System

Abstract

An object of the system according to the embodiment is to provide an environment in which a new employee can easily ask a question and to support efficient human resource development.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a question. The analyzer analyzes the question received by the receiver. The generation unit generates an answer based on the question analyzed by the analysis unit. The providing unit provides the answer generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a high psychological hurdle for new employees to ask questions of their seniors, making it difficult to develop human resources efficiently.

[0005] The system according to the embodiment aims to provide an environment in which new employees can easily ask questions and to support efficient human resource development. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a question. The analysis unit analyzes the question received by the reception unit. The generation unit generates an answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment provides an environment in which new employees can easily ask questions, and can support efficient human resource development. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​personal mentoring system according to an embodiment of the present invention allows new employees to easily ask any question and receive an immediate answer. In the AI ​​personal mentoring system, new employees input their questions, and the AI ​​analyzes the questions and provides an immediate answer. This mechanism allows new employees to easily ask any question and provides the seeds for growth. Furthermore, the questions and answers posed by new employees are relevant to future customer questions and contribute to increasing internal knowledge. For example, a new employee can input questions such as "How is this project progressing?" or "Please tell me how to use this tool." The AI ​​personal mentoring system then uses natural language processing technology to understand the content of the question and generate an appropriate answer. For example, in response to the question "How is this project progressing?", the AI ​​personal mentoring system retrieves the latest progress from the project management system and provides an answer. The generated answer is immediately provided to the new employee. For example, in response to the question "Please tell me how to use this tool," the AI ​​personal mentoring system provides a manual or video on how to use the tool. In this way, new employees can easily ask any question and receive an immediate answer. This allows new employees to easily ask any question and receive an immediate answer. For example, if a new employee asks, "What are the features of this product?", the answer can be used to answer future questions from customers. In this way, the AI ​​personal mentor system contributes to human resource development and increasing in-house knowledge.

[0029] The AI ​​personal mentor system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions entered by new employees. Questions entered by new employees may be in, for example, text format, audio format, or image format, but are not limited to these examples. The reception unit receives questions in, for example, text format. The reception unit can also receive questions in audio format. The reception unit can also receive questions in image format. For example, the reception unit receives questions in text format through an input form. Audio questions are recorded using a microphone and converted into text using audio recognition technology. Image questions are captured using a camera and analyzed using image recognition technology. The analysis unit analyzes the questions received by the reception unit. The analysis is performed using, for example, natural language processing technology, but is not limited to these examples. For example, the analysis unit analyzes the content of the question using morphological analysis. The analysis unit can also analyze the structure of the question using grammatical analysis. The analysis unit can also understand the meaning of the question using semantic analysis. For example, the analysis unit uses morphological analysis to divide the words in the question and analyze the meaning of each word. Grammar analysis analyzes the grammatical structure of the question and understands the relationships between sentences. Semantic analysis understands the context of the question and extracts information for generating an appropriate answer. The generation unit generates an answer based on the question analyzed by the analysis unit. Generation is performed, for example, using natural language processing technology, but is not limited to this example. For example, the generation unit generates an answer using a text generation AI (e.g., LLM). The generation unit can also generate an answer using a multimodal generation AI. The generation unit can also obtain the latest progress from a project management system and generate an answer. For example, the generation unit generates an appropriate answer to a question using a text generation AI. The multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The generation unit obtains the latest progress from the project management system and generates an answer based on that information. The provision unit provides the answer generated by the generation unit. The provision is performed, for example, in text format, audio format, image format, etc., but is not limited to this example. For example, the providing unit provides the newcomer with an answer in text format.The providing unit can also provide an answer in audio format. The providing unit can also provide an answer in image format. For example, the providing unit displays an answer in text format through a chat window. An answer in audio format is played through a speaker. An answer in image format is displayed on a screen. In this way, the AI ​​personal mentor system according to the embodiment allows newcomers to easily ask any question and receive an answer immediately. For example, the providing unit immediately provides the answer generated by the generating unit to the newcomer. In this way, the newcomer can receive an answer quickly.

[0030] The generation unit can understand the content of the question and generate an answer using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the generation unit can use morphological analysis to divide the words of the question and analyze the meaning of each word. The generation unit can also analyze the grammatical structure of the question using grammatical analysis. The generation unit can also understand the context of the question using semantic analysis. For example, the generation unit can use morphological analysis to divide the words of the question and analyze the meaning of each word. The generation unit can also analyze the grammatical structure of the question using grammatical analysis. The generation unit can also understand the context of the question using semantic analysis. In this way, the use of natural language processing technology can accurately understand the content of the question and generate an appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the content of the question into a generation AI, which analyzes the content of the question and generates an appropriate answer.

[0031] The providing unit can provide the generated answer to the newcomer immediately. Immediately includes, but is not limited to, for example, within a few seconds or within a few minutes. For example, the providing unit can provide the generated answer to the newcomer within a few seconds. The providing unit can also provide the generated answer within a few minutes. The providing unit can also provide the generated answer in real time. For example, the providing unit can display the generated answer through a chat window within a few seconds, send the generated answer by email within a few minutes, or play the generated answer through a speaker in real time. In this way, the generated answer can be provided immediately, allowing the newcomer to quickly obtain the answer. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit inputs the generated answer to the generation AI, which then provides the answer immediately.

[0032] The reception unit can accept questions entered by new employees. New employees include, but are not limited to, employees who have been with the company for less than a year and employees who have joined a new project. The reception unit, for example, accepts questions entered by employees who have been with the company for less than a year. The reception unit can also accept questions entered by employees who have joined a new project. The reception unit can also accept questions entered by employees who are participating in a specific training program. For example, the reception unit accepts questions entered by employees who have been with the company for less than a year. The reception unit accepts questions entered by employees who have joined a new project. The reception unit accepts questions entered by employees who are participating in a specific training program. In this way, by accepting questions entered by new employees, employees can feel free to ask any question. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs questions entered by new employees into a generation AI, and the generation AI accepts the questions.

[0033] The analysis unit can collect related information based on the content of the question. Examples of related information include, but are not limited to, past question history and information from a database. The analysis unit, for example, collects related information based on past question history. The analysis unit can also collect related information from a database. The analysis unit can also collect related information from public information on the Internet. For example, the analysis unit collects related information based on past question history. Collects related information from a database. Collects related information from public information on the Internet. By collecting related information based on the content of the question, a more appropriate answer can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the content of the question into a generation AI, and the generation AI collects related information.

[0034] The generation unit can obtain the latest progress status from the project management system and generate an answer. Examples of project management systems include, but are not limited to, JIRA and Trello. The generation unit can obtain the latest progress status from, for example, JIRA and generate an answer. The generation unit can also obtain the latest progress status from Trello and generate an answer. The generation unit can also obtain the latest progress status from other project management systems and generate an answer. For example, the generation unit obtains the latest progress status from JIRA and generates an answer. The generation unit obtains the latest progress status from Trello and generates an answer. The generation unit can obtain the latest progress status from other project management systems and generate an answer. In this way, by obtaining the latest progress status from the project management system, an accurate answer can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the latest progress status obtained from the project management system into the generation AI, and the generation AI generates an answer.

[0035] The providing unit can provide a manual or a video on how to use the tool. Examples of the manual or video include, but are not limited to, a manual in PDF format, a YouTube (registered trademark) video, etc. The providing unit can provide, for example, a manual in PDF format. The providing unit can also provide a YouTube video. The providing unit can also provide a manual or a video in other formats. For example, the providing unit can provide a manual in PDF format. The providing unit can provide a YouTube video. The providing unit can provide a manual or a video in other formats. By providing a manual or a video on how to use the tool, newcomers can quickly understand how to use the tool. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit inputs a manual or a video on how to use the tool into the generation AI, which then provides it to the newcomer.

[0036] The generation unit can add questions asked by new employees and their answers to an internal knowledge base. Internal knowledge bases include, but are not limited to, Wiki-style knowledge bases and databases. For example, the generation unit adds questions and answers to a Wiki-style knowledge base. The generation unit can also add questions and answers to a database. The generation unit can also add questions and answers to knowledge bases in other formats. For example, the generation unit adds questions and answers to a Wiki-style knowledge base. Adds questions and answers to a database. Adds questions and answers to knowledge bases in other formats. In this way, adding questions asked by new employees and their answers to the internal knowledge base contributes to increasing internal knowledge. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs questions and answers into the generation AI, which adds them to the internal knowledge base.

[0037] The reception unit can analyze the new employee's past question history and select a reception method. The question history includes, for example, past questions and their answers, the frequency of questions, etc., but is not limited to these examples. The reception unit, for example, automatically suggests related questions based on the content of questions frequently asked by the new employee in the past. The reception unit can also prioritize suggesting question formats (text, voice, etc.) used by the new employee in the past. The reception unit can also suggest the reception method optimal for a specific time period based on the new employee's past question history. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the new employee in the past. The reception unit prioritizes suggesting question formats (text, voice, etc.) used by the new employee in the past. The reception unit suggests the reception method optimal for a specific time period based on the new employee's past question history. In this way, the optimal reception method can be selected by analyzing the new employee's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the new employee's past question history into a generation AI, which analyzes the question history and selects the optimal reception method.

