System

Abstract

An object of a system according to an embodiment is to provide a quick and appropriate answer or solution to an employee's question or problem.SOLUTION: A system includes a question reception unit, an analysis unit, and an answer generation unit. The question receiving unit receives a question or a task from an employee. The analysis unit analyzes the question and the problem received by the question reception unit. The answer generation unit generates an optimal answer or solution to the question or problem analyzed by the analysis 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] Conventional technology made it difficult to share knowledge between departments, making it difficult to respond quickly and appropriately to employee questions and issues.

[0005] The system according to the embodiment aims to provide prompt and appropriate answers and solutions to employees' questions and problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, an analysis unit, and an answer generating unit. The question receiving unit receives questions and issues from employees. The analysis unit analyzes the questions and issues received by the question receiving unit. The answer generating unit generates optimal answers and solutions to the questions and issues analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate answers and solutions to employees' questions and problems. [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 knowledge sharing system according to an embodiment of the present invention is a system that provides cross-sectional support for questions and issues that employees have. This system uses generative AI to receive questions and issues from employees and provide optimal answers and solutions. This allows the knowledge sharing system to improve employee productivity and contribute to unified output across the entire company.

[0029] A knowledge sharing system according to an embodiment includes a question receiving unit, an analysis unit, and an answer generating unit. The question receiving unit receives questions and issues from employees. For example, an employee inputs a question into the system, and the question receiving unit receives the question. The question receiving unit can also accept voice input and image input. The analysis unit analyzes the questions and issues received by the question receiving unit. For example, the analysis unit analyzes the content of the question using natural language processing technology and derives an appropriate answer. The analysis unit can also refer to an external database to incorporate the latest knowledge. The answer generating unit generates optimal answers and solutions to the questions and issues analyzed by the analysis unit. For example, the answer generating unit generates answers to questions using a generation AI. The answer generating unit can also generate answers that are appropriate to the context by referring to past question history and work history. This allows the knowledge sharing system according to an embodiment to provide quick and accurate answers to employees' questions and issues. For example, if an employee in the technical department asks a question about a new technical asset, the system can suggest the optimal technical asset. Similarly, if an employee in the planning department inputs an issue regarding a new marketing strategy, the system can suggest the optimal marketing strategy. This will improve employee productivity and contribute to the company's overall output.

[0030] The question receiving unit can generate answers that are more relevant to the context by referencing the questioner's past question history and work history. For example, when the generation AI receives a question, the question receiving unit references the questioner's past question history to understand the context based on the past question content and answers. For example, it references questions and answers previously asked by technical department employees to suggest more specific technical assets. The question receiving unit also references the questioner's work history to generate answers that take into account the questioner's current work content and project progress. For example, if an employee in the planning department asks a question related to an ongoing project, it will suggest the optimal marketing strategy based on the project's progress. The question receiving unit also integrates the questioner's past question history and work history to generate answers that are more relevant to the context. For example, it will suggest the optimal technical asset based on the technical department employee's past questions and current work content. This allows the system to provide more specific and contextual answers by referencing the questioner's past question history and work history.

[0031] When analyzing questions or issues, the analysis unit can refer to external databases and provide answers that incorporate the latest knowledge. For example, when analyzing questions or issues, the analysis unit has the generation AI refer to a patent database and generate answers based on the latest technical information. For example, in response to a question about a new technological asset, the optimal technological asset is suggested based on the latest patent information. The analysis unit also refers to an academic paper database and generates answers based on the latest research results. For example, in response to a question about a marketing strategy, the optimal strategy is suggested based on the latest marketing research results. The analysis unit also integrates external databases and generates answers that incorporate knowledge from patent information and academic papers. For example, in response to a question about a technological asset, the optimal technological asset is suggested based on patent information and knowledge from academic papers. In this way, by referring to external databases, answers that incorporate the latest knowledge can be provided.

[0032] The question receiving unit can accept voice input and image input and perform multimodal analysis. For example, when receiving a question or issue, the question receiving unit accepts voice input, converts it into text using voice recognition technology, and performs analysis. For example, an employee in the technical department inputs a question by voice, analyzes the voice data, and proposes the optimal technical asset. The question receiving unit also accepts image input, analyzes the image data using image recognition technology, and understands the content of the question or issue. For example, an employee in the planning department inputs a project drawing as an image, analyzes the image data, and proposes the optimal marketing strategy. The question receiving unit also integrates voice input and image input to perform multimodal analysis. For example, an employee in the technical department inputs a question by voice and inputs related drawings as images, and proposes more specific technical assets. In this way, multimodal analysis is possible by accepting voice input and image input.

