System

Abstract

An object of the system according to the embodiment is to digitize the technique and experience of a toji and to inherit them to the next generation.SOLUTION: A system according to an embodiment includes a technology / experience digitization part, an inheritance part, and an optimization part. The skill / experience digitizing section digitizes the skill and experience of the toji. The inheriting unit inherits the technology and experience digitized by the technology / experience digitizing unit to a next-generation toji. The optimizing section optimizes the manufacturing process based on the data digitized by the technology / experience digitizing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the skills and experience of the master brewer were highly dependent on the individual, making it difficult to pass them on to the next generation.

[0005] The system of the embodiment aims to digitize the skills and experience of master brewers and pass them on to the next generation. [Means for solving the problem]

[0006] The system according to the embodiment comprises a skill and experience digitization unit, a succession unit, and an optimization unit. The skill and experience digitization unit digitizes the skills and experience of master brewers. The succession unit passes on the skills and experience digitized by the skill and experience digitization unit to the next generation of master brewers. The optimization unit optimizes the manufacturing process based on the data digitized by the skill and experience digitization unit. [Effects of the Invention]

[0007] The system according to the embodiment digitizes the techniques and experience of the master brewer, enabling them to be passed on to the next generation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The sake brewing support system according to the embodiment of the present invention is a system that digitizes the techniques and experience of master brewers, passes them on to the next generation of master brewers, and optimizes the brewing process. As a result, the sake brewing support system digitizes the techniques and experience of master brewers, passes them on to the next generation of master brewers, and optimizes the brewing process.

[0029] The sake brewing support system according to the embodiment includes a skill and experience digitization unit, a succession unit, and an optimization unit. The skill and experience digitization unit digitizes the skills and experience of the master brewers. For example, it records the tasks and decisions made by the master brewers, and AI analyzes the data. For example, it records in detail the master brewers' judgment criteria and work procedures for each process, such as rice selection, rice washing, and fermentation management. The succession unit passes on the skills and experience digitized by the skill and experience digitization unit to the next generation of master brewers. For example, it provides a curriculum based on the data analyzed by AI, providing content that the next generation of master brewers should learn. For example, it allows the next generation of master brewers to learn by experiencing actual tasks through online courses and simulations. The optimization unit optimizes the brewing process based on the data digitized by the skill and experience digitization unit. For example, it proposes optimal conditions for each process based on the digitized data. For example, AI analyzes and proposes optimal conditions such as fermentation temperature and time, type of rice, and amount of water. As a result, the sake brewing support system according to the embodiment can digitize the techniques and experience of the master brewers, pass them on to the next generation of master brewers, and optimize the brewing process.

[0030] The Technique and Experience Digitalization Department uses high-precision cameras to record the brewer's subtle movements and facial expressions while he works, and AI analyzes these movements and changes in facial expression to digitize the nuances of his technique. For example, the Technique and Experience Digitalization Department uses high-precision cameras to record the brewer's hand movements and the amount of force he applies when sorting rice, and AI analyzes these movements and saves them as digital data. For example, it analyzes the speed and pressure of hand movements to clarify sorting criteria. The Technique and Experience Digitalization Department also uses high-precision cameras to record the brewer's facial expressions and body movements while managing fermentation, and AI analyzes these changes to digitize the nuances of his technique. For example, it analyzes changes in facial expression depending on the progress of fermentation. The Technique and Experience Digitalization Department also uses high-precision cameras to record how the brewer holds and uses the tools he uses while working, and AI analyzes these movements and saves them as digital data. For example, it analyzes the angle of the tools and the amount of force applied to clarify the details of the technique. This makes it possible to analyze the brewer's subtle movements and facial expressions, and digitize the nuances of his technique.

[0031] The Technology and Experience Digitalization Department uses AI to analyze the selection criteria for the tools and materials used by the master brewer and digitize the reasons for selection and background knowledge. For example, the Technology and Experience Digitalization Department uses AI to analyze the criteria used by the master brewer to select the type and quality of rice used and save the reasons for selection and background knowledge as digital data. For example, it clarifies selection criteria based on the size, color, and aroma of the rice grains. The Technology and Experience Digitalization Department also uses AI to analyze the selection criteria for the water used by the master brewer and digitize the reasons for selection and background knowledge. For example, it clarifies selection criteria based on the water's hardness and mineral content. The Technology and Experience Digitalization Department also uses AI to analyze the selection criteria for the tools used by the master brewer and save the reasons for selection and background knowledge as digital data. For example, it clarifies selection criteria based on the material, shape, and ease of use of the tools. This makes it possible to analyze the selection criteria for the tools and materials used by the master brewer and digitize the reasons for selection and background knowledge.

