System

Abstract

An object of the system according to the embodiment is to realize automation of operation work in accordance with a business model unique to a company.SOLUTION: A system according to an embodiment includes a training environment provision unit, a data analysis unit, a business procedure generation unit, and a business automation unit. The training environment provider receives business data of an enterprise. The data analysis unit analyzes the business data received by the training environment providing unit. The business procedure generation unit generates an optimal operation procedure based on the data analyzed by the data analysis unit. The business automation unit executes the operation procedure generated by the business procedure generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology lacked a dedicated training platform that enabled companies to automate operational tasks tailored to their unique business models.

[0005] The system according to the embodiment aims to realize the automation of operational tasks that are tailored to a company's unique business model. [Means for solving the problem]

[0006] The system according to the embodiment includes a training environment providing unit, a data analysis unit, a business procedure generation unit, and a business automation unit. The training environment providing unit receives business data from a company. The data analysis unit analyzes the business data received by the training environment providing unit. The business procedure generation unit generates an optimal operation procedure based on the data analyzed by the data analysis unit. The business automation unit executes the operation procedure generated by the business procedure generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize the automation of operational tasks that are tailored to a company's unique business model. [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) A dedicated training platform according to an embodiment of the present invention is a system for companies to automate operational tasks using AI. This system allows companies to train AI optimized for their own business models, thereby improving the efficiency and automation of operational tasks. As a result, the dedicated training platform allows companies to train AI optimized for their own business models, thereby improving the efficiency and automation of operational tasks.

[0029] A dedicated training platform according to an embodiment includes a training environment providing unit, a data analysis unit, a business procedure generation unit, and a business automation unit. The training environment providing unit receives business data from a company. For example, the company's past business data and procedure manuals can be uploaded to the platform. The training environment providing unit can also directly receive business data submitted in digital format. The data analysis unit analyzes the business data received by the training environment providing unit. For example, the generation AI analyzes the business data using statistical analysis or machine learning algorithms. The data analysis unit can also evaluate the reliability and consistency of the business data and automatically filter out unreliable data. The business procedure generation unit generates optimal operation procedures based on the data analyzed by the data analysis unit. For example, the generation AI generates operation procedures that are optimal for the company's business model. The business procedure generation unit can also learn from the company's past successes and failures and generate optimal operation procedures based on the learned results. The business automation unit executes the operation procedures generated by the business procedure generation unit. For example, the generation AI understands the company's business processes and executes optimal procedures. The task automation unit can also dynamically change task priorities, prioritizing tasks with a high degree of urgency. This allows the dedicated training platform according to the embodiment to analyze a company's task data, generate optimal operational procedures, and automate them, thereby improving task efficiency. For example, a manufacturing company can improve the efficiency of its production line by having the generation AI learn production data and generate an optimal production schedule. In logistics operations, the generation AI can calculate optimal delivery routes and automatically create delivery schedules. It can also analyze sales data and propose optimal inventory management methods. It can also analyze customer data and propose measures to improve customer satisfaction.

[0030] The data analysis unit can evaluate the reliability and consistency of business data and automatically exclude low-reliability data. For example, when the generation AI analyzes business data, the data analysis unit introduces an algorithm to evaluate the reliability of the data. For example, it calculates a reliability score based on information about the source and creator of the data and automatically excludes low-reliability data. The data analysis unit can also take into account the data format and update frequency to evaluate the consistency of the data. For example, it excludes data that has an inconsistent format or is updated infrequently. This eliminates low-reliability data, thereby improving the accuracy of the analysis.

[0031] The data analysis unit can improve the accuracy of analysis based on background information about the data, taking into account information about the origin and creator of the business data. For example, when the generation AI analyzes business data, the data analysis unit introduces an algorithm that takes into account information about the origin of the data. For example, it prioritizes analysis of data from highly reliable sources. The data analysis unit can also take into account information about the creator of the data. For example, it can evaluate the reliability of the data based on the creator's reliability and expertise. Furthermore, the data analysis unit can improve the accuracy of analysis based on background information about the data. For example, it takes into account the data generation process and related metadata. In this way, analysis accuracy can be improved by taking into account background information about the data.

[0032] The training environment providing unit can diversify the input format of business data and also support voice input and image input. For example, the training environment providing unit adds a voice input function to the platform, allowing users to input business data by voice. For example, it converts voice data into text data using voice recognition technology. The training environment providing unit can also add an image input function. For example, it can analyze image data using image recognition technology and use it as business data. This makes it possible to analyze a wider variety of data by supporting voice input and image input.

[0033] The training environment provision unit provides a training platform that can be used jointly by companies from different industries, thereby promoting data sharing and mutual learning between different industries. The training environment provision unit, for example, builds a training platform that can be used jointly by companies from different industries and promotes data sharing. For example, companies in the manufacturing and service industries use the same platform. The training environment provision unit can also provide functions to promote mutual learning between different industries. For example, joint research and data analysis can be shared. This promotes data sharing and mutual learning between different industries, allowing for the acquisition of broader knowledge.