[0033] The question receiving unit can link with internal chat tools and email systems to provide a wider variety of input methods. For example, the question receiving unit can link with internal chat tools to allow employees to input questions and issues in chat format. For example, an employee in the technical department can input a question using a chat tool, and the generation AI can analyze the content and provide an answer. The question receiving unit can also link with an email system to allow employees to send questions and issues via email. For example, an employee in the planning department can send an issue via email, and the generation AI can analyze the content and suggest the optimal support method. The question receiving unit can also integrate chat tools and email systems to allow employees to input questions and issues using either method. For example, an employee in the technical department can input a question using a chat tool, and an employee in the planning department can send an issue via email, and the generation AI can analyze both inputs and provide an answer. In this way, by linking with internal chat tools and email systems, a wider variety of input methods can be provided.

[0034] The analysis unit can automatically map the expertise of each department and identify employees with the most appropriate knowledge. In the analysis unit, for example, the generation AI automatically maps the expertise of each department and identifies employees with the most appropriate knowledge. For example, the generation AI maps the expertise of the technical department and identifies employees who can provide the most appropriate technical support for issues in the planning department. In addition, to promote knowledge sharing between departments, the generation AI automatically identifies employees with the appropriate knowledge and contacts them. For example, if an employee in the planning department needs technical support, the generation AI identifies the most appropriate employee in the technical department and contacts them. In addition, the analysis unit builds a system that maps the expertise of each department and identifies employees with the most appropriate knowledge. For example, the generation AI maps the expertise of the technical department and identifies employees who can provide the most appropriate technical support for issues in the planning department. In this way, by mapping the expertise of each department and identifying employees with the most appropriate knowledge, knowledge sharing between departments can be promoted.

[0035] The analysis department can automatically propose workshops and meetings and adjust schedules to promote knowledge sharing between departments. For example, the analysis department has the generative AI automatically propose workshops and meetings and adjust schedules to promote knowledge sharing between departments. For example, the generative AI proposes a workshop in which employees from the technical department and the planning department will participate and adjust the schedule. In addition, the analysis department has the generative AI automatically propose meetings and adjust schedules to promote knowledge sharing between departments. For example, if an employee from the planning department needs technical support, the generative AI proposes a meeting with an employee from the technical department and adjusts the schedule. In addition, the analysis department builds a system that automates the proposal and schedule adjustment of workshops and meetings. For example, the system proposes a workshop in which employees from the technical department and the planning department will participate and adjust the schedule. In this way, knowledge sharing between departments can be promoted by automatically proposing workshops and meetings and adjusting the schedule.

[0036] The analysis unit can automatically search for and share related documents and materials to promote knowledge sharing between departments. For example, the analysis unit has the generation AI automatically search for and share related documents and materials to promote knowledge sharing between departments. For example, it searches for and shares documents related to technical assets needed by employees in the technical department. In addition, the analysis unit has the generation AI automatically search for and share related materials to promote knowledge sharing between departments. For example, it searches for and shares materials related to marketing strategies needed by employees in the planning department. In addition, the analysis unit builds a system that automatically searches for and shares related documents and materials. For example, it searches for and shares documents related to technical assets needed by employees in the technical department. In this way, knowledge sharing between departments can be promoted by automatically searching for and sharing related documents and materials.

[0037] The analysis department can utilize internal SNS and forums to promote knowledge sharing between departments and encourage interaction between employees. For example, the analysis department can use the generation AI to promote knowledge sharing between departments and encourage interaction between employees. For example, employees from the technical department and the planning department share information and exchange opinions on the internal SNS. The analysis department can also use internal forums, with the generation AI automatically suggesting related topics to promote interaction between employees. For example, employees from the technical department hold discussions with employees from the planning department on the forum. The analysis department can also integrate internal SNS and forums, building a system where the generation AI automatically suggests related topics and promotes interaction between employees. For example, employees from the technical department and the planning department share information and exchange opinions on the internal SNS and forum. In this way, the use of internal SNS and forums can promote interaction between employees and promote knowledge sharing between departments.

