System

Abstract

An object of a system according to an embodiment is to efficiently perform work without depending on an individual's skill.SOLUTION: A system according to an embodiment includes an acquisition unit, a skill learning unit, an assist unit, and a procedure generation unit. The acquisition unit acquires a business procedure. The skill learning unit learns the work procedure acquired by the acquisition unit. The assist unit provides advice in real time when another person performs the same work based on the skill learned by the skill learning unit. The procedure generation unit automatically generates a work procedure based on the skill learned by the skill learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not sufficiently improve the efficiency of tasks that depend on individual skills, which can cause difficulties for other people when performing the same tasks.

[0005] The system according to the embodiment aims to perform work efficiently without relying on the skills of individuals. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a skill learning unit, an assist unit, and a procedure generation unit. The acquisition unit acquires a business procedure. The skill learning unit learns the business procedure acquired by the acquisition unit. The assist unit provides advice in real time to other people when performing the same business, based on the skills learned by the skill learning unit. The procedure generation unit automatically generates a business procedure based on the skills learned by the skill learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform work without relying on the skills of individuals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The task assistance system according to an embodiment of the present invention is a system that allows other people to efficiently perform the same tasks without relying on individual skills. The task assistance system uses AI to learn an individual's task skills and assist others when performing the same tasks. This eliminates the need to create explanatory materials. For example, the task assistance system trains AI to learn an individual's task skills. During this process, detailed records of the task's procedures and know-how are entered into the AI. The AI ​​then assists others when performing the same tasks based on the skills learned. For example, when a new employee performs a specific task, the AI ​​provides real-time advice. Furthermore, the AI's assistance eliminates the need to create explanatory materials. This improves work efficiency and enables tasks to be performed without relying on individual skills. This allows the task assistance system to efficiently perform the same tasks without relying on individual skills. For example, even if an employee skilled in a particular task leaves the company, the AI ​​retains that skill, allowing other employees to perform the same task. Furthermore, new employees can quickly master the task and become immediately effective. This will improve productivity across the entire company and make operations more efficient.

[0029] The business assistance system according to the embodiment includes an acquisition unit, a skill learning unit, an assist unit, and a procedure generation unit. The acquisition unit acquires business procedures. For example, the acquisition unit records business procedures and know-how in detail and inputs them into an AI. The acquisition unit can record business procedures in, for example, text format, video format, or audio format. The skill learning unit learns the business procedures acquired by the acquisition unit. The skill learning unit learns, for example, how to operate specific software or how to proceed with a specific business process. The skill learning unit can learn business procedures using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The assist unit provides advice in real time when other people perform the same task based on the skills learned by the skill learning unit. For example, the assist unit provides advice in real time when a new employee performs a specific task using methods such as voice assistance, text messages, or pop-up notifications. The procedure generation unit automatically generates business procedures based on the skills learned by the skill learning unit. The procedure generation unit automatically generates work procedures using, for example, an AI algorithm. This allows the work assistance system to be performed efficiently by other people, without relying on individual skills. For example, even if an employee skilled in a particular task leaves the company, another employee can perform the same task because the AI ​​retains that skill. Furthermore, new employees can quickly learn the task and become immediately effective. This improves productivity across the company and realizes work efficiency.

[0030] The acquisition unit can specifically record business procedures or know-how and input them to the AI. For example, the acquisition unit can record business procedures or know-how in detail and input them to the AI. For example, the acquisition unit can record business procedures in text format, video format, audio format, etc. For example, the acquisition unit can record business procedures in text format and input them to the AI. The acquisition unit can also record business procedures in video format and input them to the AI. The acquisition unit can also record business procedures in audio format and input them to the AI. In this way, by recording business procedures and know-how in detail, the AI ​​can learn accurately. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can record business procedures in text format and input them to the generation AI, causing the generation AI to analyze the business procedures.

[0031] The skill learning unit can learn how to operate specific software or how to proceed with a specific business process. The skill learning unit, for example, learns how to operate specific software. For example, the skill learning unit learns how to operate Excel. The skill learning unit can also learn how to operate Photoshop. The skill learning unit can also learn how to proceed with a specific business process. For example, the skill learning unit learns manufacturing line procedures. The skill learning unit can also learn customer support procedures. In this way, by learning specific software or business processes, the AI ​​can handle a wide range of tasks. Some or all of the above-mentioned processing in the skill learning unit may be performed using, or without, a generation AI. For example, the skill learning unit can input how to operate specific software into the generation AI, causing the generation AI to learn the operation method.

[0032] The assisting unit can provide advice in real time to a new employee when performing a specific task. For example, the assisting unit provides advice in real time to a new employee when performing a specific task. For example, the assisting unit provides voice assistance. The assisting unit can also provide text messages. The assisting unit can also provide pop-up notifications. This allows the new employee to quickly learn the task and perform the task efficiently. Some or all of the above-described processing in the assisting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the assisting unit can cause the generation AI to provide advice in real time to a new employee when performing a specific task.

[0033] The procedure generation unit can automatically generate business procedures. The procedure generation unit, for example, automatically generates business procedures. For example, the procedure generation unit automatically generates business procedures using an AI algorithm. The procedure generation unit can also automatically generate business procedures using a generation AI. By automatically generating business procedures, the effort required to create explanatory materials can be reduced. Some or all of the above-described processing in the procedure generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the procedure generation unit can input business procedures into the generation AI and have the generation AI automatically generate the business procedures.

[0034] The acquisition unit can analyze the user's past work history and select the optimal acquisition method when acquiring a work procedure. For example, the acquisition unit analyzes the user's past work history and selects the optimal acquisition method when acquiring a work procedure. For example, the acquisition unit prioritizes acquiring work procedures that the user has frequently performed in the past. The acquisition unit can also select and acquire efficient procedures from the user's past work history. The acquisition unit can also avoid work procedures that the user has had difficulty with in the past and prioritize acquiring procedures that the user is good at. In this way, the optimal acquisition method can be selected by analyzing the user's past work history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's past work history data into the generation AI and have the generation AI select the optimal acquisition method.

[0035] The acquisition unit can filter the business procedures based on the user's current project and areas of interest when acquiring the business procedures. For example, the acquisition unit can prioritize acquiring business procedures related to the project the user is currently working on. The acquisition unit can also filter and acquire related business procedures based on the user's areas of interest. The acquisition unit can also acquire business procedures related to areas in which the user has previously shown interest. In this way, by filtering business procedures based on the user's current project and areas of interest, highly relevant procedures can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, or without, a generation AI. For example, the acquisition unit can input data on the user's current project and areas of interest into the generation AI and have the generation AI perform filtering.