[0038] When receiving a question, the reception unit can filter the question based on the new employee's current project or area of ​​interest. Examples of filtering include, but are not limited to, project type and area of ​​interest tags. For example, the reception unit preferentially receives questions related to the project in which the new employee is currently involved. The reception unit can also filter and receive related questions based on the new employee's area of ​​interest. The reception unit can also filter and receive questions based on topics in which the new employee has previously shown interest. For example, the reception unit preferentially receives questions related to the project in which the new employee is currently involved. The reception unit can filter and receive related questions based on the new employee's area of ​​interest. Questions can be filtered and received based on topics in which the new employee has previously shown interest. By filtering questions based on the new employee's current project and area of ​​interest, highly relevant questions can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit inputs data on the new employee's project information and area of ​​interest into the generation AI, which then filters the questions.

[0039] When accepting a question, the reception unit can select a reception means according to the new employee's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the new employee inputs a question by voice, the reception unit can accept the question using voice recognition technology. Furthermore, if the new employee inputs a question in text, the reception unit can also accept the question using text analysis technology. Furthermore, if the new employee inputs a question using an image, the reception unit can also accept the question using image recognition technology. For example, if the new employee inputs a question by voice, the reception unit can accept the question using voice recognition technology. If the new employee inputs a question in text, the reception unit can accept the question using text analysis technology. If the new employee inputs a question using an image, the reception unit can accept the question using image recognition technology. This allows the reception of questions to be smoothly carried out by selecting the optimal reception means according to the new employee's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the new employee's input data into a generation AI, which then selects the optimal reception means.

[0040] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the new employee's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the new employee is in the office, the reception unit can prioritize receiving office-related questions. Furthermore, when the new employee is out, the reception unit can prioritize receiving questions related to the new employee's destination. Furthermore, when the new employee is in a specific location, the reception unit can prioritize receiving questions related to the location. For example, when the new employee is in the office, the reception unit prioritizes receiving office-related questions. When the new employee is out, the reception unit prioritizes receiving questions related to the new employee's destination. When the new employee is in a specific location, the reception unit prioritizes receiving questions related to the location. In this way, by taking the new employee's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception department inputs the geographical location information of new employees into the generation AI, which then prioritizes accepting questions that are highly relevant.

[0041] When receiving a question, the reception unit can analyze the newcomer's social media activity and receive related questions. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. The reception unit can, for example, receive related questions based on the content posted by the newcomer on social media. The reception unit can also analyze the newcomer's social media activity history and receive related questions. The reception unit can also receive related questions by referring to the activities of the newcomer's friends on social media. For example, the reception unit can receive related questions based on the content posted by the newcomer on social media. The reception unit can analyze the newcomer's social media activity history and receive related questions. The reception unit can receive related questions by referring to the activities of the newcomer's friends on social media. In this way, by analyzing the newcomer's social media activity, related questions can be received preferentially. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the newcomer's social media data into the generation AI, and the generation AI receives related questions.

[0042] When receiving a question, the reception unit can customize the reception method by reflecting the new employee's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. For example, the reception unit suggests an optimal reception method based on feedback previously provided by the new employee. The reception unit can also prioritize and suggest a specific reception method based on the new employee's past feedback. The reception unit can also customize the reception method by reflecting the new employee's feedback. For example, the reception unit suggests an optimal reception method based on feedback previously provided by the new employee. The reception unit prioritizes and suggests a specific reception method based on the new employee's past feedback. The reception method is customized by reflecting the new employee's feedback. This allows the new employee's past feedback to be reflected to provide an optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit inputs the new employee's feedback data into the generation AI, which analyzes the feedback and customizes the reception method.

[0043] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The importance level includes, but is not limited to, the scope of impact and urgency of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a concise analysis on a question with a low level of importance. The analysis unit can also gradually adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. For example, the analysis unit performs a concise analysis on a question with a low level of importance. The level of detail of the analysis can be gradually adjusted based on the importance of the question. This allows for appropriate analysis results to be provided by adjusting the level of detail of the analysis based on the importance of the question. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs question importance data into the generation AI, which then adjusts the level of detail of the analysis.

[0044] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. Examples of categories include, but are not limited to, technical questions and business-related questions. For example, the analysis unit applies a specialized analysis algorithm for technical questions. The analysis unit can also apply a specialized analysis algorithm for business processes to questions about business processes. The analysis unit can also apply a general-purpose analysis algorithm to general questions. For example, the analysis unit applies a specialized analysis algorithm for technical questions. The analysis unit applies a specialized analysis algorithm for business processes to questions about business processes. The analysis unit can also apply a general-purpose analysis algorithm to general questions. By applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs question category data into the generation AI, which then applies an appropriate analysis algorithm.

[0045] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the results of past questions asked by the newcomer. Past question results include, for example, the accuracy and relevance of past answers, but are not limited to such examples. The analysis unit can improve the accuracy of the analysis by referring to, for example, questions asked by the newcomer in the past and their answers. The analysis unit can also improve the accuracy of the analysis by collecting related information based on the results of the newcomer's past questions. The analysis unit can also improve the accuracy of the analysis by analyzing the newcomer's past question history. For example, the analysis unit can improve the accuracy of the analysis by referring to the questions asked by the newcomer in the past and their answers. Based on the results of the newcomer's past questions, related information can be collected to improve the accuracy of the analysis. The newcomer's past question history can be analyzed to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the results of the newcomer's past questions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit inputs the newcomer's past question result data into the generation AI, and the generation AI improves the accuracy of the analysis.

[0046] When analyzing a question, the analysis unit can determine the analysis priority based on the time of submission of the question. The submission time includes, but is not limited to, for example, the date and time of submission and the elapsed time since submission. The analysis unit can determine the analysis priority based on, for example, the time period in which the question was submitted. The analysis unit can also gradually adjust the analysis priority depending on the time of submission of the question. The analysis unit can also prioritize analyzing questions with high urgency, taking into account the time of submission of the question. For example, the analysis unit determines the analysis priority based on the time period in which the question was submitted. The analysis unit gradually adjusts the analysis priority depending on the time of submission of the question. Questions with high urgency are prioritized for analysis, taking into account the time of submission of the question. In this way, by determining the analysis priority based on the time of submission of the question, questions with high urgency can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs question submission time data into a generation AI, and the generation AI determines the analysis priority.

[0047] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. Relevance includes, but is not limited to, similarity in question content and related topics. The analysis unit, for example, determines the order of analysis based on the relevance of the questions. The analysis unit can also prioritize analyzing questions with higher importance, taking into account the relevance of the questions. The analysis unit can also gradually adjust the order of analysis according to the relevance of the questions. For example, the analysis unit determines the order of analysis based on the relevance of the questions. Taking into account the relevance of the questions, the analysis unit prioritizes analyzing questions with higher importance. The analysis unit gradually adjusts the order of analysis according to the relevance of the questions. Thus, by adjusting the order of analysis based on the relevance of the questions, questions with higher importance can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs question relevance data into the generation AI, which then adjusts the order of analysis.

[0048] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the newcomer's level of expertise. Examples of levels of expertise include, but are not limited to, the presence or absence of qualifications and past experience. For example, if the newcomer's level of expertise is low, the analysis unit can provide the analysis results in simple language. Furthermore, if the newcomer's level of expertise is high, the analysis unit can provide the analysis results using more technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis according to the newcomer's level of expertise. For example, if the newcomer's level of expertise is low, the analysis unit can provide the analysis results in simple language. If the newcomer's level of expertise is high, the analysis unit can provide the analysis results using more technical terms. The use of technical terms in the analysis can be gradually adjusted according to the newcomer's level of expertise. By adjusting the use of technical terms in the analysis according to the newcomer's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the newcomer's level of expertise data into the generation AI, which then adjusts the use of technical terms in the analysis.

[0049] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The level of detail includes, but is not limited to, the specificity of the answer and the comprehensiveness of the information, for example. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also gradually adjust the level of detail of the answer depending on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. For example, the generation unit generates a concise answer for a question of low importance. The level of detail of the answer is gradually adjusted depending on the importance of the question. This allows an appropriate answer to be provided by adjusting the level of detail of the answer based on the importance of the question. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs question importance data into the generation AI, which then adjusts the level of detail of the answer.

[0050] When generating an answer, the generation unit can apply different generation algorithms depending on the question category. Examples of generation algorithms include, but are not limited to, rule-based generation and machine learning-based generation. For example, the generation unit applies a technically specialized generation algorithm to technical questions. The generation unit can also apply a business process specialized generation algorithm to questions about business processes. The generation unit can also apply a general-purpose generation algorithm to general questions. For example, the generation unit applies a technically specialized generation algorithm to technical questions. The generation unit applies a business process specialized generation algorithm to questions about business processes. The generation unit applies a general-purpose generation algorithm to general questions. In this way, by applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs question category data into the generation AI, which then applies an appropriate generation algorithm.