[0034] The business procedure generation unit can learn from a company's past successes and failures and generate optimal operation procedures based on them. For example, the business procedure generation unit has a generation AI learn from a company's past successes and failures to generate optimal operation procedures. For example, it automatically generates procedures with a high probability of success based on past data. The business procedure generation unit can also adjust procedures based on successes and failures. For example, it can optimize procedures by referring to successes and generate procedures to avoid failures. In this way, optimal operation procedures can be generated by learning from past successes and failures.

[0035] The business procedure generation unit can dynamically generate optimal operating procedures by taking into account the company's market environment and the trends of competitors. For example, the business procedure generation unit uses a generation AI to analyze the company's market environment and generate optimal operating procedures based on that information. For example, the unit adjusts procedures by taking into account market demand and supply conditions. The business procedure generation unit can also take into account the trends of competitors. For example, it optimizes procedures based on the strategies of competitors and industry trends. This makes it possible to dynamically generate optimal procedures by taking into account the market environment and the trends of competitors.

[0036] The business procedure generation unit can share best practices between companies with different business models and promote mutual learning. For example, the business procedure generation unit uses a generation AI to build a system for sharing best practices between companies with different business models. For example, they share success stories and failure stories and learn from each other. The business procedure generation unit can also provide functions to promote mutual learning. For example, they can share joint research and data analysis. This allows companies with different business models to share best practices and promote mutual learning, thereby gaining a wider range of knowledge.

[0037] The business procedure generation unit can generate operation procedures that are compatible with a company's culture by taking into account its culture and values. For example, the business procedure generation unit uses a generation AI to analyze the company's culture and values ​​and generate operation procedures that are compatible with the culture based on that information. For example, the unit adjusts procedures by taking into account the company's mission and vision. The business procedure generation unit can also provide a function for generating procedures that are compatible with a company's culture. For example, it optimizes procedures based on the company's values. This makes it possible to generate procedures that are compatible with the culture by taking into account the company's culture and values.

[0038] The task automation unit can dynamically change the priority of tasks and prioritize tasks with high urgency. For example, the task automation unit introduces an algorithm in which the generation AI dynamically changes the priority of tasks, and prioritizes tasks with high urgency. For example, it evaluates the urgency of tasks in real time and adjusts the priority. The task automation unit can also provide a function for prioritizing tasks with high urgency. For example, it automatically assigns tasks with high urgency. This allows tasks with high urgency to be prioritized, thereby improving task efficiency.

[0039] The business automation unit can provide an alert function that monitors the progress of work in real time and prompts manual intervention as necessary. For example, the business automation unit builds a system in which a generation AI monitors the progress of work in real time and provides an alert function that prompts manual intervention as necessary. For example, an alert can be issued if progress is behind schedule. The business automation unit can also provide a function to prompt manual intervention based on progress. For example, an alert can be issued for tasks where progress is behind schedule, prompting manual intervention. This makes it possible to monitor the progress of work in real time and prompt manual intervention as necessary, thereby improving business efficiency.

[0040] The business automation department can automate the collaboration between different business processes and improve the efficiency of the entire business. For example, the business automation department builds a system in which generative AI automates the collaboration between different business processes, improving the efficiency of the entire business. For example, it links manufacturing processes with logistics processes. The business automation department can also automate the sharing of data between business processes. For example, it shares manufacturing data and logistics data in real time. This automates the collaboration between different business processes and improves the efficiency of the entire business.

[0041] The business automation department can automatically evaluate the results of business operations and optimize the next business procedure based on the evaluation results. For example, the business automation department can build a system in which a generative AI automatically evaluates the results of business operations and optimize the next business procedure based on the evaluation results. For example, it can evaluate the success rate and efficiency of business operations. The business automation department can also adjust procedures based on the evaluation results. For example, it can optimize procedures based on the evaluation results and reflect them in the next business operation. In this way, business efficiency can be improved by automatically evaluating the results of business operations and optimizing the next business procedure.

[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 training environment provision unit receives the company's business data. For example, the company can upload its past business data and procedure manuals to the platform. The training environment provision unit can also directly receive business data submitted in digital format. The data analysis unit analyzes the business data received by the training environment provision unit. For example, the generation AI analyzes the business data using statistical analysis and machine learning algorithms. The data analysis unit can also evaluate the reliability and consistency of the business data and automatically filter out unreliable data. The business procedure generation unit generates optimal operation procedures based on the data analyzed by the data analysis unit. For example, the generation AI generates operation procedures that are optimal for the company's business model. The business procedure generation unit can also learn from the company's past successes and failures and generate optimal operation procedures based on that. The business automation unit executes the operation procedures generated by the business procedure generation unit. For example, the generation AI understands the company's business processes and executes optimal procedures. The business automation unit can also dynamically change the priorities of operations to prioritize more urgent tasks. As a result, the dedicated training platform according to the embodiment analyzes a company's business data, generates optimal operation procedures, and automates them to improve business efficiency. For example, a manufacturing company can have a generation AI learn production data and generate an optimal production schedule to improve the efficiency of its production line. In logistics operations, the generation AI can calculate optimal delivery routes and automatically create delivery schedules. It can also analyze sales data and propose optimal inventory management methods. It can also analyze customer data and propose measures to improve customer satisfaction.