[0038] The analysis unit can analyze employees' work patterns and propose optimal work schedules. In the analysis unit, for example, a generation AI analyzes employees' work patterns and proposes optimal work schedules. For example, it proposes a schedule that allows employees in the technical department to work efficiently. The analysis unit also builds a system that analyzes the work patterns of individual employees and proposes optimal work schedules. For example, it proposes a schedule that allows employees in the planning department to work efficiently. In the analysis unit, a generation AI analyzes employees' work patterns in real time and proposes optimal work schedules. For example, it proposes a schedule that allows employees in the technical department to work efficiently. In this way, by analyzing employees' work patterns and proposing optimal work schedules, employee productivity is improved.

[0039] The analysis unit can monitor the progress of employees' work in real time and send reminders and alerts as necessary. In the analysis unit, for example, the generation AI monitors the progress of employees' work in real time and sends reminders and alerts as necessary. For example, an employee in the technical department monitors the progress of a project and sends reminders. The analysis unit also builds a system that monitors the progress of work in real time and sends reminders and alerts as necessary. For example, an employee in the planning department monitors the progress of a project and sends reminders. The analysis unit also builds a system that monitors the progress of employees' work in real time and sends reminders and alerts as necessary. For example, an employee in the technical department monitors the progress of a project and sends reminders. In this way, employee productivity is improved by monitoring the progress of work in real time and sending reminders and alerts as necessary.

[0040] The analysis unit can work in conjunction with business automation tools to automate routine tasks. For example, the analysis unit's generative AI works in conjunction with business automation tools to automate routine tasks. For example, it automates routine data entry tasks performed by employees in the technical department. The analysis unit also works in conjunction with business automation tools to build a system that automates routine tasks. For example, it automates routine report creation tasks performed by employees in the planning department. The analysis unit also works in conjunction with business automation tools to build a system that automates routine tasks. For example, it automates routine data entry tasks performed by employees in the technical department. In this way, by working in conjunction with business automation tools to automate routine tasks, employee productivity is improved.

[0041] The analysis unit can propose optimal ways to use internal resources. For example, the analysis unit's generation AI proposes optimal ways to use internal resources (such as conference rooms and equipment). For example, it makes suggestions for employees in the technical department to use conference rooms efficiently. The analysis unit also builds a system that proposes optimal ways to use internal resources. For example, it makes suggestions for employees in the planning department to use equipment efficiently. The analysis unit's generation AI also proposes optimal ways to use internal resources in real time. For example, it makes suggestions for employees in the technical department to use conference rooms efficiently. In this way, by proposing optimal ways to use internal resources, employee productivity is improved.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The knowledge sharing system can also evaluate employees' skill levels and suggest appropriate training programs. For example, if an employee in the technical department asks a question about a new technology, the system can evaluate the employee's skill level and suggest an appropriate training program. Similarly, if an employee in the planning department asks a question about marketing strategy, the system can evaluate the employee's skill level and suggest the most appropriate marketing training program. Furthermore, the system can evaluate employees' skill levels and suggest training programs to promote knowledge sharing between departments. This can help improve employees' skills and increase productivity across the company.

[0044] The knowledge sharing system can also monitor employees' health and make suggestions for maintaining their health. For example, if an employee in the technical department is working at a desk for long hours, it can suggest appropriate breaks and stretching. If an employee in the planning department is feeling stressed, it can suggest relaxation methods or counseling. Furthermore, it is possible to build a system that monitors employees' health and makes specific suggestions for maintaining their health. This will help maintain employee health and improve productivity.

[0045] The knowledge sharing system can also analyze employees' career paths and propose optimal career plans. For example, if an employee in the technical department wishes to advance their career, the system can propose the optimal career plan based on that employee's skills and experience. Similarly, if an employee in the planning department wants to take on a new position, the system can analyze that employee's career path and propose the optimal career plan. Furthermore, the system can analyze employees' career paths and propose plans to promote career mobility between departments. This can support employees' career growth and improve productivity across the company.

[0046] Knowledge sharing systems can also support employee project management and monitor project progress in real time. For example, employees in the technical department can check the progress of a project and allocate the necessary resources appropriately. Employees in the planning department can also monitor the project progress and adjust the schedule. Furthermore, tools can be provided to support project management, and a system can be built to monitor project progress in real time. This can support efficient project management and improve productivity across the company.