[0051] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the newcomer's past question results. Accuracy includes, but is not limited to, the accuracy of the answer and the margin of error. For example, the generation unit can improve the accuracy of the answer by referring to the newcomer's past questions and their answers. The generation unit can also improve the accuracy of the answer by collecting related information based on the newcomer's past question results. The generation unit can also improve the accuracy of the answer by analyzing the newcomer's past question history. For example, the generation unit can improve the accuracy of the answer by referring to the newcomer's past questions and their answers. Based on the newcomer's past question results, the generation unit can improve the accuracy of the answer by collecting related information. The newcomer's past question history can be analyzed to improve the accuracy of the answer. In this way, the accuracy of the answer can be improved by referring to the newcomer's past question results. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs the newcomer's past question result data into the generation AI, which then improves the accuracy of the answer.

[0052] When generating answers, the generation unit can determine the priority of the answers based on the time when the question was submitted. Priorities include, but are not limited to, the urgency and importance of the question. The generation unit can determine the priority of the answers based on, for example, the time of day when the question was submitted. The generation unit can also gradually adjust the priority of the answers depending on the time when the question was submitted. The generation unit can also prioritize answers to questions with higher urgency, taking into account the time when the question was submitted. For example, the generation unit determines the priority of the answers based on the time of day when the question was submitted. The generation unit gradually adjusts the priority of the answers depending on the time when the question was submitted. Questions with higher urgency are prioritized, taking into account the time when the question was submitted. In this way, by determining the priority of the answers based on the time when the question was submitted, questions with higher urgency can be prioritized. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs question submission time data into the generation AI, which then determines the priority of the answers.

[0053] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. The order includes, but is not limited to, the relevance and importance of the questions. For example, the generation unit determines the order of answers based on the relevance of the questions. The generation unit can also prioritize answering questions with higher importance, taking into account the relevance of the questions. The generation unit can also gradually adjust the order of answers according to the relevance of the questions. For example, the generation unit determines the order of answers based on the relevance of the questions. The generation unit prioritizes answering questions with higher importance, taking into account the relevance of the questions. The generation unit gradually adjusts the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, questions with higher importance can be prioritized. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs question relevance data into the generation AI, and the generation AI adjusts the order of the answers.

[0054] When generating an answer, the generation unit can adjust the use of technical terminology in the answer depending on the newcomer's level of expertise. Technical terminology includes, but is not limited to, technical terms and industry jargon. For example, if the newcomer's level of expertise is low, the generation unit can generate an answer using simple language. Furthermore, if the newcomer's level of expertise is high, the generation unit can generate an answer using a lot of technical terminology. Furthermore, the generation unit can gradually adjust the use of technical terminology in the answer depending on the newcomer's level of expertise. For example, if the newcomer's level of expertise is low, the generation unit generates an answer using simple language. If the newcomer's level of expertise is high, the generation unit generates an answer using a lot of technical terminology. The use of technical terminology in the answer is gradually adjusted depending on the newcomer's level of expertise. In this way, by adjusting the use of technical terminology in the answer depending on the newcomer's level of expertise, a more understandable answer can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit inputs the newcomer's level of expertise data into the generation AI, which then adjusts the use of technical terminology in the answer.

[0055] When providing an answer, the providing unit can select a delivery method by referring to the newcomer's past question history. The question history includes, for example, past questions and their answers, the frequency of questions, etc., but is not limited to these examples. For example, the providing unit preferentially provides a delivery method (audio, text, etc.) that the newcomer has used in the past. The providing unit can also preferentially suggest a specific delivery method from the newcomer's past question history. The providing unit can also select an optimal delivery method based on the newcomer's past question history. For example, the providing unit preferentially provides a delivery method (audio, text, etc.) that the newcomer has used in the past. The providing unit preferentially suggests a specific delivery method from the newcomer's past question history. The optimal delivery method is selected based on the newcomer's past question history. In this way, the optimal delivery method can be selected by referring to the newcomer's past question history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without using, a generation AI. For example, the providing unit inputs the newcomer's question history data into the generation AI, which selects the optimal delivery method.

[0056] When providing an answer, the providing unit can customize the provided content according to the new employee's current task. Tasks include, but are not limited to, for example, a current project, work content, etc. The providing unit, for example, prioritizes providing answers related to the task the new employee is currently working on. The providing unit can also customize and provide related information according to the new employee's current task. The providing unit can also provide the optimal answer taking into account the new employee's current task. For example, the providing unit prioritizes providing answers related to the task the new employee is currently working on. The providing unit customizes and provides related information according to the new employee's current task. The providing unit provides the optimal answer taking into account the new employee's current task. In this way, by customizing the provided content according to the new employee's current task, a more relevant answer can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the new employee's task data into the generation AI, and the generation AI customizes the provided content.

[0057] The providing unit can improve the answer providing method by reflecting the newcomer's feedback when providing an answer. Examples of feedback include, but are not limited to, survey results and user comments. For example, when a newcomer provides feedback on an answer provided by the newcomer, the providing unit improves the answer providing method based on the feedback. The providing unit can also customize the answer providing method by reflecting the newcomer's feedback. The providing unit can also gradually improve the answer providing method based on the newcomer's feedback. For example, when a newcomer provides feedback on an answer provided by the newcomer, the providing unit improves the answer providing method based on the feedback. The providing unit customizes the answer providing method by reflecting the newcomer's feedback. The providing unit gradually improves the answer providing method based on the newcomer's feedback. In this way, the answer providing method can be improved by reflecting the newcomer's feedback, and more appropriate answers can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the newcomer's feedback data into the generation AI, which analyzes the feedback and improves the answer providing method.

[0058] When providing an answer, the providing unit can select the optimal presentation method by taking into account the newcomer's device information. Device information includes, but is not limited to, the device type, OS version, etc. For example, if the newcomer is using a smartphone, the providing unit can provide a presentation method tailored to the screen size. Furthermore, if the newcomer is using a tablet, the providing unit can also provide a presentation method optimized for a large screen. Furthermore, if the newcomer is using a desktop, the providing unit can also provide a presentation method including detailed information. For example, if the newcomer is using a smartphone, the providing unit can provide a presentation method tailored to the screen size. If the newcomer is using a tablet, the providing unit can provide a presentation method optimized for a large screen. If the newcomer is using a desktop, the providing unit can provide a presentation method including detailed information. This allows the optimal presentation method to be selected by taking into account the newcomer's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the newcomer's device information into the generation AI, which then selects the optimal presentation method.

[0059] When providing an answer, the providing unit can make the provided content multilingual according to the newcomer's language setting. Language settings include, for example, the user's language setting and the priority of the language used, but are not limited to these examples. The providing unit, for example, automatically sets the language of the answer based on the language setting of the newcomer's device. The providing unit can also provide a language switching function when the newcomer uses multiple languages. The providing unit can also provide the answer in a specific language when the newcomer selects that language. For example, the providing unit automatically sets the language of the answer based on the language setting of the newcomer's device. The providing unit can provide a language switching function when the newcomer uses multiple languages. If the newcomer selects a specific language, the answer is provided in that language. By making the provided content multilingual according to the newcomer's language setting, more understandable answers can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the newcomer's language setting data into the generation AI, which then generates a multilingual answer.

[0060] When providing an answer, the providing unit can customize the providing method according to the learning style of the new employee. Examples of learning styles include, but are not limited to, visual learning and auditory learning. For example, if the new employee has a visual learning style, the providing unit can provide an answer including diagrams and videos. Furthermore, if the new employee has an auditory learning style, the providing unit can provide an audio answer. Furthermore, if the new employee has a hands-on learning style, the providing unit can provide an answer including practical examples. For example, if the new employee has a visual learning style, the providing unit provides an answer including diagrams and videos. If the new employee has an auditory learning style, the providing unit provides an audio answer. If the new employee has a hands-on learning style, the providing unit provides an answer including practical examples. In this way, by customizing the providing method according to the new employee's learning style, more effective answers can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the new employee's learning style data into the generation AI, which selects the optimal providing method.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] When accepting questions entered by new employees, the reception unit can classify the questions into appropriate categories based on their content. For example, the reception unit can classify the questions into technical questions, questions related to business processes, general questions, etc. This allows the analysis unit to select an appropriate analysis algorithm when analyzing the questions. Furthermore, when the provision unit provides answers, the answers can be provided in an appropriate format. For example, answers including detailed technical information can be provided for technical questions, and answers explaining the steps of the business processes can be provided for questions related to business processes. This allows new employees to quickly obtain more appropriate answers when asking questions.

[0063] The generator can generate an answer by referencing related external resources based on the content of the question. For example, it can refer to public information on the Internet, specialized books, industry best practices, etc. This allows the generator to provide an answer that contains more information. The generator can also evaluate the reliability of the answer based on the information obtained from the external resources and prioritize the use of reliable information. For example, by providing answers based on reliable specialized books and industry best practices, it is possible to ensure that newcomers can use the information with confidence.