[0032] When digitizing the skills and experience of a master brewer, the Skills and Experience Digitization Department can apply this to other traditional crafts and agricultural techniques, building a broad-based skill inheritance system. For example, when digitizing the skills and experience of a master brewer, the Skills and Experience Digitization Department can also digitize the techniques of other traditional crafts, such as pottery and lacquerware, to build a skill inheritance system. For example, the department records and analyzes the potter's hand movements and tool usage. The Skills and Experience Digitization Department also digitizes agricultural techniques and integrates them with the master brewer's skills and experience to build a broad-based skill inheritance system. For example, rice cultivation methods and harvesting techniques are digitized and linked to the master brewer's skills. The Skills and Experience Digitization Department also extracts common skills and knowledge from the digitized data of other traditional crafts and agricultural techniques to build a broad-based skill inheritance system. For example, common work procedures and judgment criteria are analyzed and compiled into a database. This allows the digitization of a master brewer's skills and experience to be applied to other traditional crafts and agricultural techniques, building a broad-based skill inheritance system.

[0033] The Skills and Experience Digitalization Department can recreate digitized skills and experiences using VR technology, allowing the next generation of master brewers to experience actual work in a virtual space. For example, the Skills and Experience Digitalization Department can recreate digitized skills and experiences of master brewers using VR technology, allowing the next generation of master brewers to experience rice sorting and washing in a virtual space. For example, they can wear VR goggles and perform the work in the virtual space. The Skills and Experience Digitalization Department can also recreate processes such as fermentation management and temperature adjustment using VR technology, allowing the next generation of master brewers to experience actual work in the virtual space. For example, it can simulate adjusting the temperature of a fermentation tank in a virtual space. The Skills and Experience Digitalization Department can also use VR technology to recreate the master brewer's work environment and how to use tools in a virtual space, allowing the next generation of master brewers to experience actual work. For example, they can learn how to hold and use tools in a virtual space. In this way, the digitized skills and experiences can be recreated using VR technology, allowing the next generation of master brewers to experience actual work in a virtual space.

[0034] The Inheritance Department uses AI to generate individually customized learning plans based on digitized skills and experience, and can provide them to the next generation of master brewers. For example, the Inheritance Department uses AI to generate customized learning plans based on the skills and experience of the next generation of master brewers. For example, it provides basic courses for beginners and advanced courses for advanced brewers. The Inheritance Department also uses AI to monitor the learning progress of the next generation of master brewers in real time and adjust the learning plans as needed. For example, it provides additional learning content if the level of understanding of a particular process is low. The Inheritance Department also uses AI to provide individually customized feedback to the next generation of master brewers based on digitized skills and experience. For example, it provides advice based on the accuracy and efficiency of the work. This allows the AI ​​to generate individually customized learning plans based on digitized skills and experience, and provide them to the next generation of master brewers.

[0035] The Inheritance Department uses digitized data to enable AI to monitor the progress of the next generation of master brewers in real time and adjust the learning content as needed. For example, the Inheritance Department uses AI to monitor the learning progress of the next generation of master brewers in real time and adjust the learning content according to the progress. For example, if the level of understanding of a particular process is low, additional learning content is provided. The Inheritance Department also uses AI to evaluate the learning progress of the next generation of master brewers based on the digitized data and reconstruct the learning plan as needed. For example, if progress is behind schedule, the learning schedule is adjusted. The Inheritance Department also uses AI to monitor the learning progress of the next generation of master brewers in real time and provide feedback according to the progress. For example, advice is given based on the accuracy and efficiency of work in a particular process. This allows AI to monitor the progress of the next generation of master brewers in real time and adjust the learning content as needed using digitized data.

[0036] The Transfer Department can apply the digitized technology and experience to other alcoholic beverage and food manufacturing industries, building a broad-based technology transfer system. For example, the Transfer Department can apply the digitized technology and experience of master brewers to the production techniques of other alcoholic beverages, such as wine and beer, to build a broad-based technology transfer system. For example, it can share fermentation management and temperature control techniques. The Transfer Department can also digitize the technology and experience of food manufacturing and integrate it with the technology and experience of master brewers to build a broad-based technology transfer system. For example, it can digitize and share production techniques for fermented foods and seasonings. The Transfer Department can also extract common technology and knowledge from digitized data from other alcoholic beverage and food manufacturing industries to build a broad-based technology transfer system. For example, it can analyze common work procedures and judgment criteria and create a database. This allows the digitized technology and experience to be applied to other alcoholic beverage and food manufacturing industries, building a broad-based technology transfer system.