[0047] The knowledge sharing system can also provide training programs to improve employees' communication skills. For example, a training program can be provided for employees in the technical department to learn effective presentation skills. Another training program can be provided for employees in the planning department to improve their team-building skills. Furthermore, training programs can be provided to improve employees' communication skills, facilitating smooth communication between departments. This can improve employees' communication skills and increase the productivity of the entire company.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The question receiving unit receives questions and assignments from employees. For example, an employee inputs a question into the system, and the question receiving unit receives the question. The question receiving unit can also accept voice input and image input. Step 2: The analysis unit analyzes the questions and issues received by the question receiving unit. For example, the analysis unit uses natural language processing technology to analyze the content of the question and derive an appropriate answer. The analysis unit can also refer to external databases to incorporate the latest knowledge. Step 3: The answer generation unit generates optimal answers and solutions to the questions and issues analyzed by the analysis unit. For example, the answer generation unit generates answers to questions using a generation AI. The answer generation unit can also generate answers that are appropriate to the context by referencing past question history and work history.

[0050] (Example 2) The knowledge sharing system according to an embodiment of the present invention is a system that provides cross-sectional support for questions and issues that employees have. This system uses generative AI to receive questions and issues from employees and provide optimal answers and solutions. This allows the knowledge sharing system to improve employee productivity and contribute to unified output across the entire company.

[0051] A knowledge sharing system according to an embodiment includes a question receiving unit, an analysis unit, and an answer generating unit. The question receiving unit receives questions and issues from employees. For example, an employee inputs a question into the system, and the question receiving unit receives the question. The question receiving unit can also accept voice input and image input. The analysis unit analyzes the questions and issues received by the question receiving unit. For example, the analysis unit analyzes the content of the question using natural language processing technology and derives an appropriate answer. The analysis unit can also refer to an external database to incorporate the latest knowledge. The answer generating unit generates optimal answers and solutions to the questions and issues analyzed by the analysis unit. For example, the answer generating unit generates answers to questions using a generation AI. The answer generating unit can also generate answers that are appropriate to the context by referring to past question history and work history. This allows the knowledge sharing system according to an embodiment to provide quick and accurate answers to employees' questions and issues. For example, if an employee in the technical department asks a question about a new technical asset, the system can suggest the optimal technical asset. Similarly, if an employee in the planning department inputs an issue regarding a new marketing strategy, the system can suggest the optimal marketing strategy. This will improve employee productivity and contribute to the company's overall output.

[0052] The question receiving unit can generate answers that are more relevant to the context by referencing the questioner's past question history and work history. For example, when the generation AI receives a question, the question receiving unit references the questioner's past question history to understand the context based on the past question content and answers. For example, it references questions and answers previously asked by technical department employees to suggest more specific technical assets. The question receiving unit also references the questioner's work history to generate answers that take into account the questioner's current work content and project progress. For example, if an employee in the planning department asks a question related to an ongoing project, it will suggest the optimal marketing strategy based on the project's progress. The question receiving unit also integrates the questioner's past question history and work history to generate answers that are more relevant to the context. For example, it will suggest the optimal technical asset based on the technical department employee's past questions and current work content. This allows the system to provide more specific and contextual answers by referencing the questioner's past question history and work history.

[0053] When analyzing questions or issues, the analysis unit can refer to external databases and provide answers that incorporate the latest knowledge. For example, when analyzing questions or issues, the analysis unit has the generation AI refer to a patent database and generate answers based on the latest technical information. For example, in response to a question about a new technological asset, the optimal technological asset is suggested based on the latest patent information. The analysis unit also refers to an academic paper database and generates answers based on the latest research results. For example, in response to a question about a marketing strategy, the optimal strategy is suggested based on the latest marketing research results. The analysis unit also integrates external databases and generates answers that incorporate knowledge from patent information and academic papers. For example, in response to a question about a technological asset, the optimal technological asset is suggested based on patent information and knowledge from academic papers. In this way, by referring to external databases, answers that incorporate the latest knowledge can be provided.

[0054] The analysis unit can analyze the emotional state of the questioner and generate an answer that reduces stress and anxiety. The analysis unit, for example, uses an emotion estimation function to analyze the emotional state of the questioner in real time and generate an answer that reduces stress and anxiety. For example, if the questioner is feeling anxious, an answer that gives a sense of security is provided. The analysis unit also analyzes the emotional state of the questioner and generates an answer that elicits positive emotions. For example, if the questioner is feeling stressed, words of encouragement or success stories are presented. The analysis unit also uses the emotion estimation function to generate an answer according to the questioner's emotional state. For example, if the questioner is nervous, an answer that relaxes the questioner is provided. In this way, by analyzing the emotional state of the questioner, an answer that reduces stress and anxiety can be provided.