[0036] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring the business procedures. For example, the acquisition unit selects the optimal acquisition means depending on the user's input method (voice, text, image, etc.) when acquiring the business procedures. For example, when the user uses voice input, the acquisition unit acquires the business procedures using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the business procedures using text analysis technology. Furthermore, when the user uses image input, the acquisition unit can also acquire the business procedures using image recognition technology. This enables efficient acquisition of business procedures by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit inputs the user's input data into the generation AI, causing the generation AI to select the optimal acquisition means.

[0037] When acquiring work procedures, the acquisition unit can prioritize acquiring highly relevant procedures by taking into account the user's geographical location information. For example, when acquiring work procedures, the acquisition unit prioritizes acquiring highly relevant procedures by taking into account the user's geographical location information. For example, when the user is in the office, the acquisition unit prioritizes acquiring office-related work procedures. Furthermore, when the user is on a business trip, the acquisition unit can prioritize acquiring work procedures required at the business trip destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring work procedures related to remote work. In this way, by taking into account the user's geographical location information, highly relevant work procedures can be prioritized. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit inputs the user's geographical location information to the generation AI, causing the generation AI to select highly relevant procedures.

[0038] The acquisition unit can analyze the user's social media activity when acquiring a business procedure and acquire related procedures. For example, the acquisition unit can analyze the user's social media activity when acquiring a business procedure and acquire related procedures. For example, the acquisition unit can acquire related procedures based on business content shared by the user on social media. The acquisition unit can also acquire business procedures that the user is likely to be interested in from the user's social media activity. The acquisition unit can also acquire related business procedures by referring to the activity of the user's friends on social media. In this way, related business procedures can be acquired by analyzing the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and have the generation AI select related procedures.

[0039] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring a business procedure. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring a business procedure. For example, the acquisition unit selects an optimal acquisition method based on feedback provided by the user in the past. The acquisition unit can also acquire business procedures by reflecting improvements based on the user's past feedback. The acquisition unit can also preferentially acquire business procedures that the user has previously evaluated. This allows the optimal acquisition method to be selected by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and have the generation AI customize the acquisition method.

[0040] The skill learning unit can adjust the level of detail of the learning based on the importance of the business procedure during skill learning. For example, the skill learning unit can adjust the level of detail of the learning based on the importance of the business procedure during skill learning. For example, the skill learning unit can provide detailed learning content for business procedures with high importance. The skill learning unit can also provide simplified learning content for business procedures with low importance. The skill learning unit can also adjust the progress speed of the learning based on the importance of the business procedure. This enables efficient learning by adjusting the level of detail of the learning based on the importance of the business procedure. Some or all of the above-mentioned processing in the skill learning unit can be performed using, or without, a generation AI. For example, the skill learning unit can input importance data of the business procedure into the generation AI and have the generation AI adjust the level of detail of the learning.

[0041] The skill learning unit can apply different learning algorithms depending on the task category during skill learning. For example, the skill learning unit applies different learning algorithms depending on the task category during skill learning. For example, the skill learning unit applies an interactive learning algorithm to skill learning related to software operation. The skill learning unit can also apply a simulation-based learning algorithm to skill learning related to project management. The skill learning unit can also apply an interactive learning algorithm to learning related to communication skills. This enables efficient learning by applying different learning algorithms depending on the task category. Some or all of the above-described processing in the skill learning unit may be performed using, or without, a generation AI. For example, the skill learning unit inputs task category data into the generation AI, causing the generation AI to apply an appropriate learning algorithm.

[0042] The skill learning unit can improve the accuracy of skill learning by referring to the user's past learning results. For example, the skill learning unit can improve the accuracy of skill learning by referring to the user's past learning results. For example, the skill learning unit provides learning content, including review, based on the content the user has previously learned. The skill learning unit can also focus on weak areas based on the user's past learning results. The skill learning unit can also analyze the user's past learning history and suggest an optimal learning method. By referring to the user's past learning results, the accuracy of learning is improved. Some or all of the above-described processing in the skill learning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the skill learning unit can input the user's past learning result data into the generation AI, and the generation AI can improve the accuracy of learning.

[0043] The skill learning unit can determine the learning priority based on the submission time of the business procedures during skill learning. The skill learning unit, for example, determines the learning priority based on the submission time of the business procedures during skill learning. For example, the skill learning unit prioritizes providing learning content for business procedures with an upcoming submission deadline. The skill learning unit can also postpone providing learning content for business procedures with a distant submission deadline. The skill learning unit can also adjust the learning progress speed depending on the submission time. This enables efficient learning by determining the learning priority based on the submission time of the business procedures. Some or all of the above-mentioned processing in the skill learning unit may be performed using, or without, a generation AI. For example, the skill learning unit can input submission time data for business procedures into the generation AI and have the generation AI determine the learning priority.

[0044] The skill learning unit can adjust the order of learning based on the relevance of the work procedures during skill learning. The skill learning unit, for example, adjusts the order of learning based on the relevance of the work procedures during skill learning. For example, the skill learning unit prioritizes learning of highly relevant work procedures. The skill learning unit can also postpone learning of less relevant work procedures. The skill learning unit can also adjust the speed of learning progress according to the relevance of the work procedures. This enables efficient learning by adjusting the order of learning based on the relevance of the work procedures. Some or all of the above-described processing in the skill learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the skill learning unit can input relevance data of the work procedures into the generation AI and have the generation AI adjust the order of learning.

[0045] The skill learning unit can adjust the use of technical terms in the learning according to the user's level of expertise during skill learning. For example, the skill learning unit can adjust the use of technical terms in the learning according to the user's level of expertise during skill learning. For example, the skill learning unit can provide learning content that uses a lot of technical terms to a user with a high level of expertise. The skill learning unit can also provide learning content explained in simple terms to a user with a low level of expertise. The skill learning unit can also adjust the level of detail of the learning content according to the user's level of expertise. This enables efficient learning by adjusting the use of technical terms in the learning according to the user's level of expertise. Some or all of the above-described processing in the skill learning unit can be performed using, or without, a generation AI. For example, the skill learning unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0046] The assisting unit can adjust the level of detail of the assistance based on the importance of the business procedure during assistance. For example, the assisting unit adjusts the level of detail of the assistance based on the importance of the business procedure during assistance. For example, the assisting unit provides detailed assistance for business procedures with high importance. The assisting unit can also provide simplified assistance for business procedures with low importance. The assisting unit can also adjust the progress speed of the assistance according to the importance of the business procedure. This enables efficient assistance by adjusting the level of detail of the assistance based on the importance of the business procedure. Some or all of the above-described processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input importance data of the business procedure into the generation AI and have the generation AI adjust the level of detail of the assistance.

[0047] The assisting unit can apply different assist algorithms depending on the task category when providing assistance. For example, the assisting unit applies different assist algorithms depending on the task category when providing assistance. For example, the assisting unit applies an interactive assist algorithm to assistance related to software operation. The assisting unit can also apply a simulation-based assist algorithm to assistance related to project management. The assisting unit can also apply an interactive assist algorithm to assistance related to communication skills. In this way, efficient assistance is possible by applying different assist algorithms depending on the task category. Some or all of the above-described processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input task category data into the generation AI and have the generation AI apply an appropriate assist algorithm.

