System

Abstract

An object of the system according to the embodiment is to provide a manual customized according to the characteristics of the user and the work content and to improve the work efficiency.SOLUTION: A system according to an embodiment includes a manual generation unit, a feedback collection unit, and a business improvement proposal unit. The manual generation unit customizes the manual based on the characteristics of the user and the business contents. The feedback collection unit collects feedback of a work result using the manual customized by the manual generation unit. The business improvement proposal unit analyzes the feedback collected by the feedback collection unit and proposes business improvement.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] In conventional techniques, there is a risk of work efficiency decreasing because general-purpose manuals do not suit the characteristics and work contents of a wide variety of users.

[0005] The system according to the embodiment aims to provide a manual customized according to the characteristics and work content of a user, thereby improving work efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a manual generation unit, a feedback collection unit, and a business improvement proposal unit. The manual generation unit customizes the manual based on the characteristics of the user and the business content. The feedback collection unit collects feedback on the results of business operations using the manual customized by the manual generation unit. The business improvement proposal unit analyzes the feedback collected by the feedback collection unit and proposes business improvements. [Effects of the Invention]

[0007] The system according to the embodiment can provide a manual customized according to the characteristics and work content of the user, thereby improving work efficiency. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 business improvement system according to an embodiment of the present invention uses a generation AI to customize a single manual for a wide variety of workers and proposes business improvements based on feedback on the results of work using that manual. This allows the business improvement system to provide an optimal manual tailored to the characteristics and work content of the user, evaluate its effectiveness, and implement business improvements.

[0029] A business improvement system according to an embodiment includes a manual generation unit, a feedback collection unit, and a business improvement suggestion unit. The manual generation unit customizes a manual based on a user's characteristics and work content. For example, the generation AI generates a manual that omits detailed procedures for experienced employees and includes more detailed explanations for beginners based on information about the user's characteristics and work content. The generation AI uses a text generation AI (e.g., LLM) to generate a customized manual according to the user's characteristics and work content. The generation AI can also use a multimodal generation AI to generate a manual according to the user's characteristics and work content. For example, the generation AI inputs information about the user's characteristics and work content as prompts and generates a customized manual based on the prompts. The feedback collection unit collects feedback on the results of work using the customized manual. For example, it collects information about the results of employees performing their work according to the manual and opinions about the ease of use of the manual. The feedback collection unit can collect feedback, for example, in the form of a questionnaire. The feedback collection unit can also automatically collect data on work results. For example, it collects data on work outcomes and efficiency. The business improvement proposal unit analyzes the collected feedback and proposes business improvements. For example, if the feedback indicates that a particular procedure is difficult to understand, the unit proposes improvement plans to make the procedure easier to understand. The business improvement proposal unit analyzes the feedback using, for example, a generation AI and proposes business improvements. The generation AI can also propose new procedures that improve business efficiency and reduce errors based on the feedback data. This allows the business improvement system according to the embodiment to provide an optimal manual based on the user's characteristics and business content, evaluate its effectiveness, and improve business operations. For example, the output unit displays business improvement proposals to the user via a web application or mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The manual generation unit can analyze a user's past work history and performance data to generate a manual optimized for each individual user. For example, the generation AI analyzes a user's past work history and extracts success and failure patterns for a specific task. Based on this, the manual generation unit generates a manual that includes optimal procedures and points to note for the user. The manual generation unit also analyzes the user's performance data and customizes the manual based on the efficiency of the task and the quality of the deliverables. For example, the generation AI inputs the user's past work history and performance data as prompts and generates a customized manual based on those prompts. This makes it possible to provide an optimal manual based on the user's past work history and performance data.