[0055] The question receiving unit can accept voice input and image input and perform multimodal analysis. For example, when receiving a question or issue, the question receiving unit accepts voice input, converts it into text using voice recognition technology, and performs analysis. For example, an employee in the technical department inputs a question by voice, analyzes the voice data, and proposes the optimal technical asset. The question receiving unit also accepts image input, analyzes the image data using image recognition technology, and understands the content of the question or issue. For example, an employee in the planning department inputs a project drawing as an image, analyzes the image data, and proposes the optimal marketing strategy. The question receiving unit also integrates voice input and image input to perform multimodal analysis. For example, an employee in the technical department inputs a question by voice and inputs related drawings as images, and proposes more specific technical assets. In this way, multimodal analysis is possible by accepting voice input and image input.

[0056] The question receiving unit can link with internal chat tools and email systems to provide a wider variety of input methods. For example, the question receiving unit can link with internal chat tools to allow employees to input questions and issues in chat format. For example, an employee in the technical department can input a question using a chat tool, and the generation AI can analyze the content and provide an answer. The question receiving unit can also link with an email system to allow employees to send questions and issues via email. For example, an employee in the planning department can send an issue via email, and the generation AI can analyze the content and suggest the optimal support method. The question receiving unit can also integrate chat tools and email systems to allow employees to input questions and issues using either method. For example, an employee in the technical department can input a question using a chat tool, and an employee in the planning department can send an issue via email, and the generation AI can analyze both inputs and provide an answer. In this way, by linking with internal chat tools and email systems, a wider variety of input methods can be provided.

[0057] The question receiving unit can analyze the emotions of the questioner when he or she enters information in real time, and provide an interface that draws out positive emotions. The question receiving unit, for example, uses an emotion estimation function to analyze the emotions of the questioner when he or she enters information in real time, and provide an interface that draws out positive emotions. For example, if the questioner is feeling anxious, an interface that gives a sense of security is displayed. The question receiving unit also analyzes the questioner's emotional state, and provides an interface that draws out positive emotions. For example, if the questioner is feeling stressed, an encouraging message or a success story is displayed. The question receiving unit also uses the emotion estimation function to provide an interface that corresponds to the questioner's emotional state. For example, if the questioner is nervous, an interface that helps the questioner relax is displayed. In this way, it is possible to provide an interface that analyzes the questioner's emotions in real time, and draws out positive emotions.

[0058] The analysis unit can automatically map the expertise of each department and identify employees with the most appropriate knowledge. In the analysis unit, for example, the generation AI automatically maps the expertise of each department and identifies employees with the most appropriate knowledge. For example, the generation AI maps the expertise of the technical department and identifies employees who can provide the most appropriate technical support for issues in the planning department. In addition, to promote knowledge sharing between departments, the generation AI automatically identifies employees with the appropriate knowledge and contacts them. For example, if an employee in the planning department needs technical support, the generation AI identifies the most appropriate employee in the technical department and contacts them. In addition, the analysis unit builds a system that maps the expertise of each department and identifies employees with the most appropriate knowledge. For example, the generation AI maps the expertise of the technical department and identifies employees who can provide the most appropriate technical support for issues in the planning department. In this way, by mapping the expertise of each department and identifying employees with the most appropriate knowledge, knowledge sharing between departments can be promoted.

[0059] The analysis department can automatically propose workshops and meetings and adjust schedules to promote knowledge sharing between departments. For example, the analysis department has the generative AI automatically propose workshops and meetings and adjust schedules to promote knowledge sharing between departments. For example, the generative AI proposes a workshop in which employees from the technical department and the planning department will participate and adjust the schedule. In addition, the analysis department has the generative AI automatically propose meetings and adjust schedules to promote knowledge sharing between departments. For example, if an employee from the planning department needs technical support, the generative AI proposes a meeting with an employee from the technical department and adjusts the schedule. In addition, the analysis department builds a system that automates the proposal and schedule adjustment of workshops and meetings. For example, the system proposes a workshop in which employees from the technical department and the planning department will participate and adjust the schedule. In this way, knowledge sharing between departments can be promoted by automatically proposing workshops and meetings and adjusting the schedule.