[0048] The assisting unit can improve the accuracy of the assistance by referring to the user's past assist results when providing assistance. For example, the assisting unit can improve the accuracy of the assistance by referring to the user's past assist results when providing assistance. For example, the assisting unit provides an optimal assisting method based on the assistance content received by the user in the past. The assisting unit can also adjust the assistance content by reflecting areas for improvement based on the user's past assist results. The assisting unit can also preferentially provide assisting methods that the user has previously evaluated. This improves the accuracy of the assistance by referring to the user's past assist results. Some or all of the above-described processing in the assisting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the assisting unit can input the user's past assist result data into the generation AI and use the generation AI to improve the accuracy of the assistance.

[0049] The assisting unit can determine the priority of assistance based on the submission time of the business procedures when providing assistance. The assisting unit, for example, determines the priority of assistance based on the submission time of the business procedures when providing assistance. For example, the assisting unit prioritizes providing assistance content for business procedures with an upcoming submission deadline. The assisting unit can also postpone providing assistance content for business procedures with a distant submission deadline. The assisting unit can also adjust the progress speed of the assistance depending on the submission time. This enables efficient assistance by determining the priority of assistance based on the submission time of the business procedures. Some or all of the above-mentioned processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input submission time data of the business procedures into the generation AI and have the generation AI determine the priority of assistance.

[0050] The assisting unit can adjust the order of assistance based on the relevance of the work procedures during assistance. The assisting unit, for example, adjusts the order of assistance based on the relevance of the work procedures during assistance. For example, the assisting unit prioritizes assisting highly relevant work procedures. The assisting unit can also postpone assisting less relevant work procedures. The assisting unit can also adjust the speed of the assistance according to the relevance of the work procedures. This enables efficient assistance by adjusting the order of assistance based on the relevance of the work procedures. Some or all of the above-described processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input relevance data of the work procedures into the generation AI and have the generation AI adjust the order of assistance.

[0051] The assist unit can adjust the use of technical terms in the assistance depending on the user's level of expertise during assistance. For example, the assist unit can adjust the use of technical terms in the assistance depending on the user's level of expertise during assistance. For example, the assist unit can provide assistance content that uses a lot of technical terms to a user with a high level of expertise. The assist unit can also provide assistance content that explains things in simpler terms to a user with a low level of expertise. The assist unit can also adjust the level of detail of the assistance content depending on the user's level of expertise. This enables efficient assistance by adjusting the use of technical terms in the assistance depending on the user's level of expertise. Some or all of the above-described processing in the assist unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the assist unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0052] The procedure generation unit can adjust the level of detail of the generated procedure based on the importance of the business procedure when generating the procedure. For example, the procedure generation unit adjusts the level of detail of the generated procedure based on the importance of the business procedure when generating the procedure. For example, the procedure generation unit generates detailed procedures for business procedures with high importance. The procedure generation unit can also generate simplified procedures for business procedures with low importance. The procedure generation unit can also adjust the speed of the generation process depending on the importance of the business procedure. In this way, adjusting the level of detail of the generated procedure based on the importance of the business procedure enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, or without, a generation AI. For example, the procedure generation unit can input importance data of the business procedure to the generation AI and have the generation AI adjust the level of detail of the generated procedure.

[0053] The procedure generation unit can apply different generation algorithms depending on the task category when generating a procedure. For example, the procedure generation unit applies different generation algorithms depending on the task category when generating a procedure. For example, the procedure generation unit applies an interactive generation algorithm when generating a procedure related to software operation. The procedure generation unit can also apply a simulation-based generation algorithm when generating a procedure related to project management. The procedure generation unit can also apply an interactive generation algorithm when generating a procedure related to communication skills. In this way, applying different generation algorithms depending on the task category enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, or without, a generation AI. For example, the procedure generation unit inputs task category data into the generation AI and causes the generation AI to apply an appropriate generation algorithm.

[0054] The procedure generation unit can improve the accuracy of procedure generation by referring to the user's past procedure generation results when generating a procedure. For example, the procedure generation unit can improve the accuracy of generation by referring to the user's past procedure generation results when generating a procedure. For example, the procedure generation unit generates an optimal procedure based on procedures previously generated by the user. The procedure generation unit can also generate a procedure by reflecting improvements from the user's past procedure generation results. The procedure generation unit can also preferentially generate procedures that the user has previously evaluated. In this way, the accuracy of generation is improved by referring to the user's past procedure generation results. Some or all of the above-mentioned processing in the procedure generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the procedure generation unit can input the user's past procedure generation result data into the generation AI and use the generation AI to improve the accuracy of generation.

[0055] The procedure generation unit can determine the generation priority based on the submission time of the business procedure when generating the procedure. The procedure generation unit, for example, determines the generation priority based on the submission time of the business procedure when generating the procedure. For example, the procedure generation unit prioritizes the generation of procedures for business procedures with an upcoming submission deadline. The procedure generation unit can also postpone the generation of procedures for business procedures with a distant submission deadline. The procedure generation unit can also adjust the progress speed of procedure generation depending on the submission time. In this way, determining the generation priority based on the submission time of the business procedure enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, or without, a generation AI. For example, the procedure generation unit can input submission time data of the business procedure to the generation AI and have the generation AI determine the generation priority.

[0056] The procedure generation unit can adjust the order of generation based on the relevance of business procedures when generating procedures. The procedure generation unit, for example, adjusts the order of generation based on the relevance of business procedures when generating procedures. For example, the procedure generation unit prioritizes the generation of highly relevant business procedures. The procedure generation unit can also postpone the generation of less relevant business procedures. The procedure generation unit can also adjust the speed of generation progress according to the relevance of business procedures. In this way, adjusting the order of generation based on the relevance of business procedures enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure generation unit can input relevance data of business procedures into the generation AI and have the generation AI adjust the order of generation.

[0057] The procedure generation unit can adjust the use of technical terminology in the generated procedure according to the user's level of expertise when generating the procedure. For example, the procedure generation unit adjusts the use of technical terminology in the generated procedure according to the user's level of expertise when generating the procedure. For example, the procedure generation unit generates a procedure that uses a lot of technical terminology for a user with a high level of expertise. The procedure generation unit can also generate a procedure that explains in simple terms for a user with a low level of expertise. The procedure generation unit can also adjust the level of detail of the procedure according to the user's level of expertise. This enables efficient procedure generation by adjusting the use of technical terminology in the generated procedure according to the user's level of expertise. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The skill learning unit can also customize learning methods according to the user's learning style. For example, the skill learning unit can provide learning content that makes extensive use of videos and illustrations for visual learners. It can also provide learning content in the form of audio guides or podcasts for auditory learners. It can also provide interactive simulations and practical exercises for experiential learners. This allows for efficient skill acquisition by customizing learning methods according to the user's learning style.

