System

Abstract

An object of a system according to an embodiment is to significantly reduce the number of man-hours for business takeover and improve the speed of switching to a new business.SOLUTION: A system includes a recording part, an analysis part, a generation part, and a provision part. The recording part records business contents. The analysis unit analyzes the information recorded by the recording unit. The generation module is configured to generate transfer information based on the information analyzed by the analysis module. The providing unit provides the information generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that it took a lot of time and effort to transfer work duties when personnel were transferred or changed jobs.

[0005] The system according to the embodiment aims to significantly reduce the man-hours required for task handover and improve the speed of switching to a new task. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, a generation unit, and a provision unit. The recording unit records the business content. The analysis unit analyzes the information recorded by the recording unit. The generation unit generates handover information based on the information analyzed by the analysis unit. The provision unit provides the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can significantly reduce the man-hours required for task handover and improve the speed at which tasks are switched over to new tasks. [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) A system according to an embodiment of the present invention significantly reduces the time required for job handovers and improves the speed of transition to new tasks during personnel transfers or job changes. This system records the details of the tasks currently performed by employees, and a generation AI analyzes this information to propose the optimal handover method for new personnel. For example, the generation AI generates documents that explain step-by-step tasks and provides videos on how to use related tools. The generation AI also provides necessary training materials and FAQs to help new personnel start their tasks smoothly. Furthermore, the generation AI continues to support new personnel even after the handover. This not only significantly reduces the time required for handovers but also improves the speed of transition to new tasks. This system significantly reduces the time required for job handovers and improves the speed of transition to new tasks. For example, employees record the details of their current tasks, and the generation AI analyzes this information to propose the optimal handover method for new personnel, significantly reducing the time required for handovers. The generation AI also generates documents that explain step-by-step tasks and provides videos on how to use related tools, helping new personnel start their tasks smoothly. Furthermore, the generative AI will provide necessary training materials and FAQs, and continue to support new personnel even after the handover, thereby improving the speed at which they can switch to new tasks.

[0029] A business handover support system according to an embodiment includes a recording unit, an analysis unit, a generation unit, and a provision unit. The recording unit records details of the business operations currently performed by employees. For example, the recording unit can record information such as business procedures, tools used, and related documents. The recording unit can also automatically record business operations using a generation AI. The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can analyze business operations using a generation AI. The analysis unit can analyze business operations using techniques such as data mining, statistical analysis, and machine learning. The generation unit generates handover information based on the information analyzed by the analysis unit. For example, the generation unit can generate a document that explains business procedures step by step using a generation AI. The generation unit can also provide video instructions on how to use related tools. The provision unit provides the information generated by the generation unit. For example, the provision unit can provide necessary training materials and FAQs to help new personnel smoothly start their work. The provision unit can also continue to support new personnel even after the handover. As a result, the business task handover support system according to the embodiment can significantly reduce the number of steps required for business task handover and improve the speed of switching to a new business task.

[0030] The recording unit can record information about business procedures, tools used, and related documents. For example, the recording unit can record business procedures step by step. The recording unit can also record information about tools used. For example, the recording unit can record how to use software tools or hardware tools. The recording unit can also record information about related documents. For example, the recording unit can record information such as manuals, guidelines, and specifications. This enables the recording unit to record detailed business content. Some or all of the above-mentioned processing in the recording unit may be performed using or without the generation AI. For example, the recording unit can input business procedures into the generation AI, which then automatically records them.

[0031] The generation unit can generate a document that explains a business procedure step by step. For example, the generation unit can generate a document that explains a business procedure step by step. The generation unit can also automatically document the business procedure using a generation AI. For example, the generation unit can input the business procedure into the generation AI, which can then automatically generate a step-by-step procedure manual. The generation unit can also use the generation AI to generate a document that explains the business procedure in a tutorial format. This allows the generation unit to enable new personnel to start their work smoothly. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0032] The generation unit can use a video to explain how to use a specific tool. For example, the generation unit can use a video to explain how to use a specific tool. The generation unit can also use a generation AI to automatically create a video on how to use the tool. For example, the generation unit can input how to use the tool into the generation AI, which can then automatically generate a screencast or demonstration video. The generation unit can also use the generation AI to generate a video that visually explains how to use the tool. This makes it easier for new personnel to understand how to use the tool. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0033] The providing unit can provide necessary training materials and FAQs so that the new person in charge can smoothly start their work. For example, the providing unit can provide necessary training materials and FAQs so that the new person in charge can smoothly start their work. The providing unit can also automatically generate training materials and FAQs using a generation AI. For example, the providing unit can input the contents of the training materials and FAQs into the generation AI, which can then automatically generate presentation materials, training manuals, and frequently asked questions and their answers. The providing unit can also generate troubleshooting guides using the generation AI. This allows the providing unit to enable the new person in charge to quickly obtain the necessary information. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI.