[0060] The analysis unit can use the emotion estimation function to make suggestions for removing emotional barriers during inter-departmental communication. The analysis unit, for example, uses the emotion estimation function to make suggestions for removing emotional barriers during inter-departmental communication. For example, the analysis unit makes suggestions for reducing emotional barriers when employees from the technical department and the planning department communicate. The analysis unit also uses the emotion estimation function to make specific suggestions for removing emotional barriers during inter-departmental communication. For example, the analysis unit makes suggestions for reducing emotional barriers when employees from the technical department communicate with employees from the planning department. The analysis unit also uses the emotion estimation function to build a system for removing emotional barriers during inter-departmental communication. For example, the analysis unit makes suggestions for reducing emotional barriers when employees from the technical department and the planning department communicate. In this way, using the emotion estimation function to make suggestions for removing emotional barriers facilitates communication between departments.

[0061] The analysis unit can automatically search for and share related documents and materials to promote knowledge sharing between departments. For example, the analysis unit has the generation AI automatically search for and share related documents and materials to promote knowledge sharing between departments. For example, it searches for and shares documents related to technical assets needed by employees in the technical department. In addition, the analysis unit has the generation AI automatically search for and share related materials to promote knowledge sharing between departments. For example, it searches for and shares materials related to marketing strategies needed by employees in the planning department. In addition, the analysis unit builds a system that automatically searches for and shares related documents and materials. For example, it searches for and shares documents related to technical assets needed by employees in the technical department. In this way, knowledge sharing between departments can be promoted by automatically searching for and sharing related documents and materials.

[0062] The analysis department can utilize internal SNS and forums to promote knowledge sharing between departments and encourage interaction between employees. For example, the analysis department can use the generation AI to promote knowledge sharing between departments and encourage interaction between employees. For example, employees from the technical department and the planning department share information and exchange opinions on the internal SNS. The analysis department can also use internal forums, with the generation AI automatically suggesting related topics to promote interaction between employees. For example, employees from the technical department hold discussions with employees from the planning department on the forum. The analysis department can also integrate internal SNS and forums, building a system where the generation AI automatically suggests related topics and promotes interaction between employees. For example, employees from the technical department and the planning department share information and exchange opinions on the internal SNS and forum. In this way, the use of internal SNS and forums can promote interaction between employees and promote knowledge sharing between departments.

[0063] The analysis unit can use the emotion estimation function to propose a communication method that elicits positive emotions when sharing knowledge between departments. The analysis unit, for example, uses the emotion estimation function to propose a communication method that elicits positive emotions when sharing knowledge between departments. For example, the analysis unit makes a proposal for eliciting positive emotions when employees from the technical department and the planning department communicate. The analysis unit also uses the emotion estimation function to propose a specific communication method that elicits positive emotions when sharing knowledge between departments. For example, the analysis unit makes a proposal for eliciting positive emotions when employees from the technical department communicate with employees from the planning department. The analysis unit also uses the emotion estimation function to build a system that elicits positive emotions when sharing knowledge between departments. For example, the analysis unit makes a proposal for eliciting positive emotions when employees from the technical department and the planning department communicate. In this way, by using the emotion estimation function to propose a communication method that elicits positive emotions, knowledge sharing between departments is made smoother.

[0064] The analysis unit can analyze employees' work patterns and propose optimal work schedules. In the analysis unit, for example, a generation AI analyzes employees' work patterns and proposes optimal work schedules. For example, it proposes a schedule that allows employees in the technical department to work efficiently. The analysis unit also builds a system that analyzes the work patterns of individual employees and proposes optimal work schedules. For example, it proposes a schedule that allows employees in the planning department to work efficiently. In the analysis unit, a generation AI analyzes employees' work patterns in real time and proposes optimal work schedules. For example, it proposes a schedule that allows employees in the technical department to work efficiently. In this way, by analyzing employees' work patterns and proposing optimal work schedules, employee productivity is improved.

[0065] The analysis unit can monitor the progress of employees' work in real time and send reminders and alerts as necessary. In the analysis unit, for example, the generation AI monitors the progress of employees' work in real time and sends reminders and alerts as necessary. For example, an employee in the technical department monitors the progress of a project and sends reminders. The analysis unit also builds a system that monitors the progress of work in real time and sends reminders and alerts as necessary. For example, an employee in the planning department monitors the progress of a project and sends reminders. The analysis unit also builds a system that monitors the progress of employees' work in real time and sends reminders and alerts as necessary. For example, an employee in the technical department monitors the progress of a project and sends reminders. In this way, employee productivity is improved by monitoring the progress of work in real time and sending reminders and alerts as necessary.