[0031] The manual generation unit can analyze the user's learning style and provide the manual in a format that suits it. For example, the manual generation unit uses a generation AI to analyze the user's learning style and provide a manual that makes extensive use of illustrations and videos to users who prefer visual learning. For example, procedures may be shown in video to make them easier to understand visually. The manual generation unit also provides a manual that includes audio guidance to users who prefer auditory learning. For example, procedures may be explained audio to make them easier to understand auditorily. The manual generation unit also provides a manual that includes interactive content to users who prefer tactile learning. For example, procedures may be learned by actually operating the operations. This makes it possible to provide a manual in the optimal format according to the user's learning style.

[0032] The manual generation unit automatically generates manuals that correspond to different languages ​​and cultures, making them usable in a global corporate environment. For example, the manual generation unit uses a generation AI to automatically generate manuals that correspond to different languages. For example, it provides manuals in multiple languages, such as English, Japanese, and Spanish, making them usable in a global corporate environment. The manual generation unit also automatically generates manuals that correspond to different cultures. For example, it provides manuals that correspond to the culture of each country or corporate culture. This makes it possible to provide manuals that correspond to different languages ​​and cultures.

[0033] The manual generation unit can reflect user feedback in real time and update the contents of the manual successively. For example, the generation AI in the manual generation unit collects user feedback in real time and updates the contents of the manual successively. For example, it immediately reflects errors and areas for improvement pointed out by users. The manual generation unit also modifies procedures or adds new procedures based on feedback. For example, it modifies procedures to reflect feedback and provides a manual containing the latest information and optimal procedures. This allows the manual contents to be updated successively and reflect user feedback in real time.

[0034] The feedback collection unit can analyze the feedback data and quantitatively evaluate how effective a specific business procedure is. In the feedback collection unit, for example, a generation AI analyzes the feedback data and quantitatively evaluates the effectiveness of a specific business procedure. For example, it calculates the success rate and error rate for each procedure. The feedback collection unit also sets indicators for evaluating the effectiveness of the business procedure and performs evaluations based on those indicators. For example, it sets indicators for evaluating the efficiency of work and the quality of the deliverables, and evaluates the effectiveness of the procedure based on those indicators. This makes it possible to quantitatively evaluate the effectiveness of a specific business procedure.

[0035] The feedback collection unit can develop an algorithm that automatically identifies the factors that lead to success or failure in a business based on feedback data. For example, the feedback collection unit develops an algorithm that uses a generation AI to analyze feedback data and automatically identify the factors that lead to success or failure in a business. For example, it identifies successful and unsuccessful procedures. The feedback collection unit also sets indicators for identifying the factors that lead to success or failure in a business and performs evaluations based on those indicators. For example, it sets indicators to evaluate the efficiency of the business and the quality of the deliverables, and identifies the factors that lead to success or failure based on those indicators. This makes it possible to automatically identify the factors that lead to success or failure in a business.

[0036] The feedback collection unit can integrate the feedback data with other business data and perform a comprehensive business evaluation. For example, the generation AI can integrate the feedback data with sales data and perform a comprehensive business evaluation. For example, it can evaluate the impact of a specific procedure on sales. The feedback collection unit can also integrate the feedback data with customer satisfaction data and perform a business evaluation. For example, it can evaluate the impact of a specific procedure on customer satisfaction. This allows the feedback data to be integrated with other business data and perform a comprehensive business evaluation.

[0037] The feedback collection unit can analyze the relationships between different business processes based on the feedback data and identify the factors that affect each other. In the feedback collection unit, for example, a generation AI analyzes the feedback data and identifies the relationships between different business processes. For example, it evaluates the impact that a specific procedure has on other procedures. The feedback collection unit also sets indicators for evaluating the relationships between business processes and performs evaluations based on those indicators. For example, it sets indicators for evaluating the efficiency of work and the quality of deliverables, and identifies the relationships between business processes based on those indicators. This makes it possible to analyze the relationships between different business processes and identify the factors that affect each other.