[0060] The procedure generation unit can also analyze the user's past work performance and generate an optimal procedure. For example, the procedure generation unit can generate procedures with priority given to procedures in which the user has performed well in the past. It can also generate procedures that avoid procedures that the user has had difficulty with in the past. Furthermore, it can also suggest efficient procedures based on the user's past work performance data. This makes it possible to generate an optimal procedure by taking the user's past work performance into consideration.

[0061] The assist unit can also analyze the user's past assist history and provide the optimal assist method. For example, the assist unit can prioritize and provide assist methods that have been effective for the user in the past. It can also avoid assist methods that the user has been dissatisfied with in the past. Furthermore, it can provide customized assistance content based on the user's past assist history. This makes it possible to provide the optimal assistance method by taking the user's past assist history into consideration.

[0062] The acquisition unit can also monitor the user's current work environment and select the optimal acquisition method. For example, if the user is in a quiet environment, the acquisition unit can prioritize voice input. If the user is in a noisy environment, the acquisition unit can also prioritize text input. Furthermore, if the user is on the move, the work procedures can be acquired with a simple touch operation. This allows the efficient acquisition of work procedures by selecting the optimal acquisition method according to the user's current work environment.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The acquisition unit acquires business procedures. For example, the acquisition unit records business procedures and know-how in detail and inputs them into the AI. The acquisition unit can record business procedures in text format, video format, audio format, etc. Step 2: The skill learning unit learns the business procedures acquired by the acquisition unit. For example, the skill learning unit learns how to operate specific software or how to proceed with a specific business process. The skill learning unit can learn the business procedures using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The Assistance Department provides real-time advice to other people when performing the same tasks based on the skills learned by the Skill Learning Department. For example, when a new employee performs a specific task, the Assistance Department provides advice in real time using voice assistance, text messages, pop-up notifications, etc. Step 4: The procedure generation unit automatically generates work procedures based on the skills learned by the skill learning unit. The procedure generation unit automatically generates work procedures using, for example, an AI algorithm. This allows the work assistance system to be independent of individual skills and allows other people to perform the same work efficiently.

[0065] (Example 2) The task assistance system according to an embodiment of the present invention is a system that allows other people to efficiently perform the same tasks without relying on individual skills. The task assistance system uses AI to learn an individual's task skills and assist others when performing the same tasks. This eliminates the need to create explanatory materials. For example, the task assistance system trains AI to learn an individual's task skills. During this process, detailed records of the task's procedures and know-how are entered into the AI. The AI ​​then assists others when performing the same tasks based on the skills learned. For example, when a new employee performs a specific task, the AI ​​provides real-time advice. Furthermore, the AI's assistance eliminates the need to create explanatory materials. This improves work efficiency and enables tasks to be performed without relying on individual skills. This allows the task assistance system to efficiently perform the same tasks without relying on individual skills. For example, even if an employee skilled in a particular task leaves the company, the AI ​​retains that skill, allowing other employees to perform the same task. Furthermore, new employees can quickly master the task and become immediately effective. This will improve productivity across the entire company and make operations more efficient.

[0066] The business assistance system according to the embodiment includes an acquisition unit, a skill learning unit, an assist unit, and a procedure generation unit. The acquisition unit acquires business procedures. For example, the acquisition unit records business procedures and know-how in detail and inputs them into an AI. The acquisition unit can record business procedures in, for example, text format, video format, or audio format. The skill learning unit learns the business procedures acquired by the acquisition unit. The skill learning unit learns, for example, how to operate specific software or how to proceed with a specific business process. The skill learning unit can learn business procedures using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The assist unit provides advice in real time when other people perform the same task based on the skills learned by the skill learning unit. For example, the assist unit provides advice in real time when a new employee performs a specific task using methods such as voice assistance, text messages, or pop-up notifications. The procedure generation unit automatically generates business procedures based on the skills learned by the skill learning unit. The procedure generation unit automatically generates work procedures using, for example, an AI algorithm. This allows the work assistance system to be performed efficiently by other people, without relying on individual skills. For example, even if an employee skilled in a particular task leaves the company, another employee can perform the same task because the AI ​​retains that skill. Furthermore, new employees can quickly learn the task and become immediately effective. This improves productivity across the company and realizes work efficiency.

[0067] The acquisition unit can specifically record business procedures or know-how and input them to the AI. For example, the acquisition unit can record business procedures or know-how in detail and input them to the AI. For example, the acquisition unit can record business procedures in text format, video format, audio format, etc. For example, the acquisition unit can record business procedures in text format and input them to the AI. The acquisition unit can also record business procedures in video format and input them to the AI. The acquisition unit can also record business procedures in audio format and input them to the AI. In this way, by recording business procedures and know-how in detail, the AI ​​can learn accurately. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can record business procedures in text format and input them to the generation AI, causing the generation AI to analyze the business procedures.

[0068] The skill learning unit can learn how to operate specific software or how to proceed with a specific business process. The skill learning unit, for example, learns how to operate specific software. For example, the skill learning unit learns how to operate Excel. The skill learning unit can also learn how to operate Photoshop. The skill learning unit can also learn how to proceed with a specific business process. For example, the skill learning unit learns manufacturing line procedures. The skill learning unit can also learn customer support procedures. In this way, by learning specific software or business processes, the AI ​​can handle a wide range of tasks. Some or all of the above-mentioned processing in the skill learning unit may be performed using, or without, a generation AI. For example, the skill learning unit can input how to operate specific software into the generation AI, causing the generation AI to learn the operation method.

[0069] The assisting unit can provide advice in real time to a new employee when performing a specific task. For example, the assisting unit provides advice in real time to a new employee when performing a specific task. For example, the assisting unit provides voice assistance. The assisting unit can also provide text messages. The assisting unit can also provide pop-up notifications. This allows the new employee to quickly learn the task and perform the task efficiently. Some or all of the above-described processing in the assisting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the assisting unit can cause the generation AI to provide advice in real time to a new employee when performing a specific task.

[0070] The procedure generation unit can automatically generate business procedures. The procedure generation unit, for example, automatically generates business procedures. For example, the procedure generation unit automatically generates business procedures using an AI algorithm. The procedure generation unit can also automatically generate business procedures using a generation AI. By automatically generating business procedures, the effort required to create explanatory materials can be reduced. Some or all of the above-described processing in the procedure generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the procedure generation unit can input business procedures into the generation AI and have the generation AI automatically generate the business procedures.