[0034] The provision unit can continue to support the new person in charge even after the handover. The provision unit can, for example, continue to support the new person in charge even after the handover. The provision unit can also automatically provide support using a generation AI. For example, the provision unit can input support content into the generation AI, which can automatically provide periodic follow-up and help desk services. The provision unit can also automatically generate answers to questions from the new person in charge using the generation AI. This allows the provision unit to ensure that the new person in charge can receive support even after the handover. Some or all of the above-mentioned processing in the provision unit may be performed using the generation AI, or may be performed without using the generation AI.

[0035] When recording work content, the recording unit can select an appropriate recording format by referring to the employee's past work history. For example, the recording unit can automatically suggest a recording format that the employee has used in the past. The recording unit can also select the most efficient recording format from the employee's past work history. The recording unit can also analyze the employee's past work history and suggest a recording format suitable for a specific task. This allows the recording unit to provide the optimal recording format based on the employee's past work history. Some or all of the above-mentioned processing in the recording unit may be performed using or without using a generation AI.

[0036] When recording the content of a task, the recording unit can adjust the level of detail of the recording depending on the importance of the task. For example, the recording unit can request detailed recording for tasks with high importance. The recording unit can also allow simplified recording for tasks with low importance. The recording unit can also automatically adjust the level of detail of the recording depending on the importance of the task. This allows the recording unit to provide an optimal record depending on the importance of the task. Some or all of the above-described processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0037] When recording work content, the recording unit can select an appropriate recording means depending on the employee's input method. For example, if the employee uses voice input, the recording unit can record the work content using voice recognition technology. Furthermore, if the employee uses text input, the recording unit can provide a text editor to record the work content. Furthermore, if the employee uses image input, the recording unit can record the work content using image recognition technology. This allows the recording unit to provide the optimal recording means depending on the employee's input method. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0038] When recording work content, the recording unit can prioritize recording highly relevant work based on the employee's geographic location information. For example, if the employee is in a specific location, the recording unit can prioritize recording work related to that location. The recording unit can also automatically suggest highly relevant work based on the employee's geographic location information. The recording unit can also analyze the employee's geographic location information and suggest the optimal recording method. This allows the recording unit to provide optimal work records based on the employee's geographic location information. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0039] The recording unit can analyze the employee's social media activity and record specific work content when recording work content. The recording unit can, for example, analyze the employee's social media posts and record related work content. The recording unit can also suggest an optimal recording method based on the employee's social media activity history. The recording unit can also record related work content by referring to the activity of the employee's friends on social media. In this way, the recording unit can provide optimal work records based on the employee's social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0040] When recording work content, the recording unit can customize the recording method by reflecting the employee's past feedback. The recording unit can customize the recording method based on, for example, feedback provided by the employee in the past. The recording unit can also analyze the employee's past feedback and propose an optimal recording method. The recording unit can also reflect the employee's feedback and continuously improve the recording method. In this way, the recording unit can provide an optimal recording method based on the employee's past feedback. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task content. For example, the analysis unit can perform a detailed analysis for a task with a high level of importance. The analysis unit can also perform a simplified analysis for a task with a low level of importance. The analysis unit can also automatically adjust the level of detail of the analysis according to the importance of the task. This allows the analysis unit to provide an optimal analysis according to the importance of the task content. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0042] The analysis unit can apply different analysis algorithms depending on the business category during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the business category. The analysis unit can also apply different analysis algorithms to each business category. The analysis unit can also automatically switch the analysis algorithm based on the business category. This allows the analysis unit to provide the optimal analysis depending on the business category. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on the employee's past analysis results. The analysis unit can also analyze the employee's past analysis results and propose an optimal analysis method. The analysis unit can also improve the analysis algorithm by reflecting the employee's past analysis results. This allows the analysis unit to provide an optimal analysis based on the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the work content. For example, the analysis unit can prioritize analysis of work with an upcoming submission deadline. The analysis unit can also postpone analysis of work with a distant submission deadline. The analysis unit can also automatically determine the priority of analysis based on the submission time of the work content. This allows the analysis unit to provide an optimal analysis based on the submission time of the work content. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the work content during analysis. For example, the analysis unit can prioritize analysis of highly relevant work. The analysis unit can also postpone analysis of less relevant work. The analysis unit can also automatically adjust the order of analysis based on the relevance of the work content. This allows the analysis unit to provide an optimal analysis order based on the relevance of the work content. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with a low level of expertise. The analysis unit can also automatically adjust the use of technical terminology in the analysis results based on the employee's level of expertise. This allows the analysis unit to provide optimal analysis results according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0047] The generation unit can adjust the level of detail of the generated document based on the importance of the business content when generating the document. For example, the generation unit can generate a detailed document for a business of high importance. The generation unit can also generate a simplified document for a business of low importance. The generation unit can also automatically adjust the level of detail of the generated document according to the importance of the business. This allows the generation unit to provide optimal document generation according to the importance of the business content. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0048] The generation unit can apply different generation algorithms depending on the business category when generating a document. The generation unit can select the optimal generation algorithm depending on the business category, for example. The generation unit can also apply different generation algorithms to each business category. The generation unit can also automatically switch the generation algorithm based on the business category. This allows the generation unit to provide optimal document generation depending on the business category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0049] When generating a document, the generation unit can improve the accuracy of generation by referring to the employee's past generation results. The generation unit can improve the accuracy of generation, for example, based on the employee's past generation results. The generation unit can also analyze the employee's past generation results and propose an optimal generation method. The generation unit can also improve the generation algorithm by reflecting the employee's past generation results. In this way, the generation unit can provide optimal document generation based on the employee's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0050] The generation unit can determine the generation priority when generating documents based on the submission time of the work content. For example, the generation unit can prioritize document generation for work with an upcoming submission deadline. The generation unit can also postpone document generation for work with a distant submission deadline. The generation unit can also automatically determine the generation priority based on the submission time of the work content. This allows the generation unit to provide optimal document generation based on the submission time of the work content. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0051] The generation unit can adjust the order of generation based on the relevance of the work content when generating documents. For example, the generation unit can prioritize documenting highly relevant work. The generation unit can also document less relevant work later. The generation unit can also automatically adjust the order of generation based on the relevance of the work content. This allows the generation unit to provide an optimal document generation order based on the relevance of the work content. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0052] When generating a document, the generation unit can adjust the use of technical terminology in the generation according to the employee's level of expertise. For example, the generation unit can generate a document that uses a lot of technical terminology for an employee with a high level of expertise. The generation unit can also generate a document that avoids technical terminology for an employee with a low level of expertise. The generation unit can also automatically adjust the use of technical terminology in the generated document based on the employee's level of expertise. This allows the generation unit to generate an optimal document according to the employee's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0053] The providing unit can adjust the level of detail to be provided based on the importance of the work content when providing information. For example, the providing unit can provide detailed information for work of high importance. The providing unit can also provide simplified information for work of low importance. The providing unit can also automatically adjust the level of detail to be provided according to the importance of the work. This allows the providing unit to provide optimal information according to the importance of the work content. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0054] The providing unit can apply different providing algorithms depending on the business category when providing information. The providing unit can select the optimal providing algorithm depending on the business category, for example. The providing unit can also apply different providing algorithms for each business category. The providing unit can also automatically switch the providing algorithm based on the business category. This allows the providing unit to provide optimal information depending on the business category. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0055] When providing information, the providing unit can improve the accuracy of the information provided by referring to the employee's past provision results. The providing unit can improve the accuracy of the information provided by, for example, based on the employee's past provision results. The providing unit can also analyze the employee's past provision results and propose an optimal provision method. The providing unit can also improve the provision algorithm by reflecting the employee's past provision results. This allows the providing unit to provide optimal information based on the employee's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0056] The providing unit can determine the priority of provision based on the submission time of the work content when providing information. For example, the providing unit can provide information preferentially for work whose submission deadline is approaching. The providing unit can also postpone providing information for work whose submission deadline is far away. The providing unit can also automatically determine the priority of provision based on the submission time of the work content. This allows the providing unit to provide optimal information based on the submission time of the work content. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0057] The providing unit can adjust the order of information provision based on the relevance of the work content when providing information. For example, the providing unit can provide information on highly relevant work with priority. The providing unit can also provide information on less relevant work at a later date. The providing unit can also automatically adjust the order of information provision based on the relevance of the work content. This allows the providing unit to provide an optimal information provision order based on the relevance of the work content. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0058] When providing information, the providing unit can adjust the use of technical terminology in the provided information according to the employee's level of expertise. For example, the providing unit can provide information that uses a lot of technical terminology to employees with a high level of expertise. The providing unit can also provide information that avoids technical terminology to employees with a low level of expertise. The providing unit can also automatically adjust the use of technical terminology in the provided information based on the employee's level of expertise. This allows the providing unit to provide optimal information according to the employee's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can analyze an employee's past work performance data and propose an optimal handover method. For example, the analysis unit can identify which tasks are particularly important based on the employee's past work performance data and generate detailed handover procedures for those tasks. The analysis unit can also analyze an employee's past work performance data to identify which tasks take particularly long and propose efficient handover procedures for those tasks. Furthermore, the analysis unit can identify which tasks are particularly prone to errors based on the employee's past work performance data and propose handover procedures to reduce errors for those tasks. In this way, the analysis unit can provide an optimal handover method based on the employee's past work performance data.

[0061] The recording unit can automatically record related work content based on the employee's geographic location information. For example, when an employee is in a specific office, the recording unit can prioritize recording work content related to that office. In addition, when an employee is on a business trip, the recording unit can automatically record work content related to the business trip destination. Furthermore, when an employee is working remotely, the recording unit can also record work content related to the remote work. In this way, the recording unit can provide optimal work records based on the employee's geographic location information.