[0037] The Inheritance Department can introduce gamification elements into the learning platform, allowing the next generation of master brewers to learn their skills while having fun. For example, the Inheritance Department can introduce a point system or badges into the learning platform, allowing the next generation of master brewers to learn their skills while having fun. For example, points can be earned and badges can be collected each time a specific process is completed. The Inheritance Department can also develop a simulation game that incorporates gamification elements, allowing the next generation of master brewers to learn their skills while having fun. For example, fermentation management and temperature control can be learned in a game format. The Inheritance Department can also introduce a ranking system into the learning platform, allowing the next generation of master brewers to learn their skills while competing. For example, rankings can be displayed according to learning progress and skill acquisition level. In this way, gamification elements can be introduced into the learning platform, allowing the next generation of master brewers to learn their skills while having fun.

[0038] The optimization unit can perform a detailed analysis of the materials and conditions used in each step of the manufacturing process and propose the optimal combination. For example, the optimization unit uses AI to perform a detailed analysis of the type and quality of rice used in each step of the manufacturing process and propose the optimal combination. For example, it analyzes the combination of a specific type of rice and fermentation conditions and proposes the optimal conditions. The optimization unit can also perform a detailed analysis of the type and amount of water used in each step of the manufacturing process and propose the optimal combination. For example, it proposes the optimal type and amount of water based on the water's hardness and mineral content. The optimization unit can also perform a detailed analysis of the temperature and time used in each step of the manufacturing process and propose the optimal combination. For example, it analyzes and proposes the optimal combination of fermentation temperature and time. This allows the optimization unit to perform a detailed analysis of the materials and conditions used in each step of the manufacturing process and propose the optimal combination.

[0039] The optimization department will monitor minute changes during the manufacturing process in real time, allowing AI to instantly make optimal adjustments. For example, the optimization department will build a system that monitors fluctuations in temperature and humidity during the manufacturing process in real time, allowing AI to instantly make optimal adjustments. For example, it will monitor the temperature and humidity of fermentation tanks in real time and adjust as needed. The optimization department will also develop a system that uses AI to analyze minute changes during the manufacturing process in real time and make optimal adjustments. For example, it will adjust temperature and humidity according to the progress of fermentation. The optimization department will also introduce a system that monitors minute changes during the manufacturing process in real time, allowing AI to instantly make optimal adjustments. For example, it will automatically make adjustments according to fluctuations in temperature and humidity. This will allow AI to monitor minute changes during the manufacturing process in real time, allowing AI to instantly make optimal adjustments.

[0040] The optimization unit can apply the optimization system for the manufacturing process to the manufacturing of other fermented foods and beverages, thereby building a wide-ranging manufacturing optimization system. The optimization unit can apply the optimization system for the manufacturing of other fermented foods, such as cheese and yogurt, to build a wide-ranging manufacturing optimization system. For example, it optimizes fermentation conditions and temperature control. The optimization unit can also apply the optimization system to the manufacturing processes of other beverages, such as beer and wine, to build a wide-ranging manufacturing optimization system. For example, it optimizes fermentation temperature and time. The optimization unit can also extract common technologies and knowledge based on digitized data of the manufacturing processes of other fermented foods and beverages, to build a wide-ranging manufacturing optimization system. For example, it can analyze common work procedures and judgment criteria and create a database. This allows the optimization system for the manufacturing process to be applied to the manufacturing of other fermented foods and beverages, thereby building a wide-ranging manufacturing optimization system.

[0041] The optimization department can promote innovative product development by adding an experimental mode to the manufacturing process optimization system, which tests new manufacturing methods and recipes suggested by AI. For example, the optimization department can add an experimental mode to the manufacturing process optimization system, allowing it to test new manufacturing methods and recipes suggested by AI. For example, it can try new fermentation conditions or combinations of ingredients. The optimization department can also use the experimental mode to test innovative manufacturing methods and recipes suggested by AI and develop products based on the results. For example, it can develop sake with new flavors and aromas. The optimization department can also introduce an experimental mode to the manufacturing process optimization system, which tests new manufacturing methods and recipes suggested by AI, thereby diversifying products. For example, it can develop special seasonal sake. In this way, adding an experimental mode to the manufacturing process optimization system and testing new manufacturing methods and recipes suggested by AI can promote innovative product development.

[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 sake production support system can further include a quality evaluation unit. The quality evaluation unit can evaluate the quality of the produced sake in real time and provide feedback to maintain consistent quality. For example, AI can analyze the aroma, taste, and color of the sake and evaluate it based on quality standards. The quality evaluation unit can also identify factors that cause quality fluctuations based on data acquired during the production process and make suggestions for improvement. For example, it can suggest fine-tuning fermentation temperature and time. The quality evaluation unit can also collect consumer feedback and improve quality based on that data. For example, it can analyze consumer preferences and trends and reflect them in product development. This makes it possible to evaluate the quality of sake in real time and provide feedback to maintain consistent quality.

