized information tailored to each company. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input data on the company's industry and employee job titles into a generating AI and have the generating AI generate customized information.

[0068] The service provider can provide standardized information. For example, it can provide information based on industry standards. For example, it can provide information based on IT industry standards. The service provider can also provide information based on internal company standards. For example, it can provide information based on internal company procedures. Furthermore, the service provider can provide information based on specific guidelines. For example, it can provide information based on ISO standards. By providing standardized information, variations in the quality of instruction can be eliminated. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on industry standards and internal company standards into a generating AI and have the generating AI perform the generation of standardized information.

[0069] The management department can provide guidance and supplementary information tailored to employees' learning progress. For example, the management department can evaluate employees' learning progress and provide guidance accordingly. For instance, if an employee completes a specific learning item, the management department can provide guidance on the next item. The management department can also provide supplementary information tailored to employees' learning progress. For example, if an employee is having difficulty understanding something, additional learning materials can be provided. Furthermore, the management department can record and analyze employees' learning progress. For example, the effectiveness of learning can be evaluated based on employee learning progress data. This enables efficient learning by providing guidance and supplementary information tailored to employees' learning progress. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input employee learning progress data into a generating AI and have the generating AI provide guidance and supplementary information tailored to the progress.

[0070] The reception desk can estimate the emotions of employees and adjust the timing of question acceptance based on the estimated emotions. For example, if an employee is feeling stressed, the reception desk can temporarily delay question acceptance to give them time to relax. Conversely, if an employee is focused, the reception desk can immediately accept questions and respond quickly. Furthermore, if an employee is tired, the reception desk can temporarily stop accepting questions to encourage them to take a break. This allows for question acceptance at a more appropriate time by adjusting the timing according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze an employee's past question history and select the optimal reception method. For example, the reception desk can automatically suggest relevant questions based on the content of questions the employee has frequently asked in the past. The reception desk can also prioritize suggesting question formats (text, voice, etc.) that the employee has used in the past. Furthermore, the reception desk can suggest the optimal reception method for a specific time of day based on the employee's past question history. In this way, the optimal reception method can be selected by analyzing the employee's past question history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the employee's past question history data into a generating AI and have the generating AI select the optimal reception method.

[0072] The reception desk can filter questions based on the employee's current work situation and areas of interest. For example, the reception desk can prioritize questions related to projects the employee is currently working on. It can also filter and accept relevant questions based on the employee's areas of interest. Furthermore, the reception desk can prioritize questions of high urgency depending on the employee's work situation. This allows for the priority of receiving highly relevant questions by filtering them based on the employee's current work situation and areas of interest. 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 work situation data and areas of interest data into a generating AI and have the generating AI perform the question filtering.

[0073] The reception desk can estimate an employee's emotions and prioritize questions based on those emotions. For example, if an employee is feeling anxious, the reception desk can prioritize urgent questions. If an employee is relaxed, the reception desk can prioritize normal questions. Furthermore, if an employee is agitated, the reception desk can prioritize important questions. This allows for prioritizing more appropriate questions based on 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 reception desk may be performed using AI or not. For example, the reception desk can input employee emotion data into a generative AI and have the generative AI determine the priority of questions.

[0074] The reception desk can prioritize questions based on their relevance, taking into account the employee's geographical location. For example, if an employee is in a specific office, the reception desk can prioritize questions related to that office. Similarly, if an employee is on a business trip, the reception desk can prioritize questions related to their destination. Furthermore, if an employee is working remotely, the reception desk can prioritize questions related to their home. This allows for the prioritization of highly relevant questions by considering the employee's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk could input employee geographical location data into a generating AI and have the AI ​​determine the priority of most relevant questions.

[0075] The reception desk can analyze an employee's social media activity when receiving a question and accept relevant questions. For example, the reception desk can accept relevant questions based on information shared by the employee on social media. It can also accept questions related to the employee's areas of interest based on their social media activity. Furthermore, the reception desk can analyze the content of an employee's social media posts and accept relevant questions. This allows the reception desk to prioritize the acceptance of relevant questions by analyzing the employee's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input employee social media activity data into a generating AI and have the generating AI perform the task of accepting relevant questions.