[0062] The analysis unit can customize the way in which the analysis results are presented based on the employee's past feedback. For example, the analysis unit can display the analysis results in a visually easy-to-understand manner based on the employee's past feedback. The analysis unit can also analyze the employee's past feedback and propose the optimal way to display the analysis results. Furthermore, the analysis unit can reflect the employee's feedback and continuously improve the way in which the analysis results are displayed. In this way, the analysis unit can provide the optimal way to display the analysis results based on the employee's past feedback.

[0063] The recording unit can analyze employees' social media activities and automatically record related work content. For example, the recording unit can analyze employees' social media posts and record work content related to those posts. The recording unit can also suggest an optimal recording method based on the employee's social media activity history. Furthermore, the recording unit can also record related work content by referring to the activities of the employee's friends on social media. In this way, the recording unit can provide optimal work records based on the employee's social media activities.

[0064] The provision department can provide optimal training materials based on the employee's past work history. For example, the provision department can analyze the employee's past work history and provide training materials related to that work. The provision department can also suggest the most efficient training materials based on the employee's past work history. Furthermore, the provision department can continuously improve the training materials by reflecting the employee's past work history. This allows the provision department to provide optimal training materials based on the employee's past work history.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The recording department records the details of the work that the employee is currently performing. For example, it can record information such as work procedures, tools used, and related documents. It can also automatically record work content using generative AI. Step 2: The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can use generative AI to analyze the business content. The analysis unit can analyze the business content using techniques such as data mining, statistical analysis, and machine learning. Step 3: The generation unit generates handover information based on the information analyzed by the analysis unit. For example, the generation AI can be used to generate documents that explain business procedures step by step. It can also be used to generate videos that explain how to use related tools. Step 4: The provision unit provides the information generated by the generation unit. For example, it can provide the necessary training materials and FAQs to help new staff members start their work smoothly. It can also continue to support new staff members even after the handover.

[0067] (Example 2) A system according to an embodiment of the present invention significantly reduces the time required for job handovers and improves the speed of transition to new tasks during personnel transfers or job changes. This system records the details of the tasks currently performed by employees, and a generation AI analyzes this information to propose the optimal handover method for new personnel. For example, the generation AI generates documents that explain step-by-step tasks and provides videos on how to use related tools. The generation AI also provides necessary training materials and FAQs to help new personnel start their tasks smoothly. Furthermore, the generation AI continues to support new personnel even after the handover. This not only significantly reduces the time required for handovers but also improves the speed of transition to new tasks. This system significantly reduces the time required for job handovers and improves the speed of transition to new tasks. For example, employees record the details of their current tasks, and the generation AI analyzes this information to propose the optimal handover method for new personnel, significantly reducing the time required for handovers. The generation AI also generates documents that explain step-by-step tasks and provides videos on how to use related tools, helping new personnel start their tasks smoothly. Furthermore, the generative AI will provide necessary training materials and FAQs, and continue to support new personnel even after the handover, thereby improving the speed at which they can switch to new tasks.

[0068] A business handover support system according to an embodiment includes a recording unit, an analysis unit, a generation unit, and a provision unit. The recording unit records details of the business operations currently performed by employees. For example, the recording unit can record information such as business procedures, tools used, and related documents. The recording unit can also automatically record business operations using a generation AI. The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can analyze business operations using a generation AI. The analysis unit can analyze business operations using techniques such as data mining, statistical analysis, and machine learning. The generation unit generates handover information based on the information analyzed by the analysis unit. For example, the generation unit can generate a document that explains business procedures step by step using a generation AI. The generation unit can also provide video instructions on how to use related tools. The provision unit provides the information generated by the generation unit. For example, the provision unit can provide necessary training materials and FAQs to help new personnel smoothly start their work. The provision unit can also continue to support new personnel even after the handover. As a result, the business task handover support system according to the embodiment can significantly reduce the number of steps required for business task handover and improve the speed of switching to a new business task.

[0069] The recording unit can record information about business procedures, tools used, and related documents. For example, the recording unit can record business procedures step by step. The recording unit can also record information about tools used. For example, the recording unit can record how to use software tools or hardware tools. The recording unit can also record information about related documents. For example, the recording unit can record information such as manuals, guidelines, and specifications. This enables the recording unit to record detailed business content. Some or all of the above-mentioned processing in the recording unit may be performed using or without the generation AI. For example, the recording unit can input business procedures into the generation AI, which then automatically records them.