[0044] The sake production support system can further include an energy management unit. The energy management unit can monitor energy consumption in the production process in real time and propose efficient energy usage. For example, it can optimize energy consumption in temperature management of fermentation tanks. The energy management unit can also promote the use of renewable energy and make proposals to reduce environmental impact. For example, it can propose the introduction of solar power generation or wind power generation. The energy management unit can also make improvement proposals to improve the energy efficiency of the entire production process based on energy consumption data. For example, it can propose peak energy consumption shifts. This makes it possible to monitor energy consumption in the production process in real time and propose efficient energy usage.

[0045] The sake brewing support system can further include a supply chain management section. The supply chain management section can manage all processes from raw material procurement to product shipment, building an efficient supply chain. For example, it manages inventory of raw materials such as rice and water, and procures the appropriate amount at the required time. The supply chain management section can also optimize product shipping schedules to ensure prompt delivery to customers. For example, it can create shipping plans based on demand forecasts for shipping destinations. The supply chain management section can also analyze data from the entire supply chain and make improvement proposals for efficiency. For example, it can propose reducing logistics costs and improving inventory turnover. This makes it possible to manage all processes from raw material procurement to product shipment, building an efficient supply chain.

[0046] The sake brewing support system can further include a marketing support unit. The marketing support unit can analyze market trends for products and propose effective marketing strategies. For example, it can analyze consumer preferences and purchase history to identify target markets. The marketing support unit can also support product branding and promotional activities and make proposals for improving brand value. For example, it can propose promotion strategies that utilize social media and online advertising. The marketing support unit can also support product sales forecasts and inventory management based on sales data. For example, it can forecast seasonal demand and maintain appropriate inventory levels. This makes it possible to analyze market trends for products and propose effective marketing strategies.

[0047] The sake brewing support system can further include a customer feedback collection unit. The customer feedback collection unit can collect feedback from consumers and make product improvements based on that data. For example, it can collect consumer opinions through online surveys and review sites. The customer feedback collection unit can also analyze the collected feedback and use it to improve products and develop new products. For example, it can analyze consumer preferences and trends to develop sake with new flavors and designs. The customer feedback collection unit can also make suggestions to strengthen communication with consumers and increase brand loyalty. For example, it can build relationships with consumers through regular events and campaigns. This allows it to collect feedback from consumers and make product improvements based on that data.

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

[0049] Step 1: The Technology and Experience Digitalization Department digitizes the brewer's skills and experience. For example, the brewer's work and decisions are recorded, and the data is analyzed by AI. Specifically, the brewer's decision-making criteria and work procedures for each process, such as rice selection, rice washing, and fermentation management, are recorded in detail. Step 2: The Succession Department passes on the skills and experience digitized by the Skills and Experience Digitization Department to the next generation of master brewers. For example, based on data analyzed by AI, the department provides a curriculum of what the next generation of master brewers should learn. Specifically, the department allows the next generation of master brewers to learn by experiencing actual work through online courses and simulations. Step 3: The optimization unit optimizes the manufacturing process based on the data digitized by the technology and experience digitization unit. For example, it proposes optimal conditions for each process based on the digitized data. Specifically, the AI ​​analyzes and proposes optimal conditions such as fermentation temperature and time, type of rice, and amount of water.

[0050] (Example 2) The sake brewing support system according to the embodiment of the present invention is a system that digitizes the techniques and experience of master brewers, passes them on to the next generation of master brewers, and optimizes the brewing process. As a result, the sake brewing support system digitizes the techniques and experience of master brewers, passes them on to the next generation of master brewers, and optimizes the brewing process.

[0051] The sake brewing support system according to the embodiment includes a skill and experience digitization unit, a succession unit, and an optimization unit. The skill and experience digitization unit digitizes the skills and experience of the master brewers. For example, it records the tasks and decisions made by the master brewers, and AI analyzes the data. For example, it records in detail the master brewers' judgment criteria and work procedures for each process, such as rice selection, rice washing, and fermentation management. The succession unit passes on the skills and experience digitized by the skill and experience digitization unit to the next generation of master brewers. For example, it provides a curriculum based on the data analyzed by AI, providing content that the next generation of master brewers should learn. For example, it allows the next generation of master brewers to learn by experiencing actual tasks through online courses and simulations. The optimization unit optimizes the brewing process based on the data digitized by the skill and experience digitization unit. For example, it proposes optimal conditions for each process based on the digitized data. For example, AI analyzes and proposes optimal conditions such as fermentation temperature and time, type of rice, and amount of water. As a result, the sake brewing support system according to the embodiment can digitize the techniques and experience of the master brewers, pass them on to the next generation of master brewers, and optimize the brewing process.