[0044] The data analysis unit can evaluate the reliability and consistency of business data and automatically exclude low-reliability data. For example, it can calculate a reliability score based on information about the source and creator of the data, and automatically exclude low-reliability data. The data analysis unit can also take into account the data format and update frequency to evaluate data consistency. For example, it can exclude data that does not have a standardized format or is updated infrequently. This can improve analysis accuracy by excluding low-reliability data.

[0045] The data analysis unit can improve the accuracy of analysis based on the background information of the data, taking into account information about the origin and creator of the business data. For example, it prioritizes analysis of data from highly reliable sources. The data analysis unit can also take into account information about the creator of the data. For example, it evaluates the reliability of data based on the creator's reliability and expertise. Furthermore, the data analysis unit takes into account the data generation process and related metadata. In this way, by taking into account the background information of the data, the accuracy of analysis can be improved.

[0046] The training environment providing unit can diversify the input format of business data and also support voice input and image input. For example, a voice input function can be added to the platform, allowing users to input business data by voice. For example, voice recognition technology can be used to convert voice data into text data. The training environment providing unit can also add an image input function. For example, image recognition technology can be used to analyze image data and use it as business data. This makes it possible to support voice input and image input, making it possible to analyze a wider variety of data.

[0047] The training environment provision department provides a training platform that can be used jointly by companies from different industries, promoting data sharing and mutual learning between different industries. For example, it builds a training platform that can be used jointly by companies from different industries to promote data sharing. For example, companies in the manufacturing and service industries use the same platform. The training environment provision department can also provide functions to promote mutual learning between different industries. For example, it can share joint research and data analysis. This promotes data sharing and mutual learning between different industries, allowing for the acquisition of broader knowledge.

[0048] The business procedure generation unit can learn from a company's past successes and failures and generate optimal operation procedures based on them. For example, the generation AI can learn from a company's past successes and failures to generate optimal operation procedures. For example, it can automatically generate procedures with a high probability of success based on past data. The business procedure generation unit can also adjust procedures based on successes and failures. For example, it can optimize procedures by referring to successes and generate procedures to avoid failures. In this way, optimal operation procedures can be generated by learning from past successes and failures.

[0049] The business procedure generation unit can dynamically generate optimal operating procedures by taking into account the company's market environment and the trends of competitors. For example, the generation AI analyzes the company's market environment and generates optimal operating procedures based on that information. For example, it adjusts procedures by taking into account market demand and supply conditions. The business procedure generation unit can also take into account the trends of competitors. For example, it optimizes procedures based on competitors' strategies and industry trends. This makes it possible to dynamically generate optimal procedures by taking into account the market environment and the trends of competitors.

[0050] The business procedure generation unit can share best practices between companies with different business models and promote mutual learning. For example, the generation AI can build a system for sharing best practices between companies with different business models. For example, they can share success stories and failure stories and learn from each other. The business procedure generation unit can also provide functions to promote mutual learning. For example, joint research and data analysis can be shared. This allows companies with different business models to share best practices and promote mutual learning, thereby gaining a wider range of knowledge.

[0051] The business procedure generation unit can generate operating procedures that are compatible with a company's culture, taking into account its culture and values. For example, the generation AI can analyze the company's culture and values ​​and generate operating procedures that are compatible with the culture based on that information. For example, it can adjust procedures by taking into account the company's mission and vision. The business procedure generation unit can also provide a function for generating procedures that are compatible with a company's culture. For example, it can optimize procedures based on the company's values. This makes it possible to generate procedures that are compatible with the culture by taking into account the company's culture and values.

[0052] The process automation unit can dynamically change the priority of tasks and prioritize urgent tasks. For example, the generation AI can introduce an algorithm that dynamically changes the priority of tasks, giving priority to urgent tasks. For example, it can evaluate the urgency of tasks in real time and adjust the priority. The process automation unit can also provide a function for prioritizing urgent tasks. For example, it can automatically assign urgent tasks. This can improve the efficiency of work by prioritizing urgent tasks.