[0064] When providing the generated answer, the providing unit can select the notification method based on the importance of the answer. For example, a push notification or an urgent email can be sent for an answer with a high importance, and a notification can be sent by regular email or in a chat window for an answer with a low importance. This allows new employees to respond quickly without missing important information. The providing unit can also adjust the frequency and timing of notifications depending on the importance of the answer. For example, an answer with a high importance can be notified immediately, and an answer with a low importance can be notified at regular time intervals.

[0065] When receiving a question entered by a new employee, the reception unit can convert the question into an appropriate format based on the content of the question. For example, it can convert a voice-based question into text format, and an image-based question into text format. This makes it easier for the analysis unit to analyze the question and generate a more accurate answer. The reception unit can also supplement necessary information when converting the question into an appropriate format based on the content of the question. For example, it can convert a voice-based question into text using speech recognition technology, and supplement the necessary information.

[0066] The analysis unit can perform analysis by referring to related past questions and answers based on the content of the question. For example, if a similar question has been asked in the past, by performing analysis by referring to those questions and answers, it is possible to generate a faster and more accurate answer. The analysis unit can also analyze question trends and patterns based on past questions and answers and make predictions about future questions. This allows new employees to quickly obtain more appropriate answers when asking questions.

[0067] When obtaining the latest progress information from the project management system, the generation unit can filter the information based on the priority of the project. For example, the generation unit can preferentially obtain the progress information of high-priority projects and generate an answer. The generation unit can also adjust the level of detail of the answer based on the progress of the project. For example, the generation unit can provide a concise answer if progress is going smoothly and a detailed answer if there are problems with the progress. This allows new employees to quickly understand the progress of the project and take appropriate action.

[0068] When providing manuals and videos on how to use the tool, the provision unit can customize the content to be provided based on the learning progress of the new employee. For example, if the new employee already understands basic usage, the provision unit can provide manuals and videos on advanced usage, and for beginners, the provision unit can provide manuals and videos on basic usage. The provision unit can also adjust the difficulty level of the content to be provided according to the learning progress of the new employee. For example, the provision unit can provide easy content to beginners and advanced content to advanced users.

[0069] When adding questions and answers asked by new employees to the company's knowledge base, the generation department can assign appropriate tags based on the question's category. For example, a technical question can be tagged as "Technical" and a question about business processes can be tagged as "Business Process." This allows other employees to quickly find relevant information when searching the knowledge base. Furthermore, when adding questions and answers, the generation department can also set links to other related questions and answers. This allows the information in the knowledge base to be organized more systematically and become easier to use.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception department accepts questions entered by new employees. Questions can be in text, audio, or image format. For example, text questions are accepted through an input form, audio questions are recorded through a microphone and converted into text using voice recognition technology, and image questions are captured using a camera and analyzed using image recognition technology. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing techniques, including morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides the question into words and analyzes the meaning of each word. Grammatical analysis analyzes the grammatical structure of the question and understands the relationships between sentences. Semantic analysis understands the context of the question and extracts information to generate an appropriate answer. Step 3: The generator generates answers based on the questions analyzed by the analyzer. Natural language processing techniques, such as text generation AI (e.g., LLM) or multimodal generation AI, can be used. The generator can also obtain the latest progress information from the project management system and generate answers based on that information. Step 4: The providing unit provides the answer generated by the generating unit. The answer may be provided in text format, audio format, image format, etc. For example, a text format answer may be displayed through a chat window, an audio format answer may be played through a speaker, and an image format answer may be displayed on a screen.

[0072] (Example 2) The AI ​​personal mentoring system according to an embodiment of the present invention allows new employees to easily ask any question and receive an immediate answer. In the AI ​​personal mentoring system, new employees input their questions, and the AI ​​analyzes the questions and provides an immediate answer. This mechanism allows new employees to easily ask any question and provides the seeds for growth. Furthermore, the questions and answers posed by new employees are relevant to future customer questions and contribute to increasing internal knowledge. For example, a new employee can input questions such as "How is this project progressing?" or "Please tell me how to use this tool." The AI ​​personal mentoring system then uses natural language processing technology to understand the content of the question and generate an appropriate answer. For example, in response to the question "How is this project progressing?", the AI ​​personal mentoring system retrieves the latest progress from the project management system and provides an answer. The generated answer is immediately provided to the new employee. For example, in response to the question "Please tell me how to use this tool," the AI ​​personal mentoring system provides a manual or video on how to use the tool. In this way, new employees can easily ask any question and receive an immediate answer. This allows new employees to easily ask any question and receive an immediate answer. For example, if a new employee asks, "What are the features of this product?", the answer can be used to answer future questions from customers. In this way, the AI ​​personal mentor system contributes to human resource development and increasing in-house knowledge.

[0073] The AI ​​personal mentor system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions entered by new employees. Questions entered by new employees may be in, for example, text format, audio format, or image format, but are not limited to these examples. The reception unit receives questions in, for example, text format. The reception unit can also receive questions in audio format. The reception unit can also receive questions in image format. For example, the reception unit receives questions in text format through an input form. Audio questions are recorded using a microphone and converted into text using audio recognition technology. Image questions are captured using a camera and analyzed using image recognition technology. The analysis unit analyzes the questions received by the reception unit. The analysis is performed using, for example, natural language processing technology, but is not limited to these examples. For example, the analysis unit analyzes the content of the question using morphological analysis. The analysis unit can also analyze the structure of the question using grammatical analysis. The analysis unit can also understand the meaning of the question using semantic analysis. For example, the analysis unit uses morphological analysis to divide the words in the question and analyze the meaning of each word. Grammar analysis analyzes the grammatical structure of the question and understands the relationships between sentences. Semantic analysis understands the context of the question and extracts information for generating an appropriate answer. The generation unit generates an answer based on the question analyzed by the analysis unit. Generation is performed, for example, using natural language processing technology, but is not limited to this example. For example, the generation unit generates an answer using a text generation AI (e.g., LLM). The generation unit can also generate an answer using a multimodal generation AI. The generation unit can also obtain the latest progress from a project management system and generate an answer. For example, the generation unit generates an appropriate answer to a question using a text generation AI. The multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The generation unit obtains the latest progress from the project management system and generates an answer based on that information. The provision unit provides the answer generated by the generation unit. The provision is performed, for example, in text format, audio format, image format, etc., but is not limited to this example. For example, the providing unit provides the newcomer with an answer in text format.The providing unit can also provide an answer in audio format. The providing unit can also provide an answer in image format. For example, the providing unit displays an answer in text format through a chat window. An answer in audio format is played through a speaker. An answer in image format is displayed on a screen. In this way, the AI ​​personal mentor system according to the embodiment allows newcomers to easily ask any question and receive an answer immediately. For example, the providing unit immediately provides the answer generated by the generating unit to the newcomer. In this way, the newcomer can receive an answer quickly.

[0074] The generation unit can understand the content of the question and generate an answer using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the generation unit can use morphological analysis to divide the words of the question and analyze the meaning of each word. The generation unit can also analyze the grammatical structure of the question using grammatical analysis. The generation unit can also understand the context of the question using semantic analysis. For example, the generation unit can use morphological analysis to divide the words of the question and analyze the meaning of each word. The generation unit can also analyze the grammatical structure of the question using grammatical analysis. The generation unit can also understand the context of the question using semantic analysis. In this way, the use of natural language processing technology can accurately understand the content of the question and generate an appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the content of the question into a generation AI, which analyzes the content of the question and generates an appropriate answer.

[0075] The providing unit can provide the generated answer to the newcomer immediately. Immediately includes, but is not limited to, for example, within a few seconds or within a few minutes. For example, the providing unit can provide the generated answer to the newcomer within a few seconds. The providing unit can also provide the generated answer within a few minutes. The providing unit can also provide the generated answer in real time. For example, the providing unit can display the generated answer through a chat window within a few seconds, send the generated answer by email within a few minutes, or play the generated answer through a speaker in real time. In this way, the generated answer can be provided immediately, allowing the newcomer to quickly obtain the answer. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit inputs the generated answer to the generation AI, which then provides the answer immediately.

[0076] The reception unit can accept questions entered by new employees. New employees include, but are not limited to, employees who have been with the company for less than a year and employees who have joined a new project. The reception unit, for example, accepts questions entered by employees who have been with the company for less than a year. The reception unit can also accept questions entered by employees who have joined a new project. The reception unit can also accept questions entered by employees who are participating in a specific training program. For example, the reception unit accepts questions entered by employees who have been with the company for less than a year. The reception unit accepts questions entered by employees who have joined a new project. The reception unit accepts questions entered by employees who are participating in a specific training program. In this way, by accepting questions entered by new employees, employees can feel free to ask any question. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs questions entered by new employees into a generation AI, and the generation AI accepts the questions.