[0052] The Technique and Experience Digitalization Department uses high-precision cameras to record the brewer's subtle movements and facial expressions while he works, and AI analyzes these movements and changes in facial expression to digitize the nuances of his technique. For example, the Technique and Experience Digitalization Department uses high-precision cameras to record the brewer's hand movements and the amount of force he applies when sorting rice, and AI analyzes these movements and saves them as digital data. For example, it analyzes the speed and pressure of hand movements to clarify sorting criteria. The Technique and Experience Digitalization Department also uses high-precision cameras to record the brewer's facial expressions and body movements while managing fermentation, and AI analyzes these changes to digitize the nuances of his technique. For example, it analyzes changes in facial expression depending on the progress of fermentation. The Technique and Experience Digitalization Department also uses high-precision cameras to record how the brewer holds and uses the tools he uses while working, and AI analyzes these movements and saves them as digital data. For example, it analyzes the angle of the tools and the amount of force applied to clarify the details of the technique. This makes it possible to analyze the brewer's subtle movements and facial expressions, and digitize the nuances of his technique.

[0053] The Technology and Experience Digitalization Department uses AI to analyze the selection criteria for the tools and materials used by the master brewer and digitize the reasons for selection and background knowledge. For example, the Technology and Experience Digitalization Department uses AI to analyze the criteria used by the master brewer to select the type and quality of rice used and save the reasons for selection and background knowledge as digital data. For example, it clarifies selection criteria based on the size, color, and aroma of the rice grains. The Technology and Experience Digitalization Department also uses AI to analyze the selection criteria for the water used by the master brewer and digitize the reasons for selection and background knowledge. For example, it clarifies selection criteria based on the water's hardness and mineral content. The Technology and Experience Digitalization Department also uses AI to analyze the selection criteria for the tools used by the master brewer and save the reasons for selection and background knowledge as digital data. For example, it clarifies selection criteria based on the material, shape, and ease of use of the tools. This makes it possible to analyze the selection criteria for the tools and materials used by the master brewer and digitize the reasons for selection and background knowledge.

[0054] The technology and experience digitization unit can use the emotion estimation function to analyze the stress and satisfaction felt by the master brewer while he works and store the emotion data as part of the technology digitization. For example, the technology and experience digitization unit can use the emotion estimation function to analyze the stress felt by the master brewer while he works and digitize the data. For example, it can measure stress levels based on changes in heart rate and facial expressions and store the data. The technology and experience digitization unit can also use the emotion estimation function to analyze the satisfaction felt by the master brewer while he works and digitize the data. For example, it can analyze facial expressions and tone of voice according to the progress of the work and store the satisfaction data. The technology and experience digitization unit can also use the emotion estimation function to analyze changes in the emotions felt by the master brewer while he works in real time and store the data as part of the technology digitization. For example, it can collect and analyze emotion data for each step of the work. This allows it to analyze the emotions felt by the master brewer while he works and store the emotion data as part of the technology digitization.

[0055] When digitizing the skills and experience of a master brewer, the Skills and Experience Digitization Department can apply this to other traditional crafts and agricultural techniques, building a broad-based skill inheritance system. For example, when digitizing the skills and experience of a master brewer, the Skills and Experience Digitization Department can also digitize the techniques of other traditional crafts, such as pottery and lacquerware, to build a skill inheritance system. For example, the department records and analyzes the potter's hand movements and tool usage. The Skills and Experience Digitization Department also digitizes agricultural techniques and integrates them with the master brewer's skills and experience to build a broad-based skill inheritance system. For example, rice cultivation methods and harvesting techniques are digitized and linked to the master brewer's skills. The Skills and Experience Digitization Department also extracts common skills and knowledge from the digitized data of other traditional crafts and agricultural techniques to build a broad-based skill inheritance system. For example, common work procedures and judgment criteria are analyzed and compiled into a database. This allows the digitization of a master brewer's skills and experience to be applied to other traditional crafts and agricultural techniques, building a broad-based skill inheritance system.