[0053] The process automation unit can provide an alert function that monitors the progress of work in real time and prompts manual intervention as necessary. For example, a generation AI can build a system that monitors the progress of work in real time and provides an alert function that prompts manual intervention as necessary. For example, an alert can be issued if progress is behind schedule. The process automation unit can also provide a function to prompt manual intervention based on progress. For example, an alert can be issued for tasks where progress is behind schedule, prompting manual intervention. This makes it possible to monitor the progress of work in real time and prompt manual intervention as necessary, thereby improving work efficiency.

[0054] The business automation department can automate the collaboration between different business processes, improving the efficiency of the entire business. For example, generative AI can build a system that automates the collaboration between different business processes, improving the efficiency of the entire business. For example, it can link manufacturing processes and logistics processes. The business automation department can also automate the sharing of data between business processes. For example, it can share manufacturing data and logistics data in real time. This automates the collaboration between different business processes, improving the efficiency of the entire business.

[0055] The process automation department can automatically evaluate the results of a task and optimize the next task procedure based on the evaluation results. For example, a system can be built in which a generative AI automatically evaluates the results of a task, and the next task procedure can be optimized based on the evaluation results. For example, the success rate and efficiency of the task can be evaluated. The process automation department can also adjust procedures based on the evaluation results. For example, the procedure can be optimized based on the evaluation results and reflected in the next task. In this way, by automatically evaluating the results of a task and optimizing the next task procedure, it is possible to improve work efficiency.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The training environment provider receives the company's business data. For example, the company can upload past business data and procedure manuals to the platform. It can also directly receive business data submitted in digital format. Step 2: The data analysis unit analyzes the business data received by the training environment provision unit. For example, the generation AI analyzes the business data using statistical analysis or machine learning algorithms. The data analysis unit can also evaluate the reliability and consistency of the business data and automatically filter out unreliable data. Step 3: The business procedure generation unit generates optimal operation procedures based on the data analyzed by the data analysis unit. For example, the generation AI generates operation procedures that are optimal for a company's business model. The business procedure generation unit can also learn from the company's past successes and failures and generate optimal operation procedures based on that information. Step 4: The process automation unit executes the operation procedures generated by the process procedure generation unit. For example, the generation AI understands the company's business processes and executes the optimal procedures. The process automation unit can also dynamically change the priority of tasks, giving priority to tasks with high urgency.

[0058] (Example 2) A dedicated training platform according to an embodiment of the present invention is a system for companies to automate operational tasks using AI. This system allows companies to train AI optimized for their own business models, thereby improving the efficiency and automation of operational tasks. As a result, the dedicated training platform allows companies to train AI optimized for their own business models, thereby improving the efficiency and automation of operational tasks.

[0059] A dedicated training platform according to an embodiment includes a training environment providing unit, a data analysis unit, a business procedure generation unit, and a business automation unit. The training environment providing unit receives business data from a company. For example, the company's past business data and procedure manuals can be uploaded to the platform. The training environment providing unit can also directly receive business data submitted in digital format. The data analysis unit analyzes the business data received by the training environment providing unit. For example, the generation AI analyzes the business data using statistical analysis or machine learning algorithms. The data analysis unit can also evaluate the reliability and consistency of the business data and automatically filter out unreliable data. The business procedure generation unit generates optimal operation procedures based on the data analyzed by the data analysis unit. For example, the generation AI generates operation procedures that are optimal for the company's business model. The business procedure generation unit can also learn from the company's past successes and failures and generate optimal operation procedures based on the learned results. The business automation unit executes the operation procedures generated by the business procedure generation unit. For example, the generation AI understands the company's business processes and executes optimal procedures. The task automation unit can also dynamically change task priorities, prioritizing tasks with a high degree of urgency. This allows the dedicated training platform according to the embodiment to analyze a company's task data, generate optimal operational procedures, and automate them, thereby improving task efficiency. For example, a manufacturing company can improve the efficiency of its production line by having the generation AI learn production data and generate an optimal production schedule. In logistics operations, the generation AI can calculate optimal delivery routes and automatically create delivery schedules. It can also analyze sales data and propose optimal inventory management methods. It can also analyze customer data and propose measures to improve customer satisfaction.

[0060] The data analysis unit can evaluate the reliability and consistency of business data and automatically exclude low-reliability data. For example, when the generation AI analyzes business data, the data analysis unit introduces an algorithm to evaluate the reliability of the data. For example, it calculates a reliability score based on information about the source and creator of the data and automatically excludes low-reliability data. The data analysis unit can also take into account the data format and update frequency to evaluate the consistency of the data. For example, it excludes data that has an inconsistent format or is updated infrequently. This eliminates low-reliability data, thereby improving the accuracy of the analysis.

[0061] The data analysis unit can improve the accuracy of analysis based on background information about the data, taking into account information about the origin and creator of the business data. For example, when the generation AI analyzes business data, the data analysis unit introduces an algorithm that takes into account information about the origin of the data. For example, it prioritizes analysis of data from highly reliable sources. The data analysis unit can also take into account information about the creator of the data. For example, it can evaluate the reliability of the data based on the creator's reliability and expertise. Furthermore, the data analysis unit can improve the accuracy of analysis based on background information about the data. For example, it takes into account the data generation process and related metadata. In this way, analysis accuracy can be improved by taking into account background information about the data.