[0038] The business improvement proposal department can analyze feedback data, identify bottlenecks in business processes, and propose specific improvement proposals to eliminate them. For example, the business improvement proposal department uses a generation AI to analyze feedback data and identify bottlenecks in business processes. For example, if a specific procedure is reducing overall efficiency, the department will propose specific proposals to improve that procedure. The business improvement proposal department also sets indicators to eliminate bottlenecks and proposes improvement proposals based on those indicators. For example, the department sets indicators to evaluate the efficiency of work and the quality of deliverables, identifies bottlenecks based on those indicators, and proposes improvement proposals. This makes it possible to identify bottlenecks in business processes and propose specific improvement proposals.

[0039] When proposing business improvements, the Business Improvement Proposal Department can develop an algorithm that learns the effects of past improvement proposals and makes more effective proposals. For example, the Business Improvement Proposal Department develops an algorithm in which a generative AI learns the effects of past improvement proposals and makes more effective proposals. For example, it extracts patterns of successful improvement proposals based on past data. The Business Improvement Proposal Department also sets indicators to evaluate the effectiveness of improvement proposals and makes evaluations based on those indicators. For example, it sets indicators to evaluate the efficiency of work and the quality of deliverables, learns the effects of improvement proposals based on those indicators, and makes more effective proposals. This makes it possible to learn the effects of past improvement proposals and make more effective proposals.

[0040] The Business Improvement Proposal Department can collect best practices from different industries and business processes and make business improvement proposals based on them. For example, the Business Improvement Proposal Department uses generative AI to collect best practices from different industries and make business improvement proposals based on them. For example, it applies methods that have been successful in other industries to its own company's business operations. The Business Improvement Proposal Department also collects best practices from business processes and makes proposals based on them. For example, it collects methods to improve business efficiency and the quality of deliverables and makes proposals based on them. This makes it possible to collect best practices from different industries and business processes and make business improvement proposals based on them.

[0041] When making business improvement proposals, the business improvement proposal department can take into account the user's individual skill set and career path and make individually optimized proposals. For example, the business improvement proposal department uses a generation AI to analyze the user's skill set and make business improvement proposals based on that. For example, for a user with specific skills, the business improvement proposal department makes improvement proposals that make use of those skills. The business improvement proposal department also takes into account the user's career path when making proposals. For example, the business improvement proposal department takes into account the user's promotion opportunities and job transitions and makes proposals that are in line with their career path. This makes it possible to make individually optimized proposals that take into account the user's individual skill set and career path.

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

[0043] The business improvement system can also include a health monitoring unit that monitors the user's health condition and makes business improvement proposals based on the user's health condition. For example, the system collects the user's heart rate and sleep data, and if the user's health condition is poor, makes proposals to reduce the user's workload. The health monitoring unit can also analyze the user's diet and exercise data and make proposals to promote healthy lifestyle habits. This allows business improvement proposals to be made that take the user's health condition into consideration.

[0044] The business improvement system can also include a skill analysis unit that analyzes the user's skill set and proposes a training program for skill improvement. For example, it evaluates the user's current skill level and proposes a training program to improve the necessary skills. The skill analysis unit can also consider the user's career path and propose a training program to meet future skill needs. This makes it possible to make business improvement proposals that support the user's skill improvement.

[0045] The business improvement system can also include an environment monitoring unit that monitors the user's work environment and provides the optimal work environment. For example, it can monitor the temperature and lighting in the user's workspace to provide the optimal environment. The environment monitoring unit can also analyze the user's work posture and desk layout to suggest a healthy work environment. This makes it possible to make business improvement suggestions that optimize the user's work environment.

[0046] The business improvement system may further include a performance analysis unit that analyzes the user's business performance and makes specific proposals for improving performance. For example, it may analyze the user's business performance data and propose efficient business procedures. The performance analysis unit may also set indicators for evaluating the user's business performance and make evaluations based on those indicators. This allows specific proposals to be made for improving the user's business performance.

[0047] The business improvement system can also include a process analysis unit that analyzes the user's business process and optimizes it. For example, it can analyze the user's business process data and propose an efficient process. The process analysis unit can also set indicators for evaluating the optimization of the business process and perform evaluations based on those indicators. This allows it to make specific proposals for optimizing the user's business process.