[0077] The analysis unit can collect related information based on the content of the question. Examples of related information include, but are not limited to, past question history and information from a database. The analysis unit, for example, collects related information based on past question history. The analysis unit can also collect related information from a database. The analysis unit can also collect related information from public information on the Internet. For example, the analysis unit collects related information based on past question history. Collects related information from a database. Collects related information from public information on the Internet. By collecting related information based on the content of the question, a more appropriate answer can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the content of the question into a generation AI, and the generation AI collects related information.

[0078] The generation unit can obtain the latest progress status from the project management system and generate an answer. Examples of project management systems include, but are not limited to, JIRA and Trello. The generation unit can obtain the latest progress status from, for example, JIRA and generate an answer. The generation unit can also obtain the latest progress status from Trello and generate an answer. The generation unit can also obtain the latest progress status from other project management systems and generate an answer. For example, the generation unit obtains the latest progress status from JIRA and generates an answer. The generation unit obtains the latest progress status from Trello and generates an answer. The generation unit can obtain the latest progress status from other project management systems and generate an answer. In this way, by obtaining the latest progress status from the project management system, an accurate answer can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the latest progress status obtained from the project management system into the generation AI, and the generation AI generates an answer.

[0079] The providing unit can provide a manual or a video on how to use the tool. Examples of the manual or video include, but are not limited to, a manual in PDF format, a YouTube video, etc. The providing unit can provide, for example, a manual in PDF format. The providing unit can also provide a YouTube video. The providing unit can also provide a manual or a video in other formats. For example, the providing unit can provide a manual in PDF format. It can provide a YouTube video. It can provide a manual or a video in other formats. By providing a manual or a video on how to use the tool, newcomers can quickly understand how to use the tool. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit inputs a manual or a video on how to use the tool into the generation AI, which then provides it to the newcomer.

[0080] The generation unit can add questions asked by new employees and their answers to an internal knowledge base. Internal knowledge bases include, but are not limited to, Wiki-style knowledge bases and databases. For example, the generation unit adds questions and answers to a Wiki-style knowledge base. The generation unit can also add questions and answers to a database. The generation unit can also add questions and answers to knowledge bases in other formats. For example, the generation unit adds questions and answers to a Wiki-style knowledge base. Adds questions and answers to a database. Adds questions and answers to knowledge bases in other formats. In this way, adding questions asked by new employees and their answers to the internal knowledge base contributes to increasing internal knowledge. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs questions and answers into the generation AI, which adds them to the internal knowledge base.

[0081] The reception unit can estimate the new employee's emotions and adjust the timing of accepting questions based on the estimated emotions. For example, if the new employee is nervous, the reception unit displays a message encouraging the AI ​​to relax and delays the timing of accepting questions. Furthermore, if the new employee is relaxed, the reception unit can immediately accept questions and respond quickly. Furthermore, if the new employee is impatient, the reception unit can display a message encouraging the AI ​​to calm down and adjust the timing of accepting questions. For example, if the new employee is nervous, the reception unit displays a message encouraging the AI ​​to relax and delays the timing of accepting questions. If the new employee is relaxed, the reception unit can immediately accept questions and respond quickly. If the new employee is impatient, the reception unit displays a message encouraging the AI ​​to calm down and adjusts the timing of accepting questions. In this way, by adjusting the timing of accepting questions according to the new employee's emotions, questions can be accepted at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs emotion data of new employees into the generation AI, which then estimates the emotions and adjusts the timing of receiving questions.

[0082] The reception unit can analyze the new employee's past question history and select a reception method. The question history includes, for example, past questions and their answers, the frequency of questions, etc., but is not limited to these examples. The reception unit, for example, automatically suggests related questions based on the content of questions frequently asked by the new employee in the past. The reception unit can also prioritize suggesting question formats (text, voice, etc.) used by the new employee in the past. The reception unit can also suggest the reception method optimal for a specific time period based on the new employee's past question history. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the new employee in the past. The reception unit prioritizes suggesting question formats (text, voice, etc.) used by the new employee in the past. The reception unit suggests the reception method optimal for a specific time period based on the new employee's past question history. In this way, the optimal reception method can be selected by analyzing the new employee's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the new employee's past question history into a generation AI, which analyzes the question history and selects the optimal reception method.

[0083] When receiving a question, the reception unit can filter the question based on the new employee's current project or area of ​​interest. Examples of filtering include, but are not limited to, project type and area of ​​interest tags. For example, the reception unit preferentially receives questions related to the project in which the new employee is currently involved. The reception unit can also filter and receive related questions based on the new employee's area of ​​interest. The reception unit can also filter and receive questions based on topics in which the new employee has previously shown interest. For example, the reception unit preferentially receives questions related to the project in which the new employee is currently involved. The reception unit can filter and receive related questions based on the new employee's area of ​​interest. Questions can be filtered and received based on topics in which the new employee has previously shown interest. By filtering questions based on the new employee's current project and area of ​​interest, highly relevant questions can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit inputs data on the new employee's project information and area of ​​interest into the generation AI, which then filters the questions.

[0084] When accepting a question, the reception unit can select a reception means according to the new employee's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the new employee inputs a question by voice, the reception unit can accept the question using voice recognition technology. Furthermore, if the new employee inputs a question in text, the reception unit can also accept the question using text analysis technology. Furthermore, if the new employee inputs a question using an image, the reception unit can also accept the question using image recognition technology. For example, if the new employee inputs a question by voice, the reception unit can accept the question using voice recognition technology. If the new employee inputs a question in text, the reception unit can accept the question using text analysis technology. If the new employee inputs a question using an image, the reception unit can accept the question using image recognition technology. This allows the reception of questions to be smoothly carried out by selecting the optimal reception means according to the new employee's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the new employee's input data into a generation AI, which then selects the optimal reception means.

[0085] The reception unit can estimate the new employee's emotions and determine the priority of questions to be received based on the estimated new employee's emotions. Priorities include, but are not limited to, the urgency and importance of the questions. For example, if the new employee is nervous, the reception unit can prioritize questions with lower importance. Furthermore, if the new employee is relaxed, the reception unit can prioritize questions with higher importance. Furthermore, if the new employee is impatient, the reception unit can prioritize questions with higher urgency. For example, if the new employee is nervous, the reception unit can prioritize questions with lower importance. If the new employee is relaxed, the reception unit can prioritize questions with higher importance. If the new employee is impatient, the reception unit can prioritize questions with higher urgency. Thus, by determining the priority of questions according to the new employee's emotions, more appropriate questions can be prioritized. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI. For example, the reception unit inputs emotion data of new employees into the generation AI, which then estimates the emotions and determines the priority of questions.

[0086] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the new employee's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the new employee is in the office, the reception unit can prioritize receiving office-related questions. Furthermore, when the new employee is out, the reception unit can prioritize receiving questions related to the new employee's destination. Furthermore, when the new employee is in a specific location, the reception unit can prioritize receiving questions related to the location. For example, when the new employee is in the office, the reception unit prioritizes receiving office-related questions. When the new employee is out, the reception unit prioritizes receiving questions related to the new employee's destination. When the new employee is in a specific location, the reception unit prioritizes receiving questions related to the location. In this way, by taking the new employee's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception department inputs the geographical location information of new employees into the generation AI, which then prioritizes accepting questions that are highly relevant.

[0087] When receiving a question, the reception unit can analyze the newcomer's social media activity and receive related questions. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to these examples. The reception unit can, for example, receive related questions based on the content posted by the newcomer on social media. The reception unit can also analyze the newcomer's social media activity history and receive related questions. The reception unit can also receive related questions by referring to the activities of the newcomer's friends on social media. For example, the reception unit can receive related questions based on the content posted by the newcomer on social media. The reception unit can analyze the newcomer's social media activity history and receive related questions. The reception unit can receive related questions by referring to the activities of the newcomer's friends on social media. In this way, by analyzing the newcomer's social media activity, related questions can be received preferentially. Some or all of the above-described processing by the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit inputs the newcomer's social media data into the generation AI, and the generation AI receives related questions.

[0088] When receiving a question, the reception unit can customize the reception method by reflecting the new employee's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. For example, the reception unit suggests an optimal reception method based on feedback previously provided by the new employee. The reception unit can also prioritize and suggest a specific reception method based on the new employee's past feedback. The reception unit can also customize the reception method by reflecting the new employee's feedback. For example, the reception unit suggests an optimal reception method based on feedback previously provided by the new employee. The reception unit prioritizes and suggests a specific reception method based on the new employee's past feedback. The reception method is customized by reflecting the new employee's feedback. This allows the new employee's past feedback to be reflected to provide an optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit inputs the new employee's feedback data into the generation AI, which analyzes the feedback and customizes the reception method.

[0089] The analysis unit can estimate the new employee's emotions and adjust the way the analysis is presented based on the estimated new employee's emotions. Examples of presentation methods include, but are not limited to, concise text and the use of diagrams. For example, if the new employee is nervous, the analysis unit uses a simple and easy-to-understand presentation method. Furthermore, if the new employee is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the new employee is impatient, the analysis unit can provide analysis results that focus on the main points. For example, if the new employee is nervous, the analysis unit uses a simple and easy-to-understand presentation method. If the new employee is relaxed, the analysis unit provides detailed analysis results. If the new employee is impatient, the analysis unit provides analysis results that focus on the main points. By adjusting the way the analysis is presented based on the new employee's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI. For example, the analysis unit inputs the emotion data of the new employee into the generation AI, which infers the emotion and adjusts the expression method of the analysis.