[0070] The generation unit can generate a document that explains a business procedure step by step. For example, the generation unit can generate a document that explains a business procedure step by step. The generation unit can also automatically document the business procedure using a generation AI. For example, the generation unit can input the business procedure into the generation AI, which can then automatically generate a step-by-step procedure manual. The generation unit can also use the generation AI to generate a document that explains the business procedure in a tutorial format. This allows the generation unit to enable new personnel to start their work smoothly. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0071] The generation unit can use a video to explain how to use a specific tool. For example, the generation unit can use a video to explain how to use a specific tool. The generation unit can also use a generation AI to automatically create a video on how to use the tool. For example, the generation unit can input how to use the tool into the generation AI, which can then automatically generate a screencast or demonstration video. The generation unit can also use the generation AI to generate a video that visually explains how to use the tool. This makes it easier for new personnel to understand how to use the tool. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0072] The providing unit can provide necessary training materials and FAQs so that the new person in charge can smoothly start their work. For example, the providing unit can provide necessary training materials and FAQs so that the new person in charge can smoothly start their work. The providing unit can also automatically generate training materials and FAQs using a generation AI. For example, the providing unit can input the contents of the training materials and FAQs into the generation AI, which can then automatically generate presentation materials, training manuals, and frequently asked questions and their answers. The providing unit can also generate troubleshooting guides using the generation AI. This allows the providing unit to enable the new person in charge to quickly obtain the necessary information. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI.

[0073] The provision unit can continue to support the new person in charge even after the handover. The provision unit can, for example, continue to support the new person in charge even after the handover. The provision unit can also automatically provide support using a generation AI. For example, the provision unit can input support content into the generation AI, which can automatically provide periodic follow-up and help desk services. The provision unit can also automatically generate answers to questions from the new person in charge using the generation AI. This allows the provision unit to ensure that the new person in charge can receive support even after the handover. Some or all of the above-mentioned processing in the provision unit may be performed using the generation AI, or may be performed without using the generation AI.

[0074] The recording unit can estimate the employee's emotions and adjust the recording method of the work content according to the estimated employee emotions. For example, if the employee is feeling stressed, the recording unit can provide a simple interface and minimize the recording procedure. Furthermore, if the employee is relaxed, the recording unit can provide detailed recording options and suggest a customizable recording method. Furthermore, if the employee is in a hurry, the recording unit can prioritize voice input to quickly record the work content. This allows the recording unit to provide the optimal recording method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI.

[0075] When recording work content, the recording unit can select an appropriate recording format by referring to the employee's past work history. For example, the recording unit can automatically suggest a recording format that the employee has used in the past. The recording unit can also select the most efficient recording format from the employee's past work history. The recording unit can also analyze the employee's past work history and suggest a recording format suitable for a specific task. This allows the recording unit to provide the optimal recording format based on the employee's past work history. Some or all of the above-mentioned processing in the recording unit may be performed using or without using a generation AI.

[0076] When recording the content of a task, the recording unit can adjust the level of detail of the recording depending on the importance of the task. For example, the recording unit can request detailed recording for tasks with high importance. The recording unit can also allow simplified recording for tasks with low importance. The recording unit can also automatically adjust the level of detail of the recording depending on the importance of the task. This allows the recording unit to provide an optimal record depending on the importance of the task. Some or all of the above-described processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0077] When recording work content, the recording unit can select an appropriate recording means depending on the employee's input method. For example, if the employee uses voice input, the recording unit can record the work content using voice recognition technology. Furthermore, if the employee uses text input, the recording unit can provide a text editor to record the work content. Furthermore, if the employee uses image input, the recording unit can record the work content using image recognition technology. This allows the recording unit to provide the optimal recording means depending on the employee's input method. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0078] The recording unit can estimate the employee's emotions and determine the priority of the tasks to be recorded based on the estimated employee emotions. For example, if the employee is feeling stressed, the recording unit can prioritize recording tasks of high importance. Furthermore, if the employee is relaxed, the recording unit can also record tasks of low importance. Furthermore, if the employee is in a hurry, the recording unit can prioritize recording the most important tasks. This allows the recording unit to provide optimal prioritization of tasks according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI.

[0079] When recording work content, the recording unit can prioritize recording highly relevant work based on the employee's geographic location information. For example, if the employee is in a specific location, the recording unit can prioritize recording work related to that location. The recording unit can also automatically suggest highly relevant work based on the employee's geographic location information. The recording unit can also analyze the employee's geographic location information and suggest the optimal recording method. This allows the recording unit to provide optimal work records based on the employee's geographic location information. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0080] The recording unit can analyze the employee's social media activity and record specific work content when recording work content. The recording unit can, for example, analyze the employee's social media posts and record related work content. The recording unit can also suggest an optimal recording method based on the employee's social media activity history. The recording unit can also record related work content by referring to the activity of the employee's friends on social media. In this way, the recording unit can provide optimal work records based on the employee's social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0081] When recording work content, the recording unit can customize the recording method by reflecting the employee's past feedback. The recording unit can customize the recording method based on, for example, feedback provided by the employee in the past. The recording unit can also analyze the employee's past feedback and propose an optimal recording method. The recording unit can also reflect the employee's feedback and continuously improve the recording method. In this way, the recording unit can provide an optimal recording method based on the employee's past feedback. Some or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI.