[0056] The Skills and Experience Digitalization Department can recreate digitized skills and experiences using VR technology, allowing the next generation of master brewers to experience actual work in a virtual space. For example, the Skills and Experience Digitalization Department can recreate digitized skills and experiences of master brewers using VR technology, allowing the next generation of master brewers to experience rice sorting and washing in a virtual space. For example, they can wear VR goggles and perform the work in the virtual space. The Skills and Experience Digitalization Department can also recreate processes such as fermentation management and temperature adjustment using VR technology, allowing the next generation of master brewers to experience actual work in the virtual space. For example, it can simulate adjusting the temperature of a fermentation tank in a virtual space. The Skills and Experience Digitalization Department can also use VR technology to recreate the master brewer's work environment and how to use tools in a virtual space, allowing the next generation of master brewers to experience actual work. For example, they can learn how to hold and use tools in a virtual space. In this way, the digitized skills and experiences can be recreated using VR technology, allowing the next generation of master brewers to experience actual work in a virtual space.

[0057] The Technology and Experience Digitalization Department uses the emotion estimation function to analyze the emotions felt by the master brewer while he works in real time and can make suggestions for improving the work environment based on that data. For example, the Technology and Experience Digitalization Department uses the emotion estimation function to analyze the stress and fatigue felt by the master brewer while he works in real time and can make suggestions for improving the work environment based on that data. For example, it can propose adjustments to work hours and break times. The Technology and Experience Digitalization Department also uses the emotion estimation function to analyze the satisfaction and sense of accomplishment felt by the master brewer while he works and can make suggestions for improving the work environment based on that data. For example, it can provide feedback according to the progress of the work. The Technology and Experience Digitalization Department also uses the emotion estimation function to analyze changes in the emotions felt by the master brewer while he works in real time and can make suggestions for improving the work environment based on that data. For example, it can propose adjustments to the temperature and humidity of the work environment. This allows the Technology and Experience Digitalization Department to analyze the emotions felt by the master brewer while he works in real time and can make suggestions for improving the work environment based on that data.

[0058] The Inheritance Department uses AI to generate individually customized learning plans based on digitized skills and experience, and can provide them to the next generation of master brewers. For example, the Inheritance Department uses AI to generate customized learning plans based on the skills and experience of the next generation of master brewers. For example, it provides basic courses for beginners and advanced courses for advanced brewers. The Inheritance Department also uses AI to monitor the learning progress of the next generation of master brewers in real time and adjust the learning plans as needed. For example, it provides additional learning content if the level of understanding of a particular process is low. The Inheritance Department also uses AI to provide individually customized feedback to the next generation of master brewers based on digitized skills and experience. For example, it provides advice based on the accuracy and efficiency of the work. This allows the AI ​​to generate individually customized learning plans based on digitized skills and experience, and provide them to the next generation of master brewers.

[0059] The Inheritance Department uses digitized data to enable AI to monitor the progress of the next generation of master brewers in real time and adjust the learning content as needed. For example, the Inheritance Department uses AI to monitor the learning progress of the next generation of master brewers in real time and adjust the learning content according to the progress. For example, if the level of understanding of a particular process is low, additional learning content is provided. The Inheritance Department also uses AI to evaluate the learning progress of the next generation of master brewers based on the digitized data and reconstruct the learning plan as needed. For example, if progress is behind schedule, the learning schedule is adjusted. The Inheritance Department also uses AI to monitor the learning progress of the next generation of master brewers in real time and provide feedback according to the progress. For example, advice is given based on the accuracy and efficiency of work in a particular process. This allows AI to monitor the progress of the next generation of master brewers in real time and adjust the learning content as needed using digitized data.

[0060] The inheritance unit can use the emotion estimation function to analyze the emotions felt by the next generation of master brewers while they are studying and provide feedback to maintain their motivation to study. For example, the inheritance unit can use the emotion estimation function to analyze the stress and fatigue felt by the next generation of master brewers while they are studying and provide feedback to maintain their motivation based on that data. For example, the inheritance unit can suggest the timing of breaks and ways to refresh. The inheritance unit can also use the emotion estimation function to analyze the satisfaction and sense of accomplishment felt by the next generation of master brewers while they are studying and provide feedback to maintain their motivation based on that data. For example, the inheritance unit can send encouraging messages according to the progress of their studies. The inheritance unit can also use the emotion estimation function to analyze changes in the emotions felt by the next generation of master brewers while they are studying in real time and provide feedback to maintain their motivation based on that data. For example, the inheritance unit can suggest adjustments to the study content or changes in difficulty. In this way, the emotion estimation function can be used to analyze the emotions felt by the next generation of master brewers while they are studying and provide feedback to maintain their motivation to study.