[0062] The training environment providing unit can diversify the input format of business data and also support voice input and image input. For example, the training environment providing unit adds a voice input function to the platform, allowing users to input business data by voice. For example, it converts voice data into text data using voice recognition technology. The training environment providing unit can also add an image input function. For example, it can analyze image data using image recognition technology and use it as business data. This makes it possible to analyze a wider variety of data by supporting voice input and image input.

[0063] The training environment provision unit provides a training platform that can be used jointly by companies from different industries, thereby promoting data sharing and mutual learning between different industries. The training environment provision unit, for example, builds a training platform that can be used jointly by companies from different industries and promotes data sharing. For example, companies in the manufacturing and service industries use the same platform. The training environment provision unit can also provide functions to promote mutual learning between different industries. For example, joint research and data analysis can be shared. This promotes data sharing and mutual learning between different industries, allowing for the acquisition of broader knowledge.

[0064] The training environment providing unit is equipped with an emotion estimation function and can analyze the emotion of the user when entering business data in real time and provide feedback to reduce stress during the entry work. The training environment providing unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering business data in real time. For example, it analyzes the user's emotion using a camera or a microphone. The training environment providing unit can also provide feedback to reduce stress during the entry work. For example, it provides feedback in real time to reduce the user's stress. In this way, by analyzing the user's emotion in real time and providing feedback to reduce stress, the efficiency of the entry work can be improved.

[0065] The business procedure generation unit can learn from a company's past successes and failures and generate optimal operation procedures based on them. For example, the business procedure generation unit has a generation AI learn from a company's past successes and failures to generate optimal operation procedures. For example, it automatically generates procedures with a high probability of success based on past data. The business procedure generation unit can also adjust procedures based on successes and failures. For example, it can optimize procedures by referring to successes and generate procedures to avoid failures. In this way, optimal operation procedures can be generated by learning from past successes and failures.

[0066] The business procedure generation unit can dynamically generate optimal operating procedures by taking into account the company's market environment and the trends of competitors. For example, the business procedure generation unit uses a generation AI to analyze the company's market environment and generate optimal operating procedures based on that information. For example, the unit adjusts procedures by taking into account market demand and supply conditions. The business procedure generation unit can also take into account the trends of competitors. For example, it optimizes procedures based on the strategies of competitors and industry trends. This makes it possible to dynamically generate optimal procedures by taking into account the market environment and the trends of competitors.

[0067] The work procedure generation unit can use the emotion estimation function to analyze employee emotions and generate operation procedures to increase employee motivation. The work procedure generation unit, for example, uses the emotion estimation function to analyze employee emotions and generate operation procedures to increase employee motivation. For example, the unit proposes an optimal procedure based on the employee's emotion score. The work procedure generation unit can also adjust procedures taking employee emotions into consideration. For example, the unit generates procedures to reduce employee stress. In this way, work efficiency can be improved by analyzing employee emotions and generating procedures to increase employee motivation.

[0068] The business procedure generation unit can share best practices between companies with different business models and promote mutual learning. For example, the business procedure generation unit uses a generation AI to build a system for sharing best practices between companies with different business models. For example, they share success stories and failure stories and learn from each other. The business procedure generation unit can also provide functions to promote mutual learning. For example, they can share joint research and data analysis. This allows companies with different business models to share best practices and promote mutual learning, thereby gaining a wider range of knowledge.

[0069] The business procedure generation unit can generate operation procedures that are compatible with a company's culture by taking into account its culture and values. For example, the business procedure generation unit uses a generation AI to analyze the company's culture and values ​​and generate operation procedures that are compatible with the culture based on that information. For example, the unit adjusts procedures by taking into account the company's mission and vision. The business procedure generation unit can also provide a function for generating procedures that are compatible with a company's culture. For example, it optimizes procedures based on the company's values. This makes it possible to generate procedures that are compatible with the culture by taking into account the company's culture and values.

[0070] The business procedure generation unit can use the emotion estimation function to analyze customer emotions and generate operation procedures for improving customer satisfaction. The business procedure generation unit, for example, uses the emotion estimation function to analyze the emotions of a company's customers and generate operation procedures for improving customer satisfaction. For example, the operation procedure generation unit proposes an optimal procedure based on the customer's emotion score. The business procedure generation unit can also adjust procedures taking into account the customer's emotions. For example, it proposes measures to improve customer satisfaction. In this way, customer satisfaction can be improved by analyzing customer emotions and generating procedures to improve customer satisfaction.