[0082] The analysis unit can estimate the employee's emotions and adjust the way the analysis is presented depending on the estimated employee emotions. For example, if the employee is nervous, the analysis unit can provide a simple, highly visible analysis result. If the employee is relaxed, the analysis unit can provide a detailed analysis result. If the employee is in a hurry, the analysis unit can provide a summary analysis result. This allows the analysis unit to provide optimal analysis results according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the task content. For example, the analysis unit can perform a detailed analysis for a task with a high level of importance. The analysis unit can also perform a simplified analysis for a task with a low level of importance. The analysis unit can also automatically adjust the level of detail of the analysis according to the importance of the task. This allows the analysis unit to provide an optimal analysis according to the importance of the task content. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0084] The analysis unit can apply different analysis algorithms depending on the business category during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the business category. The analysis unit can also apply different analysis algorithms to each business category. The analysis unit can also automatically switch the analysis algorithm based on the business category. This allows the analysis unit to provide the optimal analysis depending on the business category. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on the employee's past analysis results. The analysis unit can also analyze the employee's past analysis results and propose an optimal analysis method. The analysis unit can also improve the analysis algorithm by reflecting the employee's past analysis results. This allows the analysis unit to provide an optimal analysis based on the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0086] The analysis unit can estimate the employee's emotions and adjust the length of the analysis depending on the estimated employee emotions. For example, if the employee is nervous, the analysis unit can provide a short and concise analysis result. If the employee is relaxed, the analysis unit can provide a detailed analysis result. If the employee is in a hurry, the analysis unit can provide a quick analysis result. This allows the analysis unit to provide an optimal analysis result according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the work content. For example, the analysis unit can prioritize analysis of work with an upcoming submission deadline. The analysis unit can also postpone analysis of work with a distant submission deadline. The analysis unit can also automatically determine the priority of analysis based on the submission time of the work content. This allows the analysis unit to provide an optimal analysis based on the submission time of the work content. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the work content during analysis. For example, the analysis unit can prioritize analysis of highly relevant work. The analysis unit can also postpone analysis of less relevant work. The analysis unit can also automatically adjust the order of analysis based on the relevance of the work content. This allows the analysis unit to provide an optimal analysis order based on the relevance of the work content. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with a low level of expertise. The analysis unit can also automatically adjust the use of technical terminology in the analysis results based on the employee's level of expertise. This allows the analysis unit to provide optimal analysis results according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI.

[0090] The generation unit can estimate the employee's emotions and adjust the expression method of the generated document according to the estimated employee's emotions. For example, if the employee is relaxed, the generation unit can generate a document that progresses at a leisurely pace. If the employee is in a hurry, the generation unit can generate a document that emphasizes the shortest route. If the employee is excited, the generation unit can generate a document that adds visually stimulating effects. This allows the generation unit to generate an optimal document according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0091] The generation unit can adjust the level of detail of the generated document based on the importance of the business content when generating the document. For example, the generation unit can generate a detailed document for a business of high importance. The generation unit can also generate a simplified document for a business of low importance. The generation unit can also automatically adjust the level of detail of the generated document according to the importance of the business. This allows the generation unit to provide optimal document generation according to the importance of the business content. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0092] The generation unit can apply different generation algorithms depending on the business category when generating a document. The generation unit can select the optimal generation algorithm depending on the business category, for example. The generation unit can also apply different generation algorithms to each business category. The generation unit can also automatically switch the generation algorithm based on the business category. This allows the generation unit to provide optimal document generation depending on the business category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0093] When generating a document, the generation unit can improve the accuracy of generation by referring to the employee's past generation results. The generation unit can improve the accuracy of generation, for example, based on the employee's past generation results. The generation unit can also analyze the employee's past generation results and propose an optimal generation method. The generation unit can also improve the generation algorithm by reflecting the employee's past generation results. In this way, the generation unit can provide optimal document generation based on the employee's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0094] The generation unit can estimate the employee's emotions and adjust the length of the document to be generated depending on the estimated employee emotions. For example, if the employee is in a hurry, the generation unit can generate a short, to-the-point document. If the employee is relaxed, the generation unit can generate a longer document with detailed explanations. If the employee is excited, the generation unit can generate a document with visually stimulating effects. This allows the generation unit to generate an optimal document according to the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0095] The generation unit can determine the generation priority when generating documents based on the submission time of the work content. For example, the generation unit can prioritize document generation for work with an upcoming submission deadline. The generation unit can also postpone document generation for work with a distant submission deadline. The generation unit can also automatically determine the generation priority based on the submission time of the work content. This allows the generation unit to provide optimal document generation based on the submission time of the work content. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0096] The generation unit can adjust the order of generation based on the relevance of the work content when generating documents. For example, the generation unit can prioritize documenting highly relevant work. The generation unit can also document less relevant work later. The generation unit can also automatically adjust the order of generation based on the relevance of the work content. This allows the generation unit to provide an optimal document generation order based on the relevance of the work content. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0097] When generating a document, the generation unit can adjust the use of technical terminology in the generation according to the employee's level of expertise. For example, the generation unit can generate a document that uses a lot of technical terminology for an employee with a high level of expertise. The generation unit can also generate a document that avoids technical terminology for an employee with a low level of expertise. The generation unit can also automatically adjust the use of technical terminology in the generated document based on the employee's level of expertise. This allows the generation unit to generate an optimal document according to the employee's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0098] The providing unit can estimate the employee's emotions and adjust the way the information is presented depending on the estimated employee's emotions. For example, if the employee is nervous, the providing unit can provide simple, highly visible information. If the employee is relaxed, the providing unit can provide detailed information. If the employee is in a hurry, the providing unit can provide information that focuses on the main points. This allows the providing unit to provide optimal information depending on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI.