[0061] The Transfer Department can apply the digitized technology and experience to other alcoholic beverage and food manufacturing industries, building a broad-based technology transfer system. For example, the Transfer Department can apply the digitized technology and experience of master brewers to the production techniques of other alcoholic beverages, such as wine and beer, to build a broad-based technology transfer system. For example, it can share fermentation management and temperature control techniques. The Transfer Department can also digitize the technology and experience of food manufacturing and integrate it with the technology and experience of master brewers to build a broad-based technology transfer system. For example, it can digitize and share production techniques for fermented foods and seasonings. The Transfer Department can also extract common technology and knowledge from digitized data from other alcoholic beverage and food manufacturing industries to build a broad-based technology transfer system. For example, it can analyze common work procedures and judgment criteria and create a database. This allows the digitized technology and experience to be applied to other alcoholic beverage and food manufacturing industries, building a broad-based technology transfer system.

[0062] The Inheritance Department can introduce gamification elements into the learning platform, allowing the next generation of master brewers to learn their skills while having fun. For example, the Inheritance Department can introduce a point system or badges into the learning platform, allowing the next generation of master brewers to learn their skills while having fun. For example, points can be earned and badges can be collected each time a specific process is completed. The Inheritance Department can also develop a simulation game that incorporates gamification elements, allowing the next generation of master brewers to learn their skills while having fun. For example, fermentation management and temperature control can be learned in a game format. The Inheritance Department can also introduce a ranking system into the learning platform, allowing the next generation of master brewers to learn their skills while competing. For example, rankings can be displayed according to learning progress and skill acquisition level. In this way, gamification elements can be introduced into the learning platform, allowing the next generation of master brewers to learn their skills while having fun.

[0063] The inheritance unit can use the emotion estimation function to analyze in real time the emotions felt by the next generation of master brewers while they are learning, and provide a learning environment that draws out positive emotions. For example, the inheritance unit can use the emotion estimation function to analyze in real time the stress and fatigue felt by the next generation of master brewers while they are learning, and provide a learning environment that draws out positive emotions based on that data. For example, the inheritance unit can provide relaxing music or videos. The inheritance unit can also use the emotion estimation function to analyze the satisfaction and sense of accomplishment felt by the next generation of master brewers while they are learning, and provide a learning environment that draws out positive emotions based on that data. For example, the inheritance unit can send encouraging messages according to the learning progress. The inheritance unit can also use the emotion estimation function to analyze in real time the changes in emotions felt by the next generation of master brewers while they are learning, and provide a learning environment that draws out positive emotions based on that data. For example, the inheritance unit can suggest adjustments to the learning content or changes in difficulty. In this way, the emotion estimation function can be used to analyze in real time the emotions felt by the next generation of master brewers while they are learning, and provide a learning environment that draws out positive emotions.

[0064] The optimization unit can perform a detailed analysis of the materials and conditions used in each step of the manufacturing process and propose the optimal combination. For example, the optimization unit uses AI to perform a detailed analysis of the type and quality of rice used in each step of the manufacturing process and propose the optimal combination. For example, it analyzes the combination of a specific type of rice and fermentation conditions and proposes the optimal conditions. The optimization unit can also perform a detailed analysis of the type and amount of water used in each step of the manufacturing process and propose the optimal combination. For example, it proposes the optimal type and amount of water based on the water's hardness and mineral content. The optimization unit can also perform a detailed analysis of the temperature and time used in each step of the manufacturing process and propose the optimal combination. For example, it analyzes and proposes the optimal combination of fermentation temperature and time. This allows the optimization unit to perform a detailed analysis of the materials and conditions used in each step of the manufacturing process and propose the optimal combination.

[0065] The optimization department will monitor minute changes during the manufacturing process in real time, allowing AI to instantly make optimal adjustments. For example, the optimization department will build a system that monitors fluctuations in temperature and humidity during the manufacturing process in real time, allowing AI to instantly make optimal adjustments. For example, it will monitor the temperature and humidity of fermentation tanks in real time and adjust as needed. The optimization department will also develop a system that uses AI to analyze minute changes during the manufacturing process in real time and make optimal adjustments. For example, it will adjust temperature and humidity according to the progress of fermentation. The optimization department will also introduce a system that monitors minute changes during the manufacturing process in real time, allowing AI to instantly make optimal adjustments. For example, it will automatically make adjustments according to fluctuations in temperature and humidity. This will allow AI to monitor minute changes during the manufacturing process in real time, allowing AI to instantly make optimal adjustments.

[0066] The optimization unit uses the emotion estimation function to analyze the emotions felt by the master brewer during the production process and can make process improvement suggestions based on that data. For example, the optimization unit uses the emotion estimation function to analyze the stress and fatigue felt by the master brewer during the production process and can make process improvement suggestions based on that data. For example, it can propose adjustments to work hours and break times. The optimization unit also uses the emotion estimation function to analyze the satisfaction and sense of accomplishment felt by the master brewer during the production process and can make process improvement suggestions based on that data. For example, it can provide feedback according to the progress of the work. The optimization unit also uses the emotion estimation function to analyze changes in the emotions felt by the master brewer during the production process in real time and can make process improvement suggestions based on that data. For example, it can propose adjustments to the temperature and humidity of the work environment. In this way, the emotion estimation function can be used to analyze the emotions felt by the master brewer during the production process and can make process improvement suggestions based on that data.