[0071] The task automation unit can dynamically change the priority of tasks and prioritize tasks with high urgency. For example, the task automation unit introduces an algorithm in which the generation AI dynamically changes the priority of tasks, and prioritizes tasks with high urgency. For example, it evaluates the urgency of tasks in real time and adjusts the priority. The task automation unit can also provide a function for prioritizing tasks with high urgency. For example, it automatically assigns tasks with high urgency. This allows tasks with high urgency to be prioritized, thereby improving task efficiency.

[0072] The business automation unit can provide an alert function that monitors the progress of work in real time and prompts manual intervention as necessary. For example, the business automation unit builds a system in which a generation AI monitors the progress of work in real time and provides an alert function that prompts manual intervention as necessary. For example, an alert can be issued if progress is behind schedule. The business automation unit can also provide a function to prompt manual intervention based on progress. For example, an alert can be issued for tasks where progress is behind schedule, prompting manual intervention. This makes it possible to monitor the progress of work in real time and prompt manual intervention as necessary, thereby improving business efficiency.

[0073] The task automation unit can use the emotion estimation function to analyze employee emotions and automatically adjust work allocation to reduce employee stress. For example, the task automation unit uses the emotion estimation function to build a system that analyzes employee emotions and automatically adjusts work allocation to reduce stress. For example, it reduces the workload of employees with high stress levels. The task automation unit can also adjust work allocation taking employee emotions into consideration. For example, it reallocates work based on the employee's emotion score. In this way, work efficiency can be improved by analyzing employee emotions and automatically adjusting work allocation to reduce stress.

[0074] The business automation department can automate the collaboration between different business processes and improve the efficiency of the entire business. For example, the business automation department builds a system in which generative AI automates the collaboration between different business processes, improving the efficiency of the entire business. For example, it links manufacturing processes with logistics processes. The business automation department can also automate the sharing of data between business processes. For example, it shares manufacturing data and logistics data in real time. This automates the collaboration between different business processes and improves the efficiency of the entire business.

[0075] The business automation department can automatically evaluate the results of business operations and optimize the next business procedure based on the evaluation results. For example, the business automation department can build a system in which a generative AI automatically evaluates the results of business operations and optimize the next business procedure based on the evaluation results. For example, it can evaluate the success rate and efficiency of business operations. The business automation department can also adjust procedures based on the evaluation results. For example, it can optimize procedures based on the evaluation results and reflect them in the next business operation. In this way, business efficiency can be improved by automatically evaluating the results of business operations and optimizing the next business procedure.

[0076] The business automation unit can use the emotion estimation function to analyze customer emotions and automatically provide customized services that meet the customer's needs. For example, the business automation unit uses the emotion estimation function to analyze customer emotions and build a system that automatically provides customized services that meet the needs. For example, the business automation unit adjusts services based on the customer's emotion score. The business automation unit can also provide services that take customer needs into consideration. For example, the business automation unit provides services that meet the customer's requests and expectations. This makes it possible to analyze customer emotions and automatically provide customized services that meet the needs, thereby improving customer satisfaction.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The training environment provision unit receives the company's business data. For example, the company can upload its past business data and procedure manuals to the platform. The training environment provision unit can also directly receive business data submitted in digital format. The data analysis unit analyzes the business data received by the training environment provision unit. For example, the generation AI analyzes the business data using statistical analysis and machine learning algorithms. The data analysis unit can also evaluate the reliability and consistency of the business data and automatically filter out unreliable data. The business procedure generation unit generates optimal operation procedures based on the data analyzed by the data analysis unit. For example, the generation AI generates operation procedures that are optimal for the company's business model. The business procedure generation unit can also learn from the company's past successes and failures and generate optimal operation procedures based on that. The business automation unit executes the operation procedures generated by the business procedure generation unit. For example, the generation AI understands the company's business processes and executes optimal procedures. The business automation unit can also dynamically change the priorities of operations to prioritize more urgent tasks. As a result, the dedicated training platform according to the embodiment analyzes a company's business data, generates optimal operation procedures, and automates them to improve business efficiency. For example, a manufacturing company can have a generation AI learn production data and generate an optimal production schedule to improve the efficiency of its production line. In logistics operations, the generation AI can calculate optimal delivery routes and automatically create delivery schedules. It can also analyze sales data and propose optimal inventory management methods. It can also analyze customer data and propose measures to improve customer satisfaction.

[0079] The data analysis unit can evaluate the reliability and consistency of business data and automatically exclude low-reliability data. For example, it can calculate a reliability score based on information about the source and creator of the data, and automatically exclude low-reliability data. The data analysis unit can also take into account the data format and update frequency to evaluate data consistency. For example, it can exclude data that does not have a standardized format or is updated infrequently. This can improve analysis accuracy by excluding low-reliability data.