[0090] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The importance level includes, but is not limited to, the scope of impact and urgency of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a concise analysis on a question with a low level of importance. The analysis unit can also gradually adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. For example, the analysis unit performs a concise analysis on a question with a low level of importance. The level of detail of the analysis can be gradually adjusted based on the importance of the question. This allows for appropriate analysis results to be provided by adjusting the level of detail of the analysis based on the importance of the question. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs question importance data into the generation AI, which then adjusts the level of detail of the analysis.

[0091] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. Examples of categories include, but are not limited to, technical questions and business-related questions. For example, the analysis unit applies a specialized analysis algorithm for technical questions. The analysis unit can also apply a specialized analysis algorithm for business processes to questions about business processes. The analysis unit can also apply a general-purpose analysis algorithm to general questions. For example, the analysis unit applies a specialized analysis algorithm for technical questions. The analysis unit applies a specialized analysis algorithm for business processes to questions about business processes. The analysis unit can also apply a general-purpose analysis algorithm to general questions. By applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs question category data into the generation AI, which then applies an appropriate analysis algorithm.

[0092] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the results of past questions asked by the newcomer. Past question results include, for example, the accuracy and relevance of past answers, but are not limited to such examples. The analysis unit can improve the accuracy of the analysis by referring to, for example, questions asked by the newcomer in the past and their answers. The analysis unit can also improve the accuracy of the analysis by collecting related information based on the results of the newcomer's past questions. The analysis unit can also improve the accuracy of the analysis by analyzing the newcomer's past question history. For example, the analysis unit can improve the accuracy of the analysis by referring to the questions asked by the newcomer in the past and their answers. Based on the results of the newcomer's past questions, related information can be collected to improve the accuracy of the analysis. The newcomer's past question history can be analyzed to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the results of the newcomer's past questions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit inputs the newcomer's past question result data into the generation AI, and the generation AI improves the accuracy of the analysis.

[0093] The analysis unit can estimate the new employee's emotions and adjust the length of the analysis based on the estimated new employee's emotions. Examples of the length include, but are not limited to, the number of characters and the number of pages. For example, if the new employee is nervous, the analysis unit can provide a short and concise analysis result. Furthermore, if the new employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the new employee is impatient, the analysis unit can provide a concise and easy-to-understand analysis result. For example, if the new employee is nervous, the analysis unit can provide a short and concise analysis result. If the new employee is relaxed, the analysis unit can provide a detailed analysis result. If the new employee is impatient, the analysis unit can provide a concise and easy-to-understand analysis result. In this way, by adjusting the length of the analysis according to the new employee's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit inputs the new employee's emotional data into the generation AI, which then estimates the emotion and adjusts the length of the analysis.

[0094] When analyzing a question, the analysis unit can determine the analysis priority based on the time of submission of the question. The submission time includes, but is not limited to, for example, the date and time of submission and the elapsed time since submission. The analysis unit can determine the analysis priority based on, for example, the time period in which the question was submitted. The analysis unit can also gradually adjust the analysis priority depending on the time of submission of the question. The analysis unit can also prioritize analyzing questions with high urgency, taking into account the time of submission of the question. For example, the analysis unit determines the analysis priority based on the time period in which the question was submitted. The analysis unit gradually adjusts the analysis priority depending on the time of submission of the question. Questions with high urgency are prioritized for analysis, taking into account the time of submission of the question. In this way, by determining the analysis priority based on the time of submission of the question, questions with high urgency can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs question submission time data into a generation AI, and the generation AI determines the analysis priority.

[0095] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. Relevance includes, but is not limited to, similarity in question content and related topics. The analysis unit, for example, determines the order of analysis based on the relevance of the questions. The analysis unit can also prioritize analyzing questions with higher importance, taking into account the relevance of the questions. The analysis unit can also gradually adjust the order of analysis according to the relevance of the questions. For example, the analysis unit determines the order of analysis based on the relevance of the questions. Taking into account the relevance of the questions, the analysis unit prioritizes analyzing questions with higher importance. The analysis unit gradually adjusts the order of analysis according to the relevance of the questions. Thus, by adjusting the order of analysis based on the relevance of the questions, questions with higher importance can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs question relevance data into the generation AI, which then adjusts the order of analysis.

[0096] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the newcomer's level of expertise. Examples of levels of expertise include, but are not limited to, the presence or absence of qualifications and past experience. For example, if the newcomer's level of expertise is low, the analysis unit can provide the analysis results in simple language. Furthermore, if the newcomer's level of expertise is high, the analysis unit can provide the analysis results using more technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis according to the newcomer's level of expertise. For example, if the newcomer's level of expertise is low, the analysis unit can provide the analysis results in simple language. If the newcomer's level of expertise is high, the analysis unit can provide the analysis results using more technical terms. The use of technical terms in the analysis can be gradually adjusted according to the newcomer's level of expertise. By adjusting the use of technical terms in the analysis according to the newcomer's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs the newcomer's level of expertise data into the generation AI, which then adjusts the use of technical terms in the analysis.

[0097] The generation unit can estimate the newcomer's emotions and adjust the answer generation method based on the estimated newcomer's emotions. Examples of the generation method include, but are not limited to, the level of detail of the answer and the algorithm used. For example, if the newcomer is nervous, the generation unit generates a simple and easy-to-understand answer. Also, if the newcomer is relaxed, the generation unit can generate a detailed answer. Also, if the newcomer is impatient, the generation unit can generate an answer that focuses on the main points. For example, if the newcomer is nervous, the generation unit generates a simple and easy-to-understand answer. If the newcomer is relaxed, the generation unit generates a detailed answer. If the newcomer is impatient, the generation unit generates an answer that focuses on the main points. In this way, by adjusting the answer generation method according to the newcomer's emotions, a more appropriate answer can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the new employee's emotional data into the generation AI, which then estimates the emotion and adjusts how it generates answers.

[0098] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The level of detail includes, but is not limited to, the specificity of the answer and the comprehensiveness of the information, for example. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also gradually adjust the level of detail of the answer depending on the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. For example, the generation unit generates a concise answer for a question of low importance. The level of detail of the answer is gradually adjusted depending on the importance of the question. This allows an appropriate answer to be provided by adjusting the level of detail of the answer based on the importance of the question. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs question importance data into the generation AI, which then adjusts the level of detail of the answer.

[0099] When generating an answer, the generation unit can apply different generation algorithms depending on the question category. Examples of generation algorithms include, but are not limited to, rule-based generation and machine learning-based generation. For example, the generation unit applies a technically specialized generation algorithm to technical questions. The generation unit can also apply a business process specialized generation algorithm to questions about business processes. The generation unit can also apply a general-purpose generation algorithm to general questions. For example, the generation unit applies a technically specialized generation algorithm to technical questions. The generation unit applies a business process specialized generation algorithm to questions about business processes. The generation unit applies a general-purpose generation algorithm to general questions. In this way, by applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs question category data into the generation AI, which then applies an appropriate generation algorithm.

[0100] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the newcomer's past question results. Accuracy includes, but is not limited to, the accuracy of the answer and the margin of error. For example, the generation unit can improve the accuracy of the answer by referring to the newcomer's past questions and their answers. The generation unit can also improve the accuracy of the answer by collecting related information based on the newcomer's past question results. The generation unit can also improve the accuracy of the answer by analyzing the newcomer's past question history. For example, the generation unit can improve the accuracy of the answer by referring to the newcomer's past questions and their answers. Based on the newcomer's past question results, the generation unit can improve the accuracy of the answer by collecting related information. The newcomer's past question history can be analyzed to improve the accuracy of the answer. In this way, the accuracy of the answer can be improved by referring to the newcomer's past question results. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs the newcomer's past question result data into the generation AI, which then improves the accuracy of the answer.

[0101] The generation unit can estimate the newcomer's emotions and adjust the length of the answer based on the estimated newcomer's emotions. Examples of the length include, but are not limited to, the number of characters and the number of pages. For example, if the newcomer is nervous, the generation unit generates a short and to-the-point answer. Also, if the newcomer is relaxed, the generation unit can generate a detailed answer. Also, if the newcomer is impatient, the generation unit can generate a concise and easy-to-understand answer. For example, if the newcomer is nervous, the generation unit generates a short and to-the-point answer. If the newcomer is relaxed, the generation unit generates a detailed answer. If the newcomer is impatient, the generation unit generates a concise and easy-to-understand answer. This allows the length of the answer to be adjusted according to the newcomer's emotions, thereby providing a more appropriate answer. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the new employee's emotional data into the generation AI, which then estimates the emotion and adjusts the length of the response.