[0076] The generation unit can estimate an employee's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if an employee is stressed, the generation unit can generate a concise and easy-to-understand response. If an employee is relaxed, the generation unit can also generate a response that includes detailed explanations. Furthermore, if an employee is excited, the generation unit can generate a visually appealing response. This allows for the provision of more appropriate responses by adjusting the way the response is expressed according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into the generation AI and have the generation AI adjust the way the response is expressed.

[0077] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate detailed answers for high-importance questions. It can also generate concise answers for low-importance questions. Furthermore, the generation unit can generate answers with an appropriate level of detail depending on the importance of the question. In this way, by adjusting the level of detail in the answer based on the importance of the question, it is possible to provide answers with an appropriate level of detail. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the answer.

[0078] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply a specialized algorithm to technical questions to generate answers. It can also apply a simpler algorithm to general questions to generate answers. Furthermore, the generation unit can select the optimal generation algorithm depending on the question category to generate answers. This allows for the provision of optimal answers by applying different generation algorithms depending on the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select the optimal generation algorithm.

[0079] The generation unit can estimate an employee's emotions and adjust the length of the response based on the estimated emotions. For example, if an employee is in a hurry, the generation unit can generate a short, concise response. If an employee is relaxed, the generation unit can generate a longer response that includes detailed explanations. Furthermore, if an employee is excited, the generation unit can generate a response with visually stimulating effects. This allows for the provision of more appropriate responses by adjusting the length of the response according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into a generation AI and have the generation AI adjust the length of the response.

[0080] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit can determine the priority of answers based on the time period in which the questions were submitted. The generation unit can also generate answers at an appropriate time depending on when the questions were submitted. Furthermore, the generation unit can determine the optimal order of answers based on when the questions were submitted. This allows for timely provision of answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the question submission time data into a generation AI and have the generation AI perform the determination of answer priority.

[0081] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating the most relevant answers based on the relevance of the questions. The generation unit can also generate answers in an appropriate order according to the relevance of the questions. Furthermore, the generation unit can determine the optimal order of answers based on the relevance of the questions. This allows for the priority provision of highly relevant answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into a generation AI and have the generation AI perform the adjustment of the answer order.

[0082] The information delivery unit can estimate an employee's emotions and adjust the way the information is presented based on the estimated emotions. For example, if an employee is stressed, the unit can provide concise and easy-to-understand information. If an employee is relaxed, the unit can provide information that includes detailed explanations. Furthermore, if an employee is excited, the unit can provide visually appealing information. This allows for the provision of more appropriate information by adjusting the way the information is presented according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input employee emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0083] The information delivery unit can adjust the level of detail of the information based on the company's specific customization requirements at the time of delivery. For example, the information delivery unit can provide detailed information based on the company's specific needs. It can also provide information with an appropriate level of detail depending on the company's customization requirements. Furthermore, the information delivery unit can provide optimal information based on the company's specific customization requirements. In this way, appropriate information can be provided by adjusting the level of detail of the information based on the company's specific customization requirements. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the company's customization data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0084] The information provider can apply different information provision algorithms when providing standardized information. For example, the information provider can provide information by applying the optimal information provision algorithm to standardized information. The information provider can also select an appropriate information provision method based on the standardized information. Furthermore, the information provider can apply different information provision algorithms when providing standardized information. This allows for the provision of optimal information by applying different information provision algorithms when providing standardized information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input standardized information data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0085] The information delivery unit can estimate an employee's emotions and prioritize the information to be delivered based on the estimated emotions. For example, if an employee is feeling anxious, the unit can prioritize providing urgent information. If an employee is relaxed, the unit can prioritize providing normal information. Furthermore, if an employee is agitated, the unit can prioritize providing important information. This allows for the prioritization of more appropriate information based on 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 information delivery unit may be performed using AI or not. For example, the information delivery unit can input employee emotion data into a generative AI and have the generative AI determine the priority of information.