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

[0049] Step 1: The manual generation unit customizes the manual based on the user's characteristics and work content. For example, the generation AI generates a manual that omits detailed procedures for experienced employees and includes more detailed explanations for beginners based on information about the user's characteristics and work content. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate a customized manual based on the user's characteristics and work content. The generation AI inputs information about the user's characteristics and work content as prompts and generates a customized manual based on those prompts. Step 2: The feedback collection unit collects feedback on the results of work using the customized manual. For example, it collects feedback on the results of employees performing their work according to the manual and their opinions on the ease of use of the manual. The feedback collection unit can collect feedback in the form of a questionnaire. It can also automatically collect data on work results. For example, it can collect data on work outcomes and efficiency. Step 3: The Business Improvement Proposal Department analyzes the collected feedback and makes proposals for business improvements. For example, if the feedback indicates that a particular procedure is difficult to understand, the department will propose improvements to make that procedure easier to understand. The Business Improvement Proposal Department uses a generation AI to analyze the feedback and make proposals for business improvements. Based on the feedback data, the generation AI can also propose new procedures that will lead to more efficient work and reduced errors.

[0050] (Example 2) The business improvement system according to an embodiment of the present invention uses a generation AI to customize a single manual for a wide variety of workers and proposes business improvements based on feedback on the results of work using that manual. This allows the business improvement system to provide an optimal manual tailored to the characteristics and work content of the user, evaluate its effectiveness, and implement business improvements.

[0051] A business improvement system according to an embodiment includes a manual generation unit, a feedback collection unit, and a business improvement suggestion unit. The manual generation unit customizes a manual based on a user's characteristics and work content. For example, the generation AI generates a manual that omits detailed procedures for experienced employees and includes more detailed explanations for beginners based on information about the user's characteristics and work content. The generation AI uses a text generation AI (e.g., LLM) to generate a customized manual according to the user's characteristics and work content. The generation AI can also use a multimodal generation AI to generate a manual according to the user's characteristics and work content. For example, the generation AI inputs information about the user's characteristics and work content as prompts and generates a customized manual based on the prompts. The feedback collection unit collects feedback on the results of work using the customized manual. For example, it collects information about the results of employees performing their work according to the manual and opinions about the ease of use of the manual. The feedback collection unit can collect feedback, for example, in the form of a questionnaire. The feedback collection unit can also automatically collect data on work results. For example, it collects data on work outcomes and efficiency. The business improvement proposal unit analyzes the collected feedback and proposes business improvements. For example, if the feedback indicates that a particular procedure is difficult to understand, the unit proposes improvement plans to make the procedure easier to understand. The business improvement proposal unit analyzes the feedback using, for example, a generation AI and proposes business improvements. The generation AI can also propose new procedures that improve business efficiency and reduce errors based on the feedback data. This allows the business improvement system according to the embodiment to provide an optimal manual based on the user's characteristics and business content, evaluate its effectiveness, and improve business operations. For example, the output unit displays business improvement proposals to the user via a web application or mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0052] The manual generation unit can analyze a user's past work history and performance data to generate a manual optimized for each individual user. For example, the generation AI analyzes a user's past work history and extracts success and failure patterns for a specific task. Based on this, the manual generation unit generates a manual that includes optimal procedures and points to note for the user. The manual generation unit also analyzes the user's performance data and customizes the manual based on the efficiency of the task and the quality of the deliverables. For example, the generation AI inputs the user's past work history and performance data as prompts and generates a customized manual based on those prompts. This makes it possible to provide an optimal manual based on the user's past work history and performance data.