[0102] When generating answers, the generation unit can determine the priority of the answers based on the time when the question was submitted. Priorities include, but are not limited to, the urgency and importance of the question. The generation unit can determine the priority of the answers based on, for example, the time of day when the question was submitted. The generation unit can also gradually adjust the priority of the answers depending on the time when the question was submitted. The generation unit can also prioritize answers to questions with higher urgency, taking into account the time when the question was submitted. For example, the generation unit determines the priority of the answers based on the time of day when the question was submitted. The generation unit gradually adjusts the priority of the answers depending on the time when the question was submitted. Questions with higher urgency are prioritized, taking into account the time when the question was submitted. In this way, by determining the priority of the answers based on the time when the question was submitted, questions with higher urgency can be prioritized. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs question submission time data into the generation AI, which then determines the priority of the answers.

[0103] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. The order includes, but is not limited to, the relevance and importance of the questions. For example, the generation unit determines the order of answers based on the relevance of the questions. The generation unit can also prioritize answering questions with higher importance, taking into account the relevance of the questions. The generation unit can also gradually adjust the order of answers according to the relevance of the questions. For example, the generation unit determines the order of answers based on the relevance of the questions. The generation unit prioritizes answering questions with higher importance, taking into account the relevance of the questions. The generation unit gradually adjusts the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, questions with higher importance can be prioritized. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs question relevance data into the generation AI, and the generation AI adjusts the order of the answers.

[0104] When generating an answer, the generation unit can adjust the use of technical terminology in the answer depending on the newcomer's level of expertise. Technical terminology includes, but is not limited to, technical terms and industry jargon. For example, if the newcomer's level of expertise is low, the generation unit can generate an answer using simple language. Furthermore, if the newcomer's level of expertise is high, the generation unit can generate an answer using a lot of technical terminology. Furthermore, the generation unit can gradually adjust the use of technical terminology in the answer depending on the newcomer's level of expertise. For example, if the newcomer's level of expertise is low, the generation unit generates an answer using simple language. If the newcomer's level of expertise is high, the generation unit generates an answer using a lot of technical terminology. The use of technical terminology in the answer is gradually adjusted depending on the newcomer's level of expertise. In this way, by adjusting the use of technical terminology in the answer depending on the newcomer's level of expertise, a more understandable answer can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit inputs the newcomer's level of expertise data into the generation AI, which then adjusts the use of technical terminology in the answer.

[0105] The providing unit can estimate the new employee's emotions and adjust the answer providing method based on the estimated new employee's emotions. Examples of the providing method include, but are not limited to, text, audio, and image formats. For example, if the new employee is nervous, the providing unit can provide an audio answer in a calm voice. Furthermore, if the new employee is relaxed, the providing unit can provide a detailed text answer. Furthermore, if the new employee is impatient, the providing unit can provide a concise and easy-to-understand answer. For example, if the new employee is nervous, the providing unit can provide an audio answer in a calm voice. If the new employee is relaxed, the providing unit can provide a detailed text answer. If the new employee is impatient, the providing unit can provide a concise and easy-to-understand answer. This allows the answer providing method to be adjusted according to the new employee's emotions, thereby providing a more appropriate answer. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or without the generation AI. For example, the provision department inputs the new employee's emotional data into the generation AI, which then estimates the emotion and adjusts how the answer is provided.

[0106] When providing an answer, the providing unit can select a delivery method by referring to the newcomer's past question history. The question history includes, for example, past questions and their answers, the frequency of questions, etc., but is not limited to these examples. For example, the providing unit preferentially provides a delivery method (audio, text, etc.) that the newcomer has used in the past. The providing unit can also preferentially suggest a specific delivery method from the newcomer's past question history. The providing unit can also select an optimal delivery method based on the newcomer's past question history. For example, the providing unit preferentially provides a delivery method (audio, text, etc.) that the newcomer has used in the past. The providing unit preferentially suggests a specific delivery method from the newcomer's past question history. The optimal delivery method is selected based on the newcomer's past question history. In this way, the optimal delivery method can be selected by referring to the newcomer's past question history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without using, a generation AI. For example, the providing unit inputs the newcomer's question history data into the generation AI, which selects the optimal delivery method.

[0107] When providing an answer, the providing unit can customize the provided content according to the new employee's current task. Tasks include, but are not limited to, for example, a current project, work content, etc. The providing unit, for example, prioritizes providing answers related to the task the new employee is currently working on. The providing unit can also customize and provide related information according to the new employee's current task. The providing unit can also provide the optimal answer taking into account the new employee's current task. For example, the providing unit prioritizes providing answers related to the task the new employee is currently working on. The providing unit customizes and provides related information according to the new employee's current task. The providing unit provides the optimal answer taking into account the new employee's current task. In this way, by customizing the provided content according to the new employee's current task, a more relevant answer can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the new employee's task data into the generation AI, and the generation AI customizes the provided content.

[0108] The providing unit can improve the answer providing method by reflecting the newcomer's feedback when providing an answer. Examples of feedback include, but are not limited to, survey results and user comments. For example, when a newcomer provides feedback on an answer provided by the newcomer, the providing unit improves the answer providing method based on the feedback. The providing unit can also customize the answer providing method by reflecting the newcomer's feedback. The providing unit can also gradually improve the answer providing method based on the newcomer's feedback. For example, when a newcomer provides feedback on an answer provided by the newcomer, the providing unit improves the answer providing method based on the feedback. The providing unit customizes the answer providing method by reflecting the newcomer's feedback. The providing unit gradually improves the answer providing method based on the newcomer's feedback. In this way, the answer providing method can be improved by reflecting the newcomer's feedback, and more appropriate answers can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the newcomer's feedback data into the generation AI, which analyzes the feedback and improves the answer providing method.

[0109] The providing unit can estimate the newcomer's emotions and adjust the order in which answers are provided based on the estimated newcomer's emotions. Examples of the order include, but are not limited to, the relevance and importance of the questions. For example, if the newcomer is nervous, the providing unit can prioritize providing answers with lower importance. Furthermore, if the newcomer is relaxed, the providing unit can prioritize providing answers with higher importance. Furthermore, if the newcomer is impatient, the providing unit can prioritize providing answers with higher urgency. For example, if the newcomer is nervous, the providing unit can prioritize providing answers with lower importance. If the newcomer is relaxed, the providing unit can prioritize providing answers with higher importance. If the newcomer is impatient, the providing unit can prioritize providing answers with higher urgency. In this way, by adjusting the order in which answers are provided according to the newcomer's emotions, answers can be provided in a more appropriate order. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit inputs emotion data of newcomers to the generation AI, and the generation AI estimates the emotions and adjusts the order in which answers are provided.

[0110] When providing an answer, the providing unit can select the optimal presentation method by taking into account the newcomer's device information. Device information includes, but is not limited to, the device type, OS version, etc. For example, if the newcomer is using a smartphone, the providing unit can provide a presentation method tailored to the screen size. Furthermore, if the newcomer is using a tablet, the providing unit can also provide a presentation method optimized for a large screen. Furthermore, if the newcomer is using a desktop, the providing unit can also provide a presentation method including detailed information. For example, if the newcomer is using a smartphone, the providing unit can provide a presentation method tailored to the screen size. If the newcomer is using a tablet, the providing unit can provide a presentation method optimized for a large screen. If the newcomer is using a desktop, the providing unit can provide a presentation method including detailed information. This allows the optimal presentation method to be selected by taking into account the newcomer's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit inputs the newcomer's device information into the generation AI, which then selects the optimal presentation method.

[0111] When providing an answer, the providing unit can make the provided content multilingual according to the newcomer's language setting. Language settings include, for example, the user's language setting and the priority of the language used, but are not limited to these examples. The providing unit, for example, automatically sets the language of the answer based on the language setting of the newcomer's device. The providing unit can also provide a language switching function when the newcomer uses multiple languages. The providing unit can also provide the answer in a specific language when the newcomer selects that language. For example, the providing unit automatically sets the language of the answer based on the language setting of the newcomer's device. The providing unit can provide a language switching function when the newcomer uses multiple languages. If the newcomer selects a specific language, the answer is provided in that language. By making the provided content multilingual according to the newcomer's language setting, more understandable answers can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the newcomer's language setting data into the generation AI, which then generates a multilingual answer.