[0066] The analysis unit can use the emotion estimation function to analyze the stress levels of employees and make suggestions to reduce stress. The analysis unit, for example, uses the emotion estimation function to analyze the stress levels of employees and make suggestions to reduce stress. For example, if an employee in the technical department is feeling stressed, the analysis unit makes suggestions to relax. The analysis unit also builds a system that analyzes the stress levels of employees and makes specific suggestions to reduce stress. For example, if an employee in the planning department is feeling stressed, the analysis unit makes suggestions to relax. The analysis unit also uses the emotion estimation function to analyze the stress levels of employees in real time and make suggestions to reduce stress. For example, if an employee in the technical department is feeling stressed, the analysis unit makes suggestions to relax. In this way, by using the emotion estimation function to analyze the stress levels of employees and making suggestions to reduce stress, employee productivity is improved.

[0067] The analysis unit can work in conjunction with business automation tools to automate routine tasks. For example, the analysis unit's generative AI works in conjunction with business automation tools to automate routine tasks. For example, it automates routine data entry tasks performed by employees in the technical department. The analysis unit also works in conjunction with business automation tools to build a system that automates routine tasks. For example, it automates routine report creation tasks performed by employees in the planning department. The analysis unit also works in conjunction with business automation tools to build a system that automates routine tasks. For example, it automates routine data entry tasks performed by employees in the technical department. In this way, by working in conjunction with business automation tools to automate routine tasks, employee productivity is improved.

[0068] The analysis unit can propose optimal ways to use internal resources. For example, the analysis unit's generation AI proposes optimal ways to use internal resources (such as conference rooms and equipment). For example, it makes suggestions for employees in the technical department to use conference rooms efficiently. The analysis unit also builds a system that proposes optimal ways to use internal resources. For example, it makes suggestions for employees in the planning department to use equipment efficiently. The analysis unit's generation AI also proposes optimal ways to use internal resources in real time. For example, it makes suggestions for employees in the technical department to use conference rooms efficiently. In this way, by proposing optimal ways to use internal resources, employee productivity is improved.

[0069] The analysis unit can use the emotion estimation function to propose feedback and reward systems to improve employee motivation. The analysis unit, for example, uses the emotion estimation function to propose feedback and reward systems to improve employee motivation. For example, it provides feedback to employees in a technical department to maintain high motivation. The analysis unit also builds a system that proposes specific feedback and reward systems to improve employee motivation. For example, it proposes a reward system to help employees in a planning department maintain high motivation. The analysis unit also uses the emotion estimation function to analyze employee motivation in real time and propose feedback and reward systems to improve motivation. For example, it provides feedback to help employees in a technical department maintain high motivation. In this way, by using the emotion estimation function to propose feedback and reward systems to improve employee motivation, employee productivity is improved.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The knowledge sharing system can also evaluate employees' skill levels and suggest appropriate training programs. For example, if an employee in the technical department asks a question about a new technology, the system can evaluate the employee's skill level and suggest an appropriate training program. Similarly, if an employee in the planning department asks a question about marketing strategy, the system can evaluate the employee's skill level and suggest the most appropriate marketing training program. Furthermore, the system can evaluate employees' skill levels and suggest training programs to promote knowledge sharing between departments. This can help improve employees' skills and increase productivity across the company.

[0072] The knowledge sharing system can also monitor employees' health and make suggestions for maintaining their health. For example, if an employee in the technical department is working at a desk for long hours, it can suggest appropriate breaks and stretching. If an employee in the planning department is feeling stressed, it can suggest relaxation methods or counseling. Furthermore, it is possible to build a system that monitors employees' health and makes specific suggestions for maintaining their health. This will help maintain employee health and improve productivity.

[0073] The knowledge sharing system can also analyze employees' career paths and propose optimal career plans. For example, if an employee in the technical department wishes to advance their career, the system can propose the optimal career plan based on that employee's skills and experience. Similarly, if an employee in the planning department wants to take on a new position, the system can analyze that employee's career path and propose the optimal career plan. Furthermore, the system can analyze employees' career paths and propose plans to promote career mobility between departments. This can support employees' career growth and improve productivity across the company.

[0074] Knowledge sharing systems can also support employee project management and monitor project progress in real time. For example, employees in the technical department can check the progress of a project and allocate the necessary resources appropriately. Employees in the planning department can also monitor the project progress and adjust the schedule. Furthermore, tools can be provided to support project management, and a system can be built to monitor project progress in real time. This can support efficient project management and improve productivity across the company.