[0071] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the work procedures based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the timing of acquiring the work procedures based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit delays the acquisition of the work procedures and acquires them in a relaxed state. Furthermore, if the user is concentrating, the acquisition unit can immediately acquire the work procedures and efficiently proceed with the work. Furthermore, if the user is tired, the acquisition unit can temporarily stop acquiring the work procedures and resume it after a break. This allows for efficient work execution by adjusting the timing of acquiring the work procedures according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, the generation AI. For example, the acquisition unit may input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0072] The acquisition unit can analyze the user's past work history and select the optimal acquisition method when acquiring a work procedure. For example, the acquisition unit analyzes the user's past work history and selects the optimal acquisition method when acquiring a work procedure. For example, the acquisition unit prioritizes acquiring work procedures that the user has frequently performed in the past. The acquisition unit can also select and acquire efficient procedures from the user's past work history. The acquisition unit can also avoid work procedures that the user has had difficulty with in the past and prioritize acquiring procedures that the user is good at. In this way, the optimal acquisition method can be selected by analyzing the user's past work history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's past work history data into the generation AI and have the generation AI select the optimal acquisition method.

[0073] The acquisition unit can filter the business procedures based on the user's current project and areas of interest when acquiring the business procedures. For example, the acquisition unit can prioritize acquiring business procedures related to the project the user is currently working on. The acquisition unit can also filter and acquire related business procedures based on the user's areas of interest. The acquisition unit can also acquire business procedures related to areas in which the user has previously shown interest. In this way, by filtering business procedures based on the user's current project and areas of interest, highly relevant procedures can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, or without, a generation AI. For example, the acquisition unit can input data on the user's current project and areas of interest into the generation AI and have the generation AI perform filtering.

[0074] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring the business procedures. For example, the acquisition unit selects the optimal acquisition means depending on the user's input method (voice, text, image, etc.) when acquiring the business procedures. For example, when the user uses voice input, the acquisition unit acquires the business procedures using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the business procedures using text analysis technology. Furthermore, when the user uses image input, the acquisition unit can also acquire the business procedures using image recognition technology. This enables efficient acquisition of business procedures by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit inputs the user's input data into the generation AI, causing the generation AI to select the optimal acquisition means.

[0075] The acquisition unit can estimate the user's emotions and determine the priority of the work procedures to be acquired based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of the work procedures to be acquired based on the estimated user emotions. For example, when the user is stressed, the acquisition unit prioritizes acquiring simple work procedures. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring complex work procedures. Furthermore, when the user is concentrating, the acquisition unit can prioritize acquiring important work procedures. This enables efficient work execution by determining the priority of work procedures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, the generation AI. For example, the acquisition unit can input user emotion data into the generation AI, causing the generation AI to estimate emotions and determine priorities.

[0076] When acquiring work procedures, the acquisition unit can prioritize acquiring highly relevant procedures by taking into account the user's geographical location information. For example, when acquiring work procedures, the acquisition unit prioritizes acquiring highly relevant procedures by taking into account the user's geographical location information. For example, when the user is in the office, the acquisition unit prioritizes acquiring office-related work procedures. Furthermore, when the user is on a business trip, the acquisition unit can prioritize acquiring work procedures required at the business trip destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring work procedures related to remote work. In this way, by taking into account the user's geographical location information, highly relevant work procedures can be prioritized. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit inputs the user's geographical location information to the generation AI, causing the generation AI to select highly relevant procedures.

[0077] The acquisition unit can analyze the user's social media activity when acquiring a business procedure and acquire related procedures. For example, the acquisition unit can analyze the user's social media activity when acquiring a business procedure and acquire related procedures. For example, the acquisition unit can acquire related procedures based on business content shared by the user on social media. The acquisition unit can also acquire business procedures that the user is likely to be interested in from the user's social media activity. The acquisition unit can also acquire related business procedures by referring to the activity of the user's friends on social media. In this way, related business procedures can be acquired by analyzing the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and have the generation AI select related procedures.

[0078] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring a business procedure. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring a business procedure. For example, the acquisition unit selects an optimal acquisition method based on feedback provided by the user in the past. The acquisition unit can also acquire business procedures by reflecting improvements based on the user's past feedback. The acquisition unit can also preferentially acquire business procedures that the user has previously evaluated. This allows the optimal acquisition method to be selected by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and have the generation AI customize the acquisition method.

[0079] The skill learning unit can estimate the user's emotions and adjust the expression method of the skill learning based on the estimated user emotions. For example, the skill learning unit can estimate the user's emotions and adjust the expression method of the skill learning based on the estimated user emotions. For example, if the user is relaxed, the skill learning unit can provide learning content that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the skill learning unit can provide learning content that focuses on the main points and is short. Furthermore, if the user is excited, the skill learning unit can provide learning content that adds visually stimulating effects. This enables efficient learning by adjusting the expression method of the skill learning according to the user'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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the skill learning unit can be performed using, for example, the generation AI. For example, the skill learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method.

[0080] The skill learning unit can adjust the level of detail of the learning based on the importance of the business procedure during skill learning. For example, the skill learning unit can adjust the level of detail of the learning based on the importance of the business procedure during skill learning. For example, the skill learning unit can provide detailed learning content for business procedures with high importance. The skill learning unit can also provide simplified learning content for business procedures with low importance. The skill learning unit can also adjust the progress speed of the learning based on the importance of the business procedure. This enables efficient learning by adjusting the level of detail of the learning based on the importance of the business procedure. Some or all of the above-mentioned processing in the skill learning unit can be performed using, or without, a generation AI. For example, the skill learning unit can input importance data of the business procedure into the generation AI and have the generation AI adjust the level of detail of the learning.

[0081] The skill learning unit can apply different learning algorithms depending on the task category during skill learning. For example, the skill learning unit applies different learning algorithms depending on the task category during skill learning. For example, the skill learning unit applies an interactive learning algorithm to skill learning related to software operation. The skill learning unit can also apply a simulation-based learning algorithm to skill learning related to project management. The skill learning unit can also apply an interactive learning algorithm to learning related to communication skills. This enables efficient learning by applying different learning algorithms depending on the task category. Some or all of the above-described processing in the skill learning unit may be performed using, or without, a generation AI. For example, the skill learning unit inputs task category data into the generation AI, causing the generation AI to apply an appropriate learning algorithm.

[0082] The skill learning unit can improve the accuracy of skill learning by referring to the user's past learning results. For example, the skill learning unit can improve the accuracy of skill learning by referring to the user's past learning results. For example, the skill learning unit provides learning content, including review, based on the content the user has previously learned. The skill learning unit can also focus on weak areas based on the user's past learning results. The skill learning unit can also analyze the user's past learning history and suggest an optimal learning method. By referring to the user's past learning results, the accuracy of learning is improved. Some or all of the above-described processing in the skill learning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the skill learning unit can input the user's past learning result data into the generation AI, and the generation AI can improve the accuracy of learning.