[0112] When providing an answer, the providing unit can customize the providing method according to the learning style of the new employee. Examples of learning styles include, but are not limited to, visual learning and auditory learning. For example, if the new employee has a visual learning style, the providing unit can provide an answer including diagrams and videos. Furthermore, if the new employee has an auditory learning style, the providing unit can provide an audio answer. Furthermore, if the new employee has a hands-on learning style, the providing unit can provide an answer including practical examples. For example, if the new employee has a visual learning style, the providing unit provides an answer including diagrams and videos. If the new employee has an auditory learning style, the providing unit provides an audio answer. If the new employee has a hands-on learning style, the providing unit provides an answer including practical examples. In this way, by customizing the providing method according to the new employee's learning style, more effective answers can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit inputs the new employee's learning style data into the generation AI, which selects the optimal providing method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a question from the new employee using the reception device 38 of the smart device 14. The analysis unit analyzes the question using the specific processing unit 290 of the data processing device 12. The generation unit generates an answer using the specific processing unit 290 of the data processing device 12. The provision unit provides the answer using the output device 40 of the smart device 14. The reception unit estimates the new employee's emotions using the camera 42 and microphone 38B of the smart device 14 and adjusts the timing of receiving the question. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a question from the newcomer using the microphone 238 of the smart glasses 214. The analysis unit analyzes the question using the specific processing unit 290 of the data processing device 12. The generation unit generates an answer using the specific processing unit 290 of the data processing device 12. The provision unit provides the answer using the speaker 240 of the smart glasses 214. The reception unit estimates the newcomer's emotions using the camera 42 and microphone 238 of the smart glasses 214 and adjusts the timing of receiving the question. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a question from a new employee using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the question using the specific processing unit 290 of the data processing device 12. The generation unit generates an answer using the specific processing unit 290 of the data processing device 12. The provision unit provides the answer using the speaker 240 of the headset type terminal 314. The reception unit estimates the new employee's emotions using the camera 42 and microphone 238 of the headset type terminal 314 and adjusts the timing of receiving the question. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a question from the new employee using the microphone 238 of the robot 414. The analysis unit analyzes the question using the specific processing unit 290 of the data processing device 12. The generation unit generates an answer using the specific processing unit 290 of the data processing device 12. The provision unit provides the answer using the speaker 240 of the robot 414. The reception unit estimates the new employee's emotions using the camera 42 and microphone 238 of the robot 414 and adjusts the timing of receiving the question.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] When accepting questions entered by new employees, the reception unit can classify the questions into appropriate categories based on their content. For example, the reception unit can classify the questions into technical questions, questions related to business processes, general questions, etc. This allows the analysis unit to select an appropriate analysis algorithm when analyzing the questions. Furthermore, when the provision unit provides answers, the answers can be provided in an appropriate format. For example, answers including detailed technical information can be provided for technical questions, and answers explaining the steps of the business processes can be provided for questions related to business processes. This allows new employees to quickly obtain more appropriate answers when asking questions.

[0115] The generator can generate an answer by referencing related external resources based on the content of the question. For example, it can refer to public information on the Internet, specialized books, industry best practices, etc. This allows the generator to provide an answer that contains more information. The generator can also evaluate the reliability of the answer based on the information obtained from the external resources and prioritize the use of reliable information. For example, by providing answers based on reliable specialized books and industry best practices, it is possible to ensure that newcomers can use the information with confidence.

[0116] When providing the generated answer, the providing unit can select the notification method based on the importance of the answer. For example, a push notification or an urgent email can be sent for an answer with a high importance, and a notification can be sent by regular email or in a chat window for an answer with a low importance. This allows new employees to respond quickly without missing important information. The providing unit can also adjust the frequency and timing of notifications depending on the importance of the answer. For example, an answer with a high importance can be notified immediately, and an answer with a low importance can be notified at regular time intervals.

[0117] When receiving a question entered by a new employee, the reception unit can convert the question into an appropriate format based on the content of the question. For example, it can convert a voice-based question into text format, and an image-based question into text format. This makes it easier for the analysis unit to analyze the question and generate a more accurate answer. The reception unit can also supplement necessary information when converting the question into an appropriate format based on the content of the question. For example, it can convert a voice-based question into text using speech recognition technology, and supplement the necessary information.

[0118] The analysis unit can perform analysis by referring to related past questions and answers based on the content of the question. For example, if a similar question has been asked in the past, by performing analysis by referring to those questions and answers, it is possible to generate a faster and more accurate answer. The analysis unit can also analyze question trends and patterns based on past questions and answers and make predictions about future questions. This allows new employees to quickly obtain more appropriate answers when asking questions.

[0119] When obtaining the latest progress information from the project management system, the generation unit can filter the information based on the priority of the project. For example, the generation unit can preferentially obtain the progress information of high-priority projects and generate an answer. The generation unit can also adjust the level of detail of the answer based on the progress of the project. For example, the generation unit can provide a concise answer if progress is going smoothly and a detailed answer if there are problems with the progress. This allows new employees to quickly understand the progress of the project and take appropriate action.

[0120] When providing manuals and videos on how to use the tool, the provision unit can customize the content to be provided based on the learning progress of the new employee. For example, if the new employee already understands basic usage, the provision unit can provide manuals and videos on advanced usage, and for beginners, the provision unit can provide manuals and videos on basic usage. The provision unit can also adjust the difficulty level of the content to be provided according to the learning progress of the new employee. For example, the provision unit can provide easy content to beginners and advanced content to advanced users.

[0121] When adding questions and answers asked by new employees to the company's knowledge base, the generation department can assign appropriate tags based on the question's category. For example, a technical question can be tagged as "Technical" and a question about business processes can be tagged as "Business Process." This allows other employees to quickly find relevant information when searching the knowledge base. Furthermore, when adding questions and answers, the generation department can also set links to other related questions and answers. This allows the information in the knowledge base to be organized more systematically and become easier to use.

[0122] The reception unit can estimate the new employee's emotions and customize the method for receiving questions based on the estimated new employee's emotions. For example, if the new employee is nervous, a message to encourage them to relax can be displayed, and questions can be received smoothly. Also, if the new employee is relaxed, questions can be received immediately and responded to quickly. Also, if the new employee is impatient, a message to encourage them to calm down can be displayed, and the method for receiving questions can be adjusted. In this way, by customizing the method for receiving questions according to the new employee's emotions, questions can be received at a more appropriate time.

[0123] The analysis unit can estimate the new employee's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the new employee is nervous, a simple and easy-to-understand presentation can be used. If the new employee is relaxed, detailed analysis results can be provided. If the new employee is impatient, analysis results that focus on the main points can be provided. By adjusting the way the analysis is presented according to the new employee's emotions, it is possible to provide analysis results that are easier to understand.

[0124] The generation unit can estimate the new employee's emotions and adjust the answer generation method based on the estimated new employee's emotions. For example, if the new employee is nervous, a simple and easy-to-understand answer can be generated. If the new employee is relaxed, a detailed answer can be generated. If the new employee is impatient, an answer that focuses on the main points can be generated. In this way, by adjusting the answer generation method according to the new employee's emotions, more appropriate answers can be provided.

[0125] The providing unit can estimate the newcomer's emotions and adjust the method of providing the answer based on the estimated newcomer's emotions. For example, if the newcomer is nervous, a voice answer can be provided in a calm voice. If the newcomer is relaxed, a detailed text answer can be provided. If the newcomer is impatient, a concise and easy-to-understand answer can be provided. In this way, by adjusting the method of providing the answer according to the newcomer's emotions, it is possible to provide an answer in a more appropriate manner.

[0126] The providing unit can estimate the new employee's emotions and adjust the order in which answers are provided based on the estimated new employee's emotions. For example, if the new employee is nervous, answers with lower importance can be provided preferentially. Also, if the new employee is relaxed, answers with higher importance can be provided preferentially. Also, if the new employee is impatient, answers with higher urgency can be provided preferentially. In this way, by adjusting the order in which answers are provided according to the new employee's emotions, answers can be provided in a more appropriate order.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The reception department accepts questions entered by new employees. Questions can be in text, audio, or image format. For example, text questions are accepted through an input form, audio questions are recorded through a microphone and converted into text using voice recognition technology, and image questions are captured using a camera and analyzed using image recognition technology. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing techniques, including morphological analysis, grammatical analysis, and semantic analysis. Morphological analysis divides the question into words and analyzes the meaning of each word. Grammatical analysis analyzes the grammatical structure of the question and understands the relationships between sentences. Semantic analysis understands the context of the question and extracts information to generate an appropriate answer. Step 3: The generator generates answers based on the questions analyzed by the analyzer. Natural language processing techniques, such as text generation AI (e.g., LLM) or multimodal generation AI, can be used. The generator can also obtain the latest progress information from the project management system and generate answers based on that information. Step 4: The providing unit provides the answer generated by the generating unit. The answer may be provided in text format, audio format, image format, etc. For example, a text format answer may be displayed through a chat window, an audio format answer may be played through a speaker, and an image format answer may be displayed on a screen.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] 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.

[0192] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for accepting questions; an analysis unit that analyzes the question received by the reception unit; a generation unit that generates an answer based on the question analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit; Equipped with A system characterized by:

2. The generation unit Uses natural language processing technology to understand the content of questions and generate answers 2. The system of claim 1.

3. The providing unit Provide generated answers to new recruits instantly 2. The system of claim 1.

4. The reception unit Accept questions entered by new employees 2. The system of claim 1.

5. The analysis unit Gather relevant information based on the question 2. The system of claim 1.

6. The generation unit Get progress updates from your project management system and generate answers 2. The system of claim 1.

7. The providing unit Provide manuals or videos on how to use the tool 2. The system of claim 1.

8. The generation unit Add new employee questions and answers to your internal knowledge base 2. The system of claim 1.

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