[0053] The manual generation unit can analyze the user's learning style and provide the manual in a format that suits it. For example, the manual generation unit uses a generation AI to analyze the user's learning style and provide a manual that makes extensive use of illustrations and videos to users who prefer visual learning. For example, procedures may be shown in video to make them easier to understand visually. The manual generation unit also provides a manual that includes audio guidance to users who prefer auditory learning. For example, procedures may be explained audio to make them easier to understand auditorily. The manual generation unit also provides a manual that includes interactive content to users who prefer tactile learning. For example, procedures may be learned by actually operating the operations. This makes it possible to provide a manual in the optimal format according to the user's learning style.

[0054] The manual generation unit can use the emotion estimation function to evaluate the user's current emotional state in real time and generate a manual that provides information in a less stressful manner. The manual generation unit, for example, uses the emotion estimation function to analyze the user's facial expressions and vocal tone to evaluate the emotional state in real time. For example, if the user is feeling stressed, it provides simple and easy-to-understand instructions. The manual generation unit also collects the user's biometric data (heart rate and electrodermal activity) using a sensor and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations and provides information in a less stressful manner. This makes it possible to provide information in a less stressful manner according to the user's emotional state.

[0055] The manual generation unit automatically generates manuals that correspond to different languages ​​and cultures, making them usable in a global corporate environment. For example, the manual generation unit uses a generation AI to automatically generate manuals that correspond to different languages. For example, it provides manuals in multiple languages, such as English, Japanese, and Spanish, making them usable in a global corporate environment. The manual generation unit also automatically generates manuals that correspond to different cultures. For example, it provides manuals that correspond to the culture of each country or corporate culture. This makes it possible to provide manuals that correspond to different languages ​​and cultures.

[0056] The manual generation unit can reflect user feedback in real time and update the contents of the manual successively. For example, the generation AI in the manual generation unit collects user feedback in real time and updates the contents of the manual successively. For example, it immediately reflects errors and areas for improvement pointed out by users. The manual generation unit also modifies procedures or adds new procedures based on feedback. For example, it modifies procedures to reflect feedback and provides a manual containing the latest information and optimal procedures. This allows the manual contents to be updated successively and reflect user feedback in real time.

[0057] The manual generation unit uses the emotion estimation function to monitor the emotional reactions of the user when using the manual, and can customize the manual to elicit positive emotions. The manual generation unit, for example, uses the emotion estimation function to monitor the emotional reactions of the user when using the manual in real time. For example, if the user shows positive emotions, the manual generation unit emphasizes the content. The manual generation unit also customizes the content of the manual based on the user's emotional reactions. For example, if the user shows negative emotions, the manual generation unit simplifies the steps and provides information to elicit positive emotions. In this way, the user's emotional reactions can be monitored and customization can be performed to elicit positive emotions.

[0058] The feedback collection unit can analyze the feedback data and quantitatively evaluate how effective a specific business procedure is. In the feedback collection unit, for example, a generation AI analyzes the feedback data and quantitatively evaluates the effectiveness of a specific business procedure. For example, it calculates the success rate and error rate for each procedure. The feedback collection unit also sets indicators for evaluating the effectiveness of the business procedure and performs evaluations based on those indicators. For example, it sets indicators for evaluating the efficiency of work and the quality of the deliverables, and evaluates the effectiveness of the procedure based on those indicators. This makes it possible to quantitatively evaluate the effectiveness of a specific business procedure.

[0059] The feedback collection unit can develop an algorithm that automatically identifies the factors that lead to success or failure in a business based on feedback data. For example, the feedback collection unit develops an algorithm that uses a generation AI to analyze feedback data and automatically identify the factors that lead to success or failure in a business. For example, it identifies successful and unsuccessful procedures. The feedback collection unit also sets indicators for identifying the factors that lead to success or failure in a business and performs evaluations based on those indicators. For example, it sets indicators to evaluate the efficiency of the business and the quality of the deliverables, and identifies the factors that lead to success or failure based on those indicators. This makes it possible to automatically identify the factors that lead to success or failure in a business.