[0083] The skill learning unit can estimate the user's emotions and adjust the length of the skill learning based on the estimated user emotions. For example, the skill learning unit can estimate the user's emotions and adjust the length of the skill learning based on the estimated user emotions. For example, if the user is in a hurry, the skill learning unit can provide learning content that covers the main points in a short amount of time. Furthermore, if the user is relaxed, the skill learning unit can provide longer learning content with detailed explanations. Furthermore, if the user is excited, the skill learning unit can provide learning content with visually stimulating effects. This enables efficient learning by adjusting the length of the skill learning according to the user'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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the skill learning unit can be performed using, for example, the generation AI. For example, the skill learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the learning.

[0084] The skill learning unit can determine the learning priority based on the submission time of the business procedures during skill learning. The skill learning unit, for example, determines the learning priority based on the submission time of the business procedures during skill learning. For example, the skill learning unit prioritizes providing learning content for business procedures with an upcoming submission deadline. The skill learning unit can also postpone providing learning content for business procedures with a distant submission deadline. The skill learning unit can also adjust the learning progress speed depending on the submission time. This enables efficient learning by determining the learning priority based on the submission time of the business procedures. Some or all of the above-mentioned processing in the skill learning unit may be performed using, or without, a generation AI. For example, the skill learning unit can input submission time data for business procedures into the generation AI and have the generation AI determine the learning priority.

[0085] The skill learning unit can adjust the order of learning based on the relevance of the work procedures during skill learning. The skill learning unit, for example, adjusts the order of learning based on the relevance of the work procedures during skill learning. For example, the skill learning unit prioritizes learning of highly relevant work procedures. The skill learning unit can also postpone learning of less relevant work procedures. The skill learning unit can also adjust the speed of learning progress according to the relevance of the work procedures. This enables efficient learning by adjusting the order of learning based on the relevance of the work procedures. Some or all of the above-described processing in the skill learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the skill learning unit can input relevance data of the work procedures into the generation AI and have the generation AI adjust the order of learning.

[0086] The skill learning unit can adjust the use of technical terms in the learning according to the user's level of expertise during skill learning. For example, the skill learning unit can adjust the use of technical terms in the learning according to the user's level of expertise during skill learning. For example, the skill learning unit can provide learning content that uses a lot of technical terms to a user with a high level of expertise. The skill learning unit can also provide learning content explained in simple terms to a user with a low level of expertise. The skill learning unit can also adjust the level of detail of the learning content according to the user's level of expertise. This enables efficient learning by adjusting the use of technical terms in the learning according to the user's level of expertise. Some or all of the above-described processing in the skill learning unit can be performed using, or without, a generation AI. For example, the skill learning unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0087] The assist unit can estimate the user's emotions and adjust the assistance method based on the estimated user emotions. For example, the assist unit can estimate the user's emotions and adjust the assistance method based on the estimated user emotions. For example, if the user is nervous, the assist unit can provide assistance in a calm voice. If the user is relaxed, the assist unit can provide assistance in a cheerful voice. If the user is in a hurry, the assist unit can provide quick and concise assistance. This enables efficient assistance by adjusting the assistance method according to the user's emotions. 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-mentioned processing in the assist unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the assist unit can input the user's emotion data into the generation AI and have the generation AI adjust the assistance method.

[0088] The assisting unit can adjust the level of detail of the assistance based on the importance of the business procedure during assistance. For example, the assisting unit adjusts the level of detail of the assistance based on the importance of the business procedure during assistance. For example, the assisting unit provides detailed assistance for business procedures with high importance. The assisting unit can also provide simplified assistance for business procedures with low importance. The assisting unit can also adjust the progress speed of the assistance according to the importance of the business procedure. This enables efficient assistance by adjusting the level of detail of the assistance based on the importance of the business procedure. Some or all of the above-described processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input importance data of the business procedure into the generation AI and have the generation AI adjust the level of detail of the assistance.

[0089] The assisting unit can apply different assist algorithms depending on the task category when providing assistance. For example, the assisting unit applies different assist algorithms depending on the task category when providing assistance. For example, the assisting unit applies an interactive assist algorithm to assistance related to software operation. The assisting unit can also apply a simulation-based assist algorithm to assistance related to project management. The assisting unit can also apply an interactive assist algorithm to assistance related to communication skills. In this way, efficient assistance is possible by applying different assist algorithms depending on the task category. Some or all of the above-described processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input task category data into the generation AI and have the generation AI apply an appropriate assist algorithm.

[0090] The assisting unit can improve the accuracy of the assistance by referring to the user's past assist results when providing assistance. For example, the assisting unit can improve the accuracy of the assistance by referring to the user's past assist results when providing assistance. For example, the assisting unit provides an optimal assisting method based on the assistance content received by the user in the past. The assisting unit can also adjust the assistance content by reflecting areas for improvement based on the user's past assist results. The assisting unit can also preferentially provide assisting methods that the user has previously evaluated. This improves the accuracy of the assistance by referring to the user's past assist results. Some or all of the above-described processing in the assisting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the assisting unit can input the user's past assist result data into the generation AI and use the generation AI to improve the accuracy of the assistance.

[0091] The assist unit can estimate the user's emotions and adjust the length of the assistance based on the estimated user emotions. For example, the assist unit estimates the user's emotions and adjusts the length of the assistance based on the estimated user emotions. For example, if the user is in a hurry, the assist unit can provide brief, concise assistance. If the user is relaxed, the assist unit can provide longer assistance with detailed explanations. If the user is excited, the assist unit can provide assistance with visually stimulating effects. This enables efficient assistance by adjusting the length of the assistance according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the assist unit can be performed using, for example, the generation AI. For example, the assist unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the assistance.

[0092] The assisting unit can determine the priority of assistance based on the submission time of the business procedures when providing assistance. The assisting unit, for example, determines the priority of assistance based on the submission time of the business procedures when providing assistance. For example, the assisting unit prioritizes providing assistance content for business procedures with an upcoming submission deadline. The assisting unit can also postpone providing assistance content for business procedures with a distant submission deadline. The assisting unit can also adjust the progress speed of the assistance depending on the submission time. This enables efficient assistance by determining the priority of assistance based on the submission time of the business procedures. Some or all of the above-mentioned processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input submission time data of the business procedures into the generation AI and have the generation AI determine the priority of assistance.

[0093] The assisting unit can adjust the order of assistance based on the relevance of the work procedures during assistance. The assisting unit, for example, adjusts the order of assistance based on the relevance of the work procedures during assistance. For example, the assisting unit prioritizes assisting highly relevant work procedures. The assisting unit can also postpone assisting less relevant work procedures. The assisting unit can also adjust the speed of the assistance according to the relevance of the work procedures. This enables efficient assistance by adjusting the order of assistance based on the relevance of the work procedures. Some or all of the above-described processing in the assisting unit may be performed using, or without, a generation AI. For example, the assisting unit can input relevance data of the work procedures into the generation AI and have the generation AI adjust the order of assistance.