[0086] The information delivery unit can prioritize providing highly relevant information by considering the employee's geographical location. For example, if an employee is in a specific office, the information delivery unit can prioritize providing information related to that office. Furthermore, if an employee is on a business trip, the information delivery unit can prioritize providing information related to their destination. Additionally, if an employee is working remotely, the information delivery unit can prioritize providing information related to their home. This allows for the prioritization of highly relevant information by considering the employee's geographical location. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input employee geographical location data into a generating AI and have the generating AI determine the priority of highly relevant information.

[0087] The information delivery unit can analyze employees' social media activity and provide relevant information at the time of delivery. For example, the information delivery unit can provide relevant information based on information shared by employees on social media. It can also provide information related to areas of interest from employees' social media activity. Furthermore, the information delivery unit can analyze the content of employees' social media posts and provide relevant information. This allows for the priority provision of relevant information by analyzing employees' social media activity. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input employee social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0088] The management department can estimate employees' emotions and adjust learning progress management methods based on those estimated emotions. For example, if an employee is stressed, the management department can ease up on learning progress management. Conversely, if an employee is relaxed, the management department can implement detailed progress management. Furthermore, if an employee is excited, the management department can implement aggressive progress management. This allows for more appropriate progress management by adjusting learning progress management methods according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input employee emotion data into a generative AI and have the generative AI adjust learning progress management methods.

[0089] The management department can select the optimal management method when managing learning progress by referring to the employee's past learning history. For example, the management department can select the optimal progress management method based on the employee's past learning history. The management department can also perform appropriate progress management by referring to what the employee has learned in the past. Furthermore, the management department can analyze the employee's past learning history and propose the optimal management method. This allows the management department to select the optimal progress management method by referring to the employee's past learning history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the employee's past learning history data into a generating AI and have the generating AI select the optimal management method.

[0090] The management department can customize management methods based on the employee's current work status when managing learning progress. For example, the management department can provide appropriate progress management methods considering the employee's current work status. The management department can also perform customized progress management according to the employee's work status. Furthermore, the management department can propose the optimal management method based on the employee's current work status. This makes it possible to perform more appropriate progress management by customizing the management method based on the employee's current work status. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input employee work status data into a generating AI and have the generating AI perform the customization of the management method.

[0091] The management department can estimate employees' emotions and prioritize learning progress based on those estimated emotions. For example, if an employee is feeling anxious, the management department can prioritize important learning items. Similarly, if an employee is relaxed, the management department can prioritize regular learning items. Furthermore, if an employee is excited, the management department can prioritize positive learning items. This allows for prioritizing more appropriate learning items by determining learning progress 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input employee emotion data into a generative AI and have the generative AI determine the priority of learning progress.

[0092] The management department can select the optimal management method when managing learning progress, taking into account the geographical location of employees. For example, if an employee is in a specific office, the management department can prioritize managing learning items related to that office. Similarly, if an employee is on a business trip, the management department can prioritize managing learning items related to the business trip destination. Furthermore, if an employee is working remotely, the management department can prioritize managing learning items related to their home. This allows the management department to select the optimal management method by considering the employee's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For instance, the management department could input employee geographical location data into a generating AI and have the generating AI select the optimal management method.

[0093] The management department can analyze employees' social media activity and propose management strategies when managing learning progress. For example, the management department can manage relevant learning items based on information shared by employees on social media. It can also manage learning items related to areas of interest based on employees' social media activity. Furthermore, the management department can analyze the content of employees' social media posts and manage relevant learning items. This allows for the priority management of relevant learning items by analyzing employees' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input employee social media activity data into a generating AI and have the generating AI propose management strategies.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The AI ​​assistant system may further include a feedback unit. The feedback unit allows employees to input feedback on the provided answers. For example, it can evaluate whether the employee is satisfied with the answer. The feedback unit can also allow employees to input additional questions about the answer. Furthermore, the feedback unit can improve the quality of the answers by analyzing the employee's feedback and providing feedback to the generation unit. This allows for continuous improvement of the AI ​​assistant system's answer quality by utilizing employee feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input employee feedback data into a generation AI and have the generation AI perform the analysis of the feedback.