[0060] The feedback collection unit can use the emotion estimation function to evaluate the user's emotional state when providing feedback and suggest a feedback collection method that involves fewer negative emotions. The feedback collection unit, for example, uses the emotion estimation function to evaluate the user's emotional state when providing feedback in real time. For example, if the user is feeling stressed, the feedback collection unit adjusts the format of the feedback. The feedback collection unit also suggests a feedback collection method based on the user's emotional state. For example, if the user shows negative emotions, the feedback collection unit suggests a method to simplify the content of the feedback and elicit positive emotions. This makes it possible to suggest a feedback collection method that involves fewer negative emotions.

[0061] The feedback collection unit can integrate the feedback data with other business data and perform a comprehensive business evaluation. For example, the generation AI can integrate the feedback data with sales data and perform a comprehensive business evaluation. For example, it can evaluate the impact of a specific procedure on sales. The feedback collection unit can also integrate the feedback data with customer satisfaction data and perform a business evaluation. For example, it can evaluate the impact of a specific procedure on customer satisfaction. This allows the feedback data to be integrated with other business data and perform a comprehensive business evaluation.

[0062] The feedback collection unit can analyze the relationships between different business processes based on the feedback data and identify the factors that affect each other. In the feedback collection unit, for example, a generation AI analyzes the feedback data and identifies the relationships between different business processes. For example, it evaluates the impact that a specific procedure has on other procedures. The feedback collection unit also sets indicators for evaluating the relationships between business processes and performs evaluations based on those indicators. For example, it sets indicators for evaluating the efficiency of work and the quality of deliverables, and identifies the relationships between business processes based on those indicators. This makes it possible to analyze the relationships between different business processes and identify the factors that affect each other.

[0063] The feedback collection unit can monitor the user's emotional reaction in real time when providing feedback using the emotion estimation function and suggest a method for promoting positive feedback. The feedback collection unit, for example, monitors the user's emotional reaction in real time when providing feedback using the emotion estimation function. For example, if the user shows positive emotion, the feedback collection unit emphasizes that content. The feedback collection unit also adjusts the content of the feedback based on the user's emotional reaction. For example, if the user shows negative emotion, the feedback collection unit shortens the content of the feedback and suggests a method for eliciting positive emotion. This makes it possible to suggest a method for promoting positive feedback.

[0064] The business improvement proposal department can analyze feedback data, identify bottlenecks in business processes, and propose specific improvement proposals to eliminate them. For example, the business improvement proposal department uses a generation AI to analyze feedback data and identify bottlenecks in business processes. For example, if a specific procedure is reducing overall efficiency, the department will propose specific proposals to improve that procedure. The business improvement proposal department also sets indicators to eliminate bottlenecks and proposes improvement proposals based on those indicators. For example, the department sets indicators to evaluate the efficiency of work and the quality of deliverables, identifies bottlenecks based on those indicators, and proposes improvement proposals. This makes it possible to identify bottlenecks in business processes and propose specific improvement proposals.

[0065] When proposing business improvements, the Business Improvement Proposal Department can develop an algorithm that learns the effects of past improvement proposals and makes more effective proposals. For example, the Business Improvement Proposal Department develops an algorithm in which a generative AI learns the effects of past improvement proposals and makes more effective proposals. For example, it extracts patterns of successful improvement proposals based on past data. The Business Improvement Proposal Department also sets indicators to evaluate the effectiveness of improvement proposals and makes evaluations based on those indicators. For example, it sets indicators to evaluate the efficiency of work and the quality of deliverables, learns the effects of improvement proposals based on those indicators, and makes more effective proposals. This makes it possible to learn the effects of past improvement proposals and make more effective proposals.

[0066] The business improvement suggestion unit uses the emotion estimation function to make business improvement suggestions that take into account the user's emotional state and present the suggestions in a format that is easy to accept. The business improvement suggestion unit, for example, uses the emotion estimation function to evaluate the user's emotional state in real time and make business improvement suggestions. For example, if the user is feeling stressed, the business improvement suggestion unit makes simple and easy-to-understand suggestions. The business improvement suggestion unit also adjusts the content of the suggestions based on the user's emotional state. For example, if the user expresses positive emotions, the content is emphasized and the suggestions are presented in a format that is easy to accept. In this way, business improvement suggestions that take into account the user's emotional state can be made and presented in a format that is easy to accept.