[0075] The knowledge sharing system can also provide training programs to improve employees' communication skills. For example, a training program can be provided for employees in the technical department to learn effective presentation skills. Another training program can be provided for employees in the planning department to improve their team-building skills. Furthermore, training programs can be provided to improve employees' communication skills, facilitating smooth communication between departments. This can improve employees' communication skills and increase the productivity of the entire company.

[0076] The knowledge sharing system can also analyze the emotional state of employees and provide feedback based on their emotions. For example, if an employee in the engineering department is feeling stressed, feedback based on that emotion can be provided to reduce stress. Similarly, if an employee in the planning department is feeling anxious, feedback based on that emotion can be provided to give them a sense of security. Furthermore, a system can be built that analyzes the emotional state of employees and provides feedback based on their emotions. This allows for feedback that takes into account the emotional state of employees, thereby improving employee productivity.

[0077] The knowledge sharing system can also analyze the emotional state of employees and suggest communication methods based on their emotions. For example, if an employee in the technical department is nervous, it can suggest a communication method based on their emotions to help them relax. Similarly, if an employee in the planning department is feeling stressed, it can suggest a communication method based on their emotions to reduce stress. Furthermore, it is possible to build a system that analyzes the emotional state of employees and suggests communication methods based on their emotions. This makes it possible to suggest communication methods that take employees' emotional states into account, thereby improving employee productivity.

[0078] The knowledge sharing system can also analyze the emotional state of employees and suggest motivation-boosting measures based on their emotions. For example, if employees in the technical department are feeling unmotivated, it can suggest motivation-boosting measures based on their emotions. Similarly, if employees in the planning department are feeling fatigued, it can suggest motivation-boosting measures based on their emotions. Furthermore, it is possible to build a system that analyzes employees' emotional state and suggests motivation-boosting measures based on their emotions. This makes it possible to suggest motivation-boosting measures that take employees' emotional states into account, thereby improving employee productivity.

[0079] The knowledge sharing system can also analyze employees' emotional states and suggest stress management measures based on their emotions. For example, if an employee in the technical department is feeling high levels of stress, stress management measures based on those emotions can be suggested. Similarly, if an employee in the planning department is feeling anxious, stress management measures based on those emotions can be suggested. Furthermore, it is possible to build a system that analyzes employees' emotional states and suggests stress management measures based on those emotions. This makes it possible to suggest stress management measures that take employees' emotional states into account, thereby improving employee productivity.

[0080] The knowledge sharing system can also analyze the emotional state of employees and provide feedback based on their emotions. For example, if an employee in the engineering department is feeling stressed, feedback based on that emotion can be provided to reduce stress. Similarly, if an employee in the planning department is feeling anxious, feedback based on that emotion can be provided to give them a sense of security. Furthermore, a system can be built that analyzes the emotional state of employees and provides feedback based on their emotions. This allows for feedback that takes into account the emotional state of employees, thereby improving employee productivity.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The question receiving unit receives questions and assignments from employees. For example, an employee inputs a question into the system, and the question receiving unit receives the question. The question receiving unit can also accept voice input and image input. Step 2: The analysis unit analyzes the questions and issues received by the question receiving unit. For example, the analysis unit uses natural language processing technology to analyze the content of the question and derive an appropriate answer. The analysis unit can also refer to external databases to incorporate the latest knowledge. Step 3: The answer generation unit generates optimal answers and solutions to the questions and issues analyzed by the analysis unit. For example, the answer generation unit generates answers to questions using a generation AI. The answer generation unit can also generate answers that are appropriate to the context by referencing past question history and work history.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 robot 414 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A question receiving department that receives questions and issues from employees, an analysis unit that analyzes the questions and tasks received by the question receiving unit; an answer generation unit that generates optimal answers or solutions to the questions or problems analyzed by the analysis unit; A system characterized by:

2. The question receiving unit Referencing the questioner's past question history and work history to generate more contextual answers 2. The system of claim 1.

3. The analysis unit When analyzing the questions and issues, external databases are referenced to provide answers that incorporate the latest knowledge.

2. The system of claim 1.

4. The analysis unit Analyze the questioner's emotional state and generate answers that reduce stress and anxiety 2. The system of claim 1.

5. The question receiving unit Accepts voice and image inputs and performs multimodal analysis 2. The system of claim 1.

Images