[0080] The data analysis unit can improve the accuracy of analysis based on the background information of the data, taking into account information about the origin and creator of the business data. For example, it prioritizes analysis of data from highly reliable sources. The data analysis unit can also take into account information about the creator of the data. For example, it evaluates the reliability of data based on the creator's reliability and expertise. Furthermore, the data analysis unit takes into account the data generation process and related metadata. In this way, by taking into account the background information of the data, the accuracy of analysis can be improved.

[0081] The training environment providing unit can diversify the input format of business data and also support voice input and image input. For example, a voice input function can be added to the platform, allowing users to input business data by voice. For example, voice recognition technology can be used to convert voice data into text data. The training environment providing unit can also add an image input function. For example, image recognition technology can be used to analyze image data and use it as business data. This makes it possible to support voice input and image input, making it possible to analyze a wider variety of data.

[0082] The training environment provision department provides a training platform that can be used jointly by companies from different industries, promoting data sharing and mutual learning between different industries. For example, it builds a training platform that can be used jointly by companies from different industries to promote data sharing. For example, companies in the manufacturing and service industries use the same platform. The training environment provision department can also provide functions to promote mutual learning between different industries. For example, it can share joint research and data analysis. This promotes data sharing and mutual learning between different industries, allowing for the acquisition of broader knowledge.

[0083] The training environment providing unit is equipped with an emotion estimation function and can analyze the emotion of the user when entering business data in real time and provide feedback to reduce stress during the entry work. For example, the emotion estimation function is used to analyze the emotion of the user when entering business data in real time. For example, a camera or microphone is used to analyze the user's emotion. The training environment providing unit can also provide feedback to reduce stress during the entry work. For example, feedback is provided in real time to reduce the user's stress. In this way, the efficiency of the entry work can be improved by analyzing the user's emotion in real time and providing feedback to reduce stress.

[0084] The business procedure generation unit can learn from a company's past successes and failures and generate optimal operation procedures based on them. For example, the generation AI can learn from a company's past successes and failures to generate optimal operation procedures. For example, it can automatically generate procedures with a high probability of success based on past data. The business procedure generation unit can also adjust procedures based on successes and failures. For example, it can optimize procedures by referring to successes and generate procedures to avoid failures. In this way, optimal operation procedures can be generated by learning from past successes and failures.

[0085] The business procedure generation unit can dynamically generate optimal operating procedures by taking into account the company's market environment and the trends of competitors. For example, the generation AI analyzes the company's market environment and generates optimal operating procedures based on that information. For example, it adjusts procedures by taking into account market demand and supply conditions. The business procedure generation unit can also take into account the trends of competitors. For example, it optimizes procedures based on competitors' strategies and industry trends. This makes it possible to dynamically generate optimal procedures by taking into account the market environment and the trends of competitors.

[0086] The work procedure generation unit can use the emotion estimation function to analyze employee emotions and generate operation procedures to increase employee motivation. For example, the emotion estimation function can be used to analyze employee emotions and generate operation procedures to increase employee motivation. For example, the operation procedure generation unit can propose optimal procedures based on the employee's emotion score. The work procedure generation unit can also adjust procedures taking employee emotions into consideration. For example, it can generate procedures to reduce employee stress. In this way, work efficiency can be improved by analyzing employee emotions and generating procedures to increase employee motivation.

[0087] The business procedure generation unit can share best practices between companies with different business models and promote mutual learning. For example, the generation AI can build a system for sharing best practices between companies with different business models. For example, they can share success stories and failure stories and learn from each other. The business procedure generation unit can also provide functions to promote mutual learning. For example, joint research and data analysis can be shared. This allows companies with different business models to share best practices and promote mutual learning, thereby gaining a wider range of knowledge.

[0088] The business procedure generation unit can generate operating procedures that are compatible with a company's culture, taking into account its culture and values. For example, the generation AI can analyze the company's culture and values ​​and generate operating procedures that are compatible with the culture based on that information. For example, it can adjust procedures by taking into account the company's mission and vision. The business procedure generation unit can also provide a function for generating procedures that are compatible with a company's culture. For example, it can optimize procedures based on the company's values. This makes it possible to generate procedures that are compatible with the culture by taking into account the company's culture and values.

[0089] The business procedure generation unit can use the emotion estimation function to analyze customer emotions and generate operation procedures for improving customer satisfaction. For example, the emotion estimation function can be used to analyze the emotions of a company's customers and generate operation procedures for improving customer satisfaction. For example, the operation procedure generation unit can propose an optimal procedure based on the customer's emotion score. The business procedure generation unit can also adjust procedures taking into account the customer's emotions. For example, it can propose measures to improve customer satisfaction. In this way, customer satisfaction can be improved by analyzing customer emotions and generating procedures for improving customer satisfaction.

[0090] The process automation unit can dynamically change the priority of tasks and prioritize urgent tasks. For example, the generation AI can introduce an algorithm that dynamically changes the priority of tasks, giving priority to urgent tasks. For example, it can evaluate the urgency of tasks in real time and adjust the priority. The process automation unit can also provide a function for prioritizing urgent tasks. For example, it can automatically assign urgent tasks. This can improve the efficiency of work by prioritizing urgent tasks.