[0096] The AI ​​assistant system may also include a notification unit. This notification unit can notify employees of important information and updates. For example, it can notify employees of their progress when they complete a new learning item. It can also send reminders when employees are approaching a specific deadline. Furthermore, it can send congratulatory messages when employees achieve a specific goal. This helps maintain learning motivation by providing timely notifications of employee learning progress and important information. Some or all of the above processes in the notification unit may be performed using AI, or not. For example, the notification unit could input employee progress data into a generating AI and have the generating AI generate the notification content.

[0097] The AI ​​assistant system may also include a recommendation section. This recommendation section can recommend relevant learning content and resources based on an employee's learning history and areas of interest. For example, if an employee has shown interest in a particular topic, it can recommend additional learning materials related to that topic. The recommendation section can also suggest items to learn next based on the employee's learning progress. Furthermore, it can analyze an employee's past learning history and suggest the optimal learning path. This personalizes the employee's learning experience and supports efficient learning. Some or all of the processes described above in the recommendation section may be performed using AI, or not. For example, the recommendation section could input employee learning history data into a generating AI and have the generating AI generate recommendations.

[0098] The AI ​​assistant system may also include an evaluation unit. The evaluation unit can assess employees' learning outcomes and provide feedback. For example, it can provide a test to assess an employee's understanding after they have completed a specific learning item. The evaluation unit can also identify areas where additional learning is needed based on the employee's test results and provide supplementary information. Furthermore, the evaluation unit can periodically evaluate employees' learning outcomes and generate progress reports. This allows for an objective assessment of employees' learning outcomes and the provision of necessary feedback, thereby improving the quality of learning. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input employee test result data into a generating AI and have the generating AI generate the evaluation content.

[0099] The AI ​​assistant system may also include a guide section. This guide section can provide guidelines and tutorials to help employees effectively use the system. For example, it can provide a tutorial explaining basic operation methods when a new employee uses the system for the first time. It can also provide step-by-step guidance when employees use specific functions. Furthermore, it can provide FAQs and help documents if employees have questions about using the system. This supports employees in effectively using the system and improves learning efficiency. Some or all of the above processes in the guide section may be performed using AI, or not. For example, the guide section could input employee usage data into a generating AI and have the generating AI generate the guide content.

[0100] The management department can estimate employees' emotions and adjust learning progress management methods based on those estimated emotions. For example, if an employee is stressed, the management department can ease up on learning progress management. Conversely, if an employee is relaxed, the management department can implement detailed progress management. Furthermore, if an employee is excited, the management department can implement aggressive progress management. This allows for more appropriate progress management by adjusting learning progress management methods according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, or not. For example, the management department can input employee emotion data into a generative AI and have the generative AI adjust learning progress management methods.

[0101] The information delivery unit can estimate an employee's emotions and adjust the way the information is presented based on the estimated emotions. For example, if an employee is stressed, the unit can provide concise and easy-to-understand information. If an employee is relaxed, the unit can provide information that includes detailed explanations. Furthermore, if an employee is excited, the unit can provide visually appealing information. This allows for the provision of more appropriate information by adjusting the way the information is presented according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input employee emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0102] The generation unit can estimate an employee's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if an employee is stressed, the generation unit can generate a concise and easy-to-understand response. If an employee is relaxed, the generation unit can also generate a response that includes detailed explanations. Furthermore, if an employee is excited, the generation unit can generate a visually appealing response. This allows for the provision of more appropriate responses by adjusting the way the response is expressed according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into the generation AI and have the generation AI adjust the way the response is expressed.