[0067] The Business Improvement Proposal Department can collect best practices from different industries and business processes and make business improvement proposals based on them. For example, the Business Improvement Proposal Department uses generative AI to collect best practices from different industries and make business improvement proposals based on them. For example, it applies methods that have been successful in other industries to its own company's business operations. The Business Improvement Proposal Department also collects best practices from business processes and makes proposals based on them. For example, it collects methods to improve business efficiency and the quality of deliverables and makes proposals based on them. This makes it possible to collect best practices from different industries and business processes and make business improvement proposals based on them.

[0068] When making business improvement proposals, the business improvement proposal department can take into account the user's individual skill set and career path and make individually optimized proposals. For example, the business improvement proposal department uses a generation AI to analyze the user's skill set and make business improvement proposals based on that. For example, for a user with specific skills, the business improvement proposal department makes improvement proposals that make use of those skills. The business improvement proposal department also takes into account the user's career path when making proposals. For example, the business improvement proposal department takes into account the user's promotion opportunities and job transitions and makes proposals that are in line with their career path. This makes it possible to make individually optimized proposals that take into account the user's individual skill set and career path.

[0069] The business improvement suggestion unit uses the emotion estimation function to monitor the user's emotional reaction to the business improvement proposal in real time, thereby continuously improving the likelihood of the proposal being accepted. The business improvement suggestion unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the business improvement proposal in real time. For example, if the user expresses positive emotion, the content of the proposal is emphasized. The business improvement suggestion unit also adjusts the content of the proposal based on the user's emotional reaction. For example, if the user expresses negative emotion, the content of the proposal is simplified and a method for eliciting positive emotion is suggested. In this way, the user's emotional reaction to the business improvement proposal can be monitored in real time, thereby continuously improving the likelihood of the proposal being accepted.

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

[0071] The business improvement system can also include a health monitoring unit that monitors the user's health condition and makes business improvement proposals based on the user's health condition. For example, the system collects the user's heart rate and sleep data, and if the user's health condition is poor, makes proposals to reduce the user's workload. The health monitoring unit can also analyze the user's diet and exercise data and make proposals to promote healthy lifestyle habits. This allows business improvement proposals to be made that take the user's health condition into consideration.

[0072] The business improvement system can also include a skill analysis unit that analyzes the user's skill set and proposes a training program for skill improvement. For example, it evaluates the user's current skill level and proposes a training program to improve the necessary skills. The skill analysis unit can also consider the user's career path and propose a training program to meet future skill needs. This makes it possible to make business improvement proposals that support the user's skill improvement.

[0073] The business improvement system can also include an environment monitoring unit that monitors the user's work environment and provides the optimal work environment. For example, it can monitor the temperature and lighting in the user's workspace to provide the optimal environment. The environment monitoring unit can also analyze the user's work posture and desk layout to suggest a healthy work environment. This makes it possible to make business improvement suggestions that optimize the user's work environment.

[0074] The business improvement system can also include a priority adjustment unit that estimates the user's emotions and adjusts the priority of tasks based on the estimated emotions. For example, if the user is feeling stressed, it can postpone less important tasks and prioritize tasks that allow for relaxation. The priority adjustment unit can also adjust the work schedule based on the user's emotional state and make suggestions for reducing stress. This makes it possible to adjust the priority of tasks according to the user's emotional state.

[0075] The business improvement system may further include a feedback adjustment unit that estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the user expresses positive emotions, detailed feedback may be provided, and if the user expresses negative emotions, brief, positive feedback may be provided. The feedback adjustment unit may also adjust the timing of the feedback based on the user's emotional state, providing feedback at the optimal timing. This makes it possible to provide feedback that is appropriate for the user's emotional state.