[0099] The providing unit can adjust the level of detail to be provided based on the importance of the work content when providing information. For example, the providing unit can provide detailed information for work of high importance. The providing unit can also provide simplified information for work of low importance. The providing unit can also automatically adjust the level of detail to be provided according to the importance of the work. This allows the providing unit to provide optimal information according to the importance of the work content. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0100] The providing unit can apply different providing algorithms depending on the business category when providing information. The providing unit can select the optimal providing algorithm depending on the business category, for example. The providing unit can also apply different providing algorithms for each business category. The providing unit can also automatically switch the providing algorithm based on the business category. This allows the providing unit to provide optimal information depending on the business category. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0101] When providing information, the providing unit can improve the accuracy of the information provided by referring to the employee's past provision results. The providing unit can improve the accuracy of the information provided by, for example, based on the employee's past provision results. The providing unit can also analyze the employee's past provision results and propose an optimal provision method. The providing unit can also improve the provision algorithm by reflecting the employee's past provision results. This allows the providing unit to provide optimal information based on the employee's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0102] The providing unit can estimate the employee's emotions and adjust the length of the information to be provided depending on the estimated employee's emotions. For example, if the employee is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, if the employee is relaxed, the providing unit can provide longer information with detailed explanations. Furthermore, if the employee is excited, the providing unit can provide information with visually stimulating effects. This allows the providing unit to provide optimal information according to the employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI.

[0103] The providing unit can determine the priority of provision based on the submission time of the work content when providing information. For example, the providing unit can provide information preferentially for work whose submission deadline is approaching. The providing unit can also postpone providing information for work whose submission deadline is far away. The providing unit can also automatically determine the priority of provision based on the submission time of the work content. This allows the providing unit to provide optimal information based on the submission time of the work content. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0104] The providing unit can adjust the order of information provision based on the relevance of the work content when providing information. For example, the providing unit can provide information on highly relevant work with priority. The providing unit can also provide information on less relevant work at a later date. The providing unit can also automatically adjust the order of information provision based on the relevance of the work content. This allows the providing unit to provide an optimal information provision order based on the relevance of the work content. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI.

[0105] When providing information, the providing unit can adjust the use of technical terminology in the provided information according to the employee's level of expertise. For example, the providing unit can provide information that uses a lot of technical terminology to employees with a high level of expertise. The providing unit can also provide information that avoids technical terminology to employees with a low level of expertise. The providing unit can also automatically adjust the use of technical terminology in the provided information based on the employee's level of expertise. This allows the providing unit to provide optimal information according to the employee's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned recording unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recording unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned recording unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recording unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned recording unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the recording unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned recording unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can analyze an employee's past work performance data and propose an optimal handover method. For example, the analysis unit can identify which tasks are particularly important based on the employee's past work performance data and generate detailed handover procedures for those tasks. The analysis unit can also analyze an employee's past work performance data to identify which tasks take particularly long and propose efficient handover procedures for those tasks. Furthermore, the analysis unit can identify which tasks are particularly prone to errors based on the employee's past work performance data and propose handover procedures to reduce errors for those tasks. In this way, the analysis unit can provide an optimal handover method based on the employee's past work performance data.

[0108] The provision unit can estimate the employee's emotions and adjust the format of the training materials to be provided based on the estimated employee's emotions. For example, if the employee is feeling stressed, the provision unit can provide training materials that are simple and visually easy to understand. If the employee is relaxed, the provision unit can provide training materials that include detailed explanations. Furthermore, if the employee is in a hurry, the provision unit can provide short training materials that focus on the main points. In this way, the provision unit can provide optimal training materials according to the employee's emotions.