[0103] The reception desk can estimate the emotions of employees and adjust the timing of question acceptance based on the estimated emotions. For example, if an employee is feeling stressed, the reception desk can temporarily delay question acceptance to give them time to relax. Conversely, if an employee is focused, the reception desk can immediately accept questions and respond quickly. Furthermore, if an employee is tired, the reception desk can temporarily stop accepting questions to encourage them to take a break. This allows for question acceptance at a more appropriate time by adjusting the timing according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input employee facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The information delivery unit can estimate an employee's emotions and prioritize the information to be delivered based on the estimated emotions. For example, if an employee is feeling anxious, the unit can prioritize providing urgent information. If an employee is relaxed, the unit can prioritize providing normal information. Furthermore, if an employee is agitated, the unit can prioritize providing important information. This allows for the prioritization of more appropriate information based on 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 information delivery unit may be performed using AI or not. For example, the information delivery unit can input employee emotion data into a generative AI and have the generative AI determine the priority of information.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives employee questions. Employee questions can be in text format, voice format, or on specific topics. For example, employees can type questions in text format, and questions can also be submitted in voice format. Furthermore, questions on specific topics can also be accepted. Step 2: The generation unit generates answers to the questions received by the reception unit. The generation unit uses AI to generate answers to the questions and can utilize natural language processing technology, machine learning technology, and rule-based algorithms. Step 3: The providing unit provides the employee with the response generated by the generating unit. The providing unit can provide responses in text format, audio format, or include visual information. Step 4: The management department manages employee learning progress. The management department can evaluate employee learning progress, provide guidance and supplementary information according to the learning progress, and record and analyze learning progress.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives employee questions in text or voice format. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates answers to questions using AI. The provision unit is implemented by, for example, the output device 40 of the smart device 14 and provides the generated answers to employees in text or voice format. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the employee's learning progress and provides guidance and supplementary information according to the progress. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives employee questions in voice format. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates answers to questions using AI. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated answers to employees in voice format. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and manages the employee's learning progress and provides guidance and supplementary information according to the progress. 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.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives employee questions in voice format. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates answers to questions using AI. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the generated answers to employees in voice format. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the employee's learning progress and provides guidance and supplementary information according to the progress. 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.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives employee questions in voice format. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates answers to questions using AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated answers to employees in voice format. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the employee's learning progress and provides guidance and supplementary information according to the progress. 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) The reception desk that takes questions from employees, A generation unit that generates answers to questions received by the reception unit, A providing unit that provides the answers generated by the generation unit to the employees, It includes a management department that manages the learning progress of employees. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We provide customized solutions tailored to each company. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide standardized information The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Provide guidance and supplementary information tailored to the learning progress of employees. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the employee's emotions and adjusts the timing of questioning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze employees' past question history to select the most suitable reception method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving questions, filtering is performed based on the employee's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the employee's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant, taking into account the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving questions, the system analyzes employees' social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit 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 12) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating answers, different generation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the employee's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We estimate employees' emotions and adjust the way we present information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, the level of detail of the information will be adjusted based on the company's specific customization requirements. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing standardized information, different delivery algorithms are applied. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates employee sentiment and prioritizes the information provided based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information by taking into account the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, we analyze employees' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, Estimate employee sentiment and adjust learning progress management methods based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing learning progress, refer to employees' past learning history to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing learning progress, customize the management method based on the employee's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, The system estimates employee emotions and prioritizes learning progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When managing learning progress, select the optimal management method by considering the geographical location information of employees. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing learning progress, we analyze employees' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 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 reception desk that takes questions from employees, A generation unit that generates answers to questions received by the reception unit, A providing unit that provides the answers generated by the generation unit to the employees, It includes a management department that manages the learning progress of employees. A system characterized by the following features.

2. The aforementioned supply unit is, We provide customized solutions tailored to each company. The system according to feature 1.

3. The aforementioned supply unit is, Provide standardized information The system according to feature 1.

4. The aforementioned management department, Provide guidance and supplementary information tailored to the learning progress of employees. The system according to feature 1.

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

6. The aforementioned reception unit is Analyze employees' past question history to select the most suitable reception method. The system according to feature 1.

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

8. The aforementioned reception unit is The system estimates the employee's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant, taking into account the employee's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving questions, the system analyzes employees' social media activity and selects relevant questions. The system according to feature 1.

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