[0076] The business improvement system can also include a progress monitoring unit that estimates the user's emotions and monitors the progress of work based on the estimated emotions. For example, if the user is feeling stressed, the progress of work can be slowed down to provide a relaxing environment. The progress monitoring unit can also adjust the progress of work based on the user's emotional state and make suggestions for efficiently progressing with work. This makes it possible to monitor the progress of work according to the user's emotional state.

[0077] The business improvement system can also include a load adjustment unit that estimates the user's emotions and adjusts the work load based on the estimated emotions. For example, if the user is feeling stressed, the system can reduce the work load and provide a more relaxing work environment. The load adjustment unit can also adjust the work load based on the user's emotional state and make suggestions for efficiently progressing with work. This allows the work load to be adjusted according to the user's emotional state.

[0078] The work improvement system can also include a motivation improvement unit that estimates the user's emotions and improves work motivation based on the estimated emotions. For example, if the user shows positive emotions, it provides work to maintain those emotions, and if the user shows negative emotions, it provides work to improve motivation. The motivation improvement unit can also adjust the content of work based on the user's emotional state and make suggestions to improve motivation. This makes it possible to improve work motivation according to the user's emotional state.

[0079] The business improvement system may further include a performance analysis unit that analyzes the user's business performance and makes specific proposals for improving performance. For example, it may analyze the user's business performance data and propose efficient business procedures. The performance analysis unit may also set indicators for evaluating the user's business performance and make evaluations based on those indicators. This allows specific proposals to be made for improving the user's business performance.

[0080] The business improvement system can also include a process analysis unit that analyzes the user's business process and optimizes it. For example, it can analyze the user's business process data and propose an efficient process. The process analysis unit can also set indicators for evaluating the optimization of the business process and perform evaluations based on those indicators. This allows it to make specific proposals for optimizing the user's business process.

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

[0082] Step 1: The manual generation unit customizes the manual based on the user's characteristics and work content. For example, the generation AI generates a manual that omits detailed procedures for experienced employees and includes more detailed explanations for beginners based on information about the user's characteristics and work content. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate a customized manual based on the user's characteristics and work content. The generation AI inputs information about the user's characteristics and work content as prompts and generates a customized manual based on those prompts. Step 2: The feedback collection unit collects feedback on the results of work using the customized manual. For example, it collects feedback on the results of employees performing their work according to the manual and their opinions on the ease of use of the manual. The feedback collection unit can collect feedback in the form of a questionnaire. It can also automatically collect data on work results. For example, it can collect data on work outcomes and efficiency. Step 3: The Business Improvement Proposal Department analyzes the collected feedback and makes proposals for business improvements. For example, if the feedback indicates that a particular procedure is difficult to understand, the department will propose improvements to make that procedure easier to understand. The Business Improvement Proposal Department uses a generation AI to analyze the feedback and make proposals for business improvements. Based on the feedback data, the generation AI can also propose new procedures that will lead to more efficient work and reduced errors.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

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

Claims

1. a manual generation unit that customizes the manual based on the characteristics and work content of the user; a feedback collection unit that collects feedback on work results using the manual customized by the manual generation unit; a business improvement proposal unit that analyzes the feedback collected by the feedback collection unit and makes proposals for business improvements. A system characterized by:

2. The manual generation unit Automatically generate manuals that correspond to different languages ​​and cultures, making them usable in a global corporate environment 2. The system of claim 1.

3. The feedback collection unit: Analyze feedback data to quantitatively assess how effective specific work procedures are 2. The system of claim 1.

4. The business improvement proposal department Analyze feedback data, identify bottlenecks in business processes, and propose specific improvement plans to resolve them 2. The system of claim 1.

5. The manual generation unit Evaluating the user's current emotional state in real time and generating a manual that provides information in a less stressful manner 2. The system of claim 1.

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