[0109] The recording unit can automatically record related work content based on the employee's geographic location information. For example, when an employee is in a specific office, the recording unit can prioritize recording work content related to that office. In addition, when an employee is on a business trip, the recording unit can automatically record work content related to the business trip destination. Furthermore, when an employee is working remotely, the recording unit can also record work content related to the remote work. In this way, the recording unit can provide optimal work records based on the employee's geographic location information.

[0110] The generation unit can estimate the employee's emotions and adjust the content of the video to be generated based on the estimated employee's emotions. For example, if the employee is feeling stressed, the generation unit can generate a simple, visually easy-to-understand video. If the employee is relaxed, the generation unit can also generate a video that includes detailed explanations. Furthermore, if the employee is in a hurry, the generation unit can also generate a short video that gets to the point. In this way, the generation unit can generate an optimal video according to the employee's emotions.

[0111] The analysis unit can customize the way in which the analysis results are presented based on the employee's past feedback. For example, the analysis unit can display the analysis results in a visually easy-to-understand manner based on the employee's past feedback. The analysis unit can also analyze the employee's past feedback and propose the optimal way to display the analysis results. Furthermore, the analysis unit can reflect the employee's feedback and continuously improve the way in which the analysis results are displayed. In this way, the analysis unit can provide the optimal way to display the analysis results based on the employee's past feedback.

[0112] The provision unit can estimate the employee's emotions and adjust the content of the FAQ to be provided based on the estimated employee's emotions. For example, if the employee is feeling stressed, the provision unit can provide a simple, visually easy-to-understand FAQ. If the employee is relaxed, the provision unit can also provide a FAQ with detailed explanations. Furthermore, if the employee is in a hurry, the provision unit can also provide a short FAQ that covers the main points. In this way, the provision unit can provide the optimal FAQ according to the employee's emotions.

[0113] The recording unit can analyze employees' social media activities and automatically record related work content. For example, the recording unit can analyze employees' social media posts and record work content related to those posts. The recording unit can also suggest an optimal recording method based on the employee's social media activity history. Furthermore, the recording unit can also record related work content by referring to the activities of the employee's friends on social media. In this way, the recording unit can provide optimal work records based on the employee's social media activities.

[0114] The generation unit can estimate the employee's emotions and adjust the format of the generated training materials based on the estimated employee emotions. For example, if the employee is feeling stressed, the generation unit can generate training materials that are simple and visually easy to understand. If the employee is feeling relaxed, the generation unit can generate training materials that include detailed explanations. Furthermore, if the employee is in a hurry, the generation unit can generate short training materials that focus on the main points. In this way, the generation unit can generate optimal training materials according to the employee's emotions.

[0115] The provision department can provide optimal training materials based on the employee's past work history. For example, the provision department can analyze the employee's past work history and provide training materials related to that work. The provision department can also suggest the most efficient training materials based on the employee's past work history. Furthermore, the provision department can continuously improve the training materials by reflecting the employee's past work history. This allows the provision department to provide optimal training materials based on the employee's past work history.

[0116] The analysis unit can estimate the employee's emotions and adjust the way the analysis results are displayed based on the estimated employee emotions. For example, if the employee is feeling stressed, the analysis unit can provide a simple, visually easy-to-understand analysis result. If the employee is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the employee is in a hurry, the analysis unit can provide a short analysis result that focuses on the main points. In this way, the analysis unit can provide the optimal analysis result according to the employee's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The recording department records the details of the work that the employee is currently performing. For example, it can record information such as work procedures, tools used, and related documents. It can also automatically record work content using generative AI. Step 2: The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can use generative AI to analyze the business content. The analysis unit can analyze the business content using techniques such as data mining, statistical analysis, and machine learning. Step 3: The generation unit generates handover information based on the information analyzed by the analysis unit. For example, the generation AI can be used to generate documents that explain business procedures step by step. It can also be used to generate videos that explain how to use related tools. Step 4: The provision unit provides the information generated by the generation unit. For example, it can provide the necessary training materials and FAQs to help new staff members start their work smoothly. It can also continue to support new staff members even after the handover.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 recording department that records the details of work; an analysis unit that analyzes the information recorded by the recording unit; a generation unit that generates handover information based on the information analyzed by the analysis unit; a providing unit that provides the information generated by the generating unit; Equipped with A system characterized by:

2. The recording unit Record information about business procedures, tools used, and related documentation 2. The system of claim 1.

3. The generation unit Generate step-by-step documentation for business procedures 2. The system of claim 1.

4. The generation unit Videos explaining how to use specific tools 2. The system of claim 1.

5. The providing unit Provide new personnel with the necessary training materials and FAQs to help them get started smoothly 2. The system of claim 1.

6. The providing unit Continue supporting new staff after the handover 2. The system of claim 1.

7. The recording unit Infer employee sentiment and adjust how work is recorded based on that sentiment 2. The system of claim 1.

8. The recording unit When recording work content, select the appropriate recording format by referring to the employee's past work history.

2. The system of claim 1.

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