[0094] The assist unit can adjust the use of technical terms in the assistance depending on the user's level of expertise during assistance. For example, the assist unit can adjust the use of technical terms in the assistance depending on the user's level of expertise during assistance. For example, the assist unit can provide assistance content that uses a lot of technical terms to a user with a high level of expertise. The assist unit can also provide assistance content that explains things in simpler terms to a user with a low level of expertise. The assist unit can also adjust the level of detail of the assistance content depending on the user's level of expertise. This enables efficient assistance by adjusting the use of technical terms in the assistance depending on the user's level of expertise. Some or all of the above-described processing in the assist unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the assist unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0095] The procedure generation unit can estimate the user's emotions and adjust the procedure generation method based on the estimated user emotions. For example, the procedure generation unit estimates the user's emotions and adjusts the procedure generation method based on the estimated user emotions. For example, if the user is relaxed, the procedure generation unit generates a procedure that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the procedure generation unit can generate a procedure that emphasizes the shortest route. Furthermore, if the user is excited, the procedure generation unit can generate a procedure that adds visually stimulating effects. This enables efficient procedure generation by adjusting the procedure generation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 procedure generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the procedure generation unit can input user emotion data into the generation AI and have the generation AI adjust the procedure generation method.

[0096] The procedure generation unit can adjust the level of detail of the generated procedure based on the importance of the business procedure when generating the procedure. For example, the procedure generation unit adjusts the level of detail of the generated procedure based on the importance of the business procedure when generating the procedure. For example, the procedure generation unit generates detailed procedures for business procedures with high importance. The procedure generation unit can also generate simplified procedures for business procedures with low importance. The procedure generation unit can also adjust the speed of the generation process depending on the importance of the business procedure. In this way, adjusting the level of detail of the generated procedure based on the importance of the business procedure enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, or without, a generation AI. For example, the procedure generation unit can input importance data of the business procedure to the generation AI and have the generation AI adjust the level of detail of the generated procedure.

[0097] The procedure generation unit can apply different generation algorithms depending on the task category when generating a procedure. For example, the procedure generation unit applies different generation algorithms depending on the task category when generating a procedure. For example, the procedure generation unit applies an interactive generation algorithm when generating a procedure related to software operation. The procedure generation unit can also apply a simulation-based generation algorithm when generating a procedure related to project management. The procedure generation unit can also apply an interactive generation algorithm when generating a procedure related to communication skills. In this way, applying different generation algorithms depending on the task category enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, or without, a generation AI. For example, the procedure generation unit inputs task category data into the generation AI and causes the generation AI to apply an appropriate generation algorithm.

[0098] The procedure generation unit can improve the accuracy of procedure generation by referring to the user's past procedure generation results when generating a procedure. For example, the procedure generation unit can improve the accuracy of generation by referring to the user's past procedure generation results when generating a procedure. For example, the procedure generation unit generates an optimal procedure based on procedures previously generated by the user. The procedure generation unit can also generate a procedure by reflecting improvements from the user's past procedure generation results. The procedure generation unit can also preferentially generate procedures that the user has previously evaluated. In this way, the accuracy of generation is improved by referring to the user's past procedure generation results. Some or all of the above-mentioned processing in the procedure generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the procedure generation unit can input the user's past procedure generation result data into the generation AI and use the generation AI to improve the accuracy of generation.

[0099] The step generation unit can estimate the user's emotions and adjust the length of the step generation based on the estimated user emotions. The step generation unit, for example, estimates the user's emotions and adjusts the length of the step generation based on the estimated user emotions. For example, if the user is in a hurry, the step generation unit generates short, concise steps. Furthermore, if the user is relaxed, the step generation unit can generate longer steps with detailed explanations. Furthermore, if the user is excited, the step generation unit can generate steps with visually stimulating effects. This enables efficient step generation by adjusting the length of the step generation based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 step generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the step generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the step generation.

[0100] The procedure generation unit can determine the generation priority based on the submission time of the business procedure when generating the procedure. The procedure generation unit, for example, determines the generation priority based on the submission time of the business procedure when generating the procedure. For example, the procedure generation unit prioritizes the generation of procedures for business procedures with an upcoming submission deadline. The procedure generation unit can also postpone the generation of procedures for business procedures with a distant submission deadline. The procedure generation unit can also adjust the progress speed of procedure generation depending on the submission time. In this way, determining the generation priority based on the submission time of the business procedure enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, or without, a generation AI. For example, the procedure generation unit can input submission time data of the business procedure to the generation AI and have the generation AI determine the generation priority.

[0101] The procedure generation unit can adjust the order of generation based on the relevance of business procedures when generating procedures. The procedure generation unit, for example, adjusts the order of generation based on the relevance of business procedures when generating procedures. For example, the procedure generation unit prioritizes the generation of highly relevant business procedures. The procedure generation unit can also postpone the generation of less relevant business procedures. The procedure generation unit can also adjust the speed of generation progress according to the relevance of business procedures. In this way, adjusting the order of generation based on the relevance of business procedures enables efficient procedure generation. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure generation unit can input relevance data of business procedures into the generation AI and have the generation AI adjust the order of generation.

[0102] The procedure generation unit can adjust the use of technical terminology in the generated procedure according to the user's level of expertise when generating the procedure. For example, the procedure generation unit adjusts the use of technical terminology in the generated procedure according to the user's level of expertise when generating the procedure. For example, the procedure generation unit generates a procedure that uses a lot of technical terminology for a user with a high level of expertise. The procedure generation unit can also generate a procedure that explains in simple terms for a user with a low level of expertise. The procedure generation unit can also adjust the level of detail of the procedure according to the user's level of expertise. This enables efficient procedure generation by adjusting the use of technical terminology in the generated procedure according to the user's level of expertise. Some or all of the above-mentioned processing in the procedure generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, skill learning unit, assist unit, and procedure generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire a task procedure using the camera 42 or microphone 38B of the smart device 14 and transmit it to the data processing device 12 via the control unit 46A. The skill learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the task procedure transmitted from the acquisition unit. The assist unit is realized, for example, by the control unit 46A of the smart device 14 and provides voice assistance or text messages in real time. The procedure generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a task procedure based on the learned skills. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, skill learning unit, assist unit, and procedure generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire a task procedure using the camera 42 or microphone 238 of the smart glasses 214 and transmit the task procedure to the data processing device 12 via the control unit 46A. The skill learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the task procedure transmitted from the acquisition unit. The assist unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides voice assistance or text messages in real time. The procedure generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a task procedure based on the learned skills. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, skill learning unit, assist unit, and procedure generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit can acquire a work procedure using the camera 42 or microphone 238 of the headset-type terminal 314 and transmit the acquired work procedure to the data processing device 12 by the control unit 46A. The skill learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the work procedure transmitted from the acquisition unit. The assist unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides voice assistance and text messages in real time. The procedure generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a work procedure based on the learned skills. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, skill learning unit, assist unit, and procedure generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire a task procedure using the camera 42 or microphone 238 of the robot 414 and transmit the task procedure to the data processing device 12 by the control unit 46A. The skill learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the task procedure transmitted from the acquisition unit. The assist unit is realized, for example, by the control unit 46A of the robot 414 and provides voice assistance or text messages in real time. The procedure generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a task procedure based on the learned skills.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The acquisition unit can also acquire the user's biometric information and adjust the timing of acquiring the work procedures. For example, the acquisition unit can monitor the user's heart rate and skin temperature and delay the acquisition of the work procedures when the user's stress level is high. The acquisition unit can also analyze the user's brain waves and acquire the work procedures when the user is highly focused. Furthermore, the acquisition unit can analyze the user's breathing pattern and acquire the work procedures when the user is relaxed. This allows for efficient work performance by adjusting the timing of acquiring the work procedures based on the user's biometric information.