[0091] The process automation unit can provide an alert function that monitors the progress of work in real time and prompts manual intervention as necessary. For example, a generation AI can build a system that monitors the progress of work in real time and provides an alert function that prompts manual intervention as necessary. For example, an alert can be issued if progress is behind schedule. The process automation unit can also provide a function to prompt manual intervention based on progress. For example, an alert can be issued for tasks where progress is behind schedule, prompting manual intervention. This makes it possible to monitor the progress of work in real time and prompt manual intervention as necessary, thereby improving work efficiency.

[0092] The task automation unit can use the emotion estimation function to analyze employee emotions and automatically adjust task allocation to reduce employee stress. For example, a system can be constructed that uses the emotion estimation function to analyze employee emotions and automatically adjust task allocation to reduce stress. For example, the task automation unit can reduce the workload of employees with high stress levels. The task automation unit can also adjust task allocation taking employee emotions into consideration. For example, tasks can be reallocated based on the employee's emotion score. This allows for the analysis of employee emotions and automatic adjustment of task allocation to reduce stress, thereby improving work efficiency.

[0093] The business automation department can automate the collaboration between different business processes, improving the efficiency of the entire business. For example, generative AI can build a system that automates the collaboration between different business processes, improving the efficiency of the entire business. For example, it can link manufacturing processes and logistics processes. The business automation department can also automate the sharing of data between business processes. For example, it can share manufacturing data and logistics data in real time. This automates the collaboration between different business processes, improving the efficiency of the entire business.

[0094] The process automation department can automatically evaluate the results of a task and optimize the next task procedure based on the evaluation results. For example, a system can be built in which a generative AI automatically evaluates the results of a task, and the next task procedure can be optimized based on the evaluation results. For example, the success rate and efficiency of the task can be evaluated. The process automation department can also adjust procedures based on the evaluation results. For example, the procedure can be optimized based on the evaluation results and reflected in the next task. In this way, by automatically evaluating the results of a task and optimizing the next task procedure, it is possible to improve work efficiency.

[0095] The business automation unit can use the emotion estimation function to analyze customer emotions and automatically provide customized services that meet the customer's needs. For example, a system can be built that uses the emotion estimation function to analyze customer emotions and automatically provide customized services that meet the needs. For example, the service can be adjusted based on the customer's emotion score. The business automation unit can also provide services that take customer needs into consideration. For example, the service can be provided in accordance with the customer's requests and expectations. In this way, customer satisfaction can be improved by analyzing customer emotions and automatically providing customized services that meet the needs.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The training environment provider receives the company's business data. For example, the company can upload past business data and procedure manuals to the platform. It can also directly receive business data submitted in digital format. Step 2: The data analysis unit analyzes the business data received by the training environment provision unit. For example, the generation AI analyzes the business data using statistical analysis or machine learning algorithms. The data analysis unit can also evaluate the reliability and consistency of the business data and automatically filter out unreliable data. Step 3: The business procedure generation unit generates optimal operation procedures based on the data analyzed by the data analysis unit. For example, the generation AI generates operation procedures that are optimal for a company's business model. The business procedure generation unit can also learn from the company's past successes and failures and generate optimal operation procedures based on that information. Step 4: The process automation unit executes the operation procedures generated by the process procedure generation unit. For example, the generation AI understands the company's business processes and executes the optimal procedures. The process automation unit can also dynamically change the priority of tasks, giving priority to tasks with high urgency.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[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 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.

[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 (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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 training environment provision department that receives business data from companies; a data analysis unit that analyzes the business data received by the training environment providing unit; a business procedure generation unit that generates an optimal operation procedure based on the data analyzed by the data analysis unit; a business automation unit that executes the operation procedure generated by the business procedure generation unit. A system characterized by:

2. The training environment providing unit The input format of the business data has been diversified, and voice and image input is also supported.

2. The system of claim 1.

3. The business procedure generation unit Learn from the company's past successes and failures, and generate the optimal operating procedures based on that.

2. The system of claim 1.

4. The business automation unit Dynamically change the priority of tasks, and prioritize the most urgent tasks.

2. The system of claim 1.

5. The training environment providing unit Equipped with an emotion estimation function, it analyzes the user's emotions in real time when entering business data and provides feedback to reduce the stress of the data entry work.

2. The system of claim 1.

6. The business procedure generation unit Analyzing employee emotions and generating the operating procedures to increase employee motivation 2. The system of claim 1.

7. The business automation unit Analyzing employee emotions and automatically adjusting work allocation to reduce stress for said employees 2. The system of claim 1.

8. The business automation unit Analyze customer sentiment and automatically provide customized services tailored to the customer's needs.

2. The system of claim 1.

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