[0105] The skill learning unit can also customize learning methods according to the user's learning style. For example, the skill learning unit can provide learning content that makes extensive use of videos and illustrations for visual learners. It can also provide learning content in the form of audio guides or podcasts for auditory learners. It can also provide interactive simulations and practical exercises for experiential learners. This allows for efficient skill acquisition by customizing learning methods according to the user's learning style.

[0106] The assist unit can also estimate the user's emotions and personalize the content of the assistance based on the estimated user's emotions. For example, if the user feels anxious, the assist unit can provide an encouraging message. If the user feels confident, the assist unit can suggest a challenging task. Furthermore, if the user feels tired, the assist unit can provide advice encouraging the user to take a break. This allows for efficient work performance by personalizing the content of the assistance according to the user's emotions.

[0107] The procedure generation unit can also analyze the user's past work performance and generate an optimal procedure. For example, the procedure generation unit can generate procedures with priority given to procedures in which the user has performed well in the past. It can also generate procedures that avoid procedures that the user has had difficulty with in the past. Furthermore, it can also suggest efficient procedures based on the user's past work performance data. This makes it possible to generate an optimal procedure by taking the user's past work performance into consideration.

[0108] The acquisition unit can also estimate the user's emotions and adjust the difficulty of the work procedures to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring easy work procedures. Also, if the user is relaxed, the acquisition unit can prioritize acquiring difficult work procedures. Furthermore, if the user is concentrating, the acquisition unit can prioritize acquiring important work procedures. In this way, the difficulty of the work procedures can be adjusted according to the user's emotions, enabling efficient work execution.

[0109] The skill learning unit can also estimate the user's emotions and adjust the learning progress speed based on the estimated user emotions. For example, if the user is relaxed, the skill learning unit can provide learning content at a leisurely pace. If the user is in a hurry, the skill learning unit can provide learning content that focuses on the main points in a short amount of time. Furthermore, if the user is excited, the skill learning unit can provide learning content with visually stimulating effects. This allows for efficient learning by adjusting the learning progress speed according to the user's emotions.

[0110] The assist unit can also analyze the user's past assist history and provide the optimal assist method. For example, the assist unit can prioritize and provide assist methods that have been effective for the user in the past. It can also avoid assist methods that the user has been dissatisfied with in the past. Furthermore, it can provide customized assistance content based on the user's past assist history. This makes it possible to provide the optimal assistance method by taking the user's past assist history into consideration.

[0111] The procedure generation unit can also estimate the user's emotions and adjust the order of procedure generation based on the estimated user's emotions. For example, if the user is relaxed, the procedure generation unit can generate complex procedures first. If the user is in a hurry, the procedure generation unit can also generate simple procedures first. Furthermore, if the user is excited, the procedure generation unit can also generate visually stimulating procedures first. In this way, by adjusting the order of procedure generation according to the user's emotions, efficient procedure generation is possible.

[0112] The acquisition unit can also monitor the user's current work environment and select the optimal acquisition method. For example, if the user is in a quiet environment, the acquisition unit can prioritize voice input. If the user is in a noisy environment, the acquisition unit can also prioritize text input. Furthermore, if the user is on the move, the work procedures can be acquired with a simple touch operation. This allows the efficient acquisition of work procedures by selecting the optimal acquisition method according to the user's current work environment.

[0113] The skill learning unit can also estimate the user's emotions and adjust the feedback of the learning content based on the estimated user's emotions. For example, if the user feels anxious, the skill learning unit can provide more positive feedback. If the user feels confident, the skill learning unit can provide more constructive feedback. Furthermore, if the user feels tired, the skill learning unit can provide shorter feedback. This allows for efficient learning by adjusting the feedback of the learning content according to the user's emotions.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The acquisition unit acquires business procedures. For example, the acquisition unit records business procedures and know-how in detail and inputs them into the AI. The acquisition unit can record business procedures in text format, video format, audio format, etc. Step 2: The skill learning unit learns the business procedures acquired by the acquisition unit. For example, the skill learning unit learns how to operate specific software or how to proceed with a specific business process. The skill learning unit can learn the business procedures using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The Assistance Department provides real-time advice to other people when performing the same tasks based on the skills learned by the Skill Learning Department. For example, when a new employee performs a specific task, the Assistance Department provides advice in real time using voice assistance, text messages, pop-up notifications, etc. Step 4: The procedure generation unit automatically generates work procedures based on the skills learned by the skill learning unit. The procedure generation unit automatically generates work procedures using, for example, an AI algorithm. This allows the work assistance system to be independent of individual skills and allows other people to perform the same work efficiently.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 AI 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 AI 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 AI 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 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. an acquisition unit that acquires business procedures; a skill learning unit that learns the business procedures acquired by the acquisition unit; an assisting unit that provides advice in real time when another person performs the same task based on the skills learned by the skill learning unit; a procedure generation unit that automatically generates a business procedure based on the skills learned by the skill learning unit. A system characterized by:

2. The acquisition unit Record specific business procedures or know-how and input them into AI The system of claim 1 .

3. The skill learning unit Learning how to operate a particular piece of software or perform a particular business process The system of claim 1 .

4. The assist unit is Providing real-time guidance to new employees on specific tasks The system of claim 1 .

5. The procedure generation unit Automatically generate business procedures The system of claim 1 .

6. The acquisition unit Estimates user emotions and adjusts the timing of acquiring work procedures based on the estimated user emotions. The system of claim 1 .

7. The acquisition unit When acquiring business procedures, analyze the user's past work history and select the optimal acquisition method. The system of claim 1 .

8. The acquisition unit Filtering business procedures based on the user's current project or area of ​​interest The system of claim 1 .

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