[0067] The optimization unit can apply the optimization system for the manufacturing process to the manufacturing of other fermented foods and beverages, thereby building a wide-ranging manufacturing optimization system. The optimization unit can apply the optimization system for the manufacturing of other fermented foods, such as cheese and yogurt, to build a wide-ranging manufacturing optimization system. For example, it optimizes fermentation conditions and temperature control. The optimization unit can also apply the optimization system to the manufacturing processes of other beverages, such as beer and wine, to build a wide-ranging manufacturing optimization system. For example, it optimizes fermentation temperature and time. The optimization unit can also extract common technologies and knowledge based on digitized data of the manufacturing processes of other fermented foods and beverages, to build a wide-ranging manufacturing optimization system. For example, it can analyze common work procedures and judgment criteria and create a database. This allows the optimization system for the manufacturing process to be applied to the manufacturing of other fermented foods and beverages, thereby building a wide-ranging manufacturing optimization system.

[0068] The optimization department can promote innovative product development by adding an experimental mode to the manufacturing process optimization system, which tests new manufacturing methods and recipes suggested by AI. For example, the optimization department can add an experimental mode to the manufacturing process optimization system, allowing it to test new manufacturing methods and recipes suggested by AI. For example, it can try new fermentation conditions or combinations of ingredients. The optimization department can also use the experimental mode to test innovative manufacturing methods and recipes suggested by AI and develop products based on the results. For example, it can develop sake with new flavors and aromas. The optimization department can also introduce an experimental mode to the manufacturing process optimization system, which tests new manufacturing methods and recipes suggested by AI, thereby diversifying products. For example, it can develop special seasonal sake. In this way, adding an experimental mode to the manufacturing process optimization system and testing new manufacturing methods and recipes suggested by AI can promote innovative product development.

[0069] The optimization unit can use the emotion estimation function to analyze the emotions felt by the master brewer during the production process in real time, and provide a production environment that draws out positive emotions. For example, the optimization unit can use the emotion estimation function to analyze the stress and fatigue felt by the master brewer during the production process in real time, and provide a production environment that draws out positive emotions based on the data. For example, the optimization unit can suggest adjustments to work hours and break times. The optimization unit can also use the emotion estimation function to analyze the satisfaction and sense of accomplishment felt by the master brewer during the production process, and provide a production environment that draws out positive emotions based on the data. For example, the optimization unit can provide feedback according to the progress of the work. The optimization unit can also use the emotion estimation function to analyze changes in the emotions felt by the master brewer during the production process in real time, and provide a production environment that draws out positive emotions based on the data. For example, the optimization unit can suggest adjustments to the temperature and humidity of the work environment. In this way, the optimization unit can use the emotion estimation function to analyze the emotions felt by the master brewer during the production process in real time, and provide a production environment that draws out positive emotions based on the data.

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

[0071] The sake production support system can further include a quality evaluation unit. The quality evaluation unit can evaluate the quality of the produced sake in real time and provide feedback to maintain consistent quality. For example, AI can analyze the aroma, taste, and color of the sake and evaluate it based on quality standards. The quality evaluation unit can also identify factors that cause quality fluctuations based on data acquired during the production process and make suggestions for improvement. For example, it can suggest fine-tuning fermentation temperature and time. The quality evaluation unit can also collect consumer feedback and improve quality based on that data. For example, it can analyze consumer preferences and trends and reflect them in product development. This makes it possible to evaluate the quality of sake in real time and provide feedback to maintain consistent quality.

[0072] The sake production support system can further include an energy management unit. The energy management unit can monitor energy consumption in the production process in real time and propose efficient energy usage. For example, it can optimize energy consumption in temperature management of fermentation tanks. The energy management unit can also promote the use of renewable energy and make proposals to reduce environmental impact. For example, it can propose the introduction of solar power generation or wind power generation. The energy management unit can also make improvement proposals to improve the energy efficiency of the entire production process based on energy consumption data. For example, it can propose peak energy consumption shifts. This makes it possible to monitor energy consumption in the production process in real time and propose efficient energy usage.

[0073] The sake brewing support system can further include a supply chain management section. The supply chain management section can manage all processes from raw material procurement to product shipment, building an efficient supply chain. For example, it manages inventory of raw materials such as rice and water, and procures the appropriate amount at the required time. The supply chain management section can also optimize product shipping schedules to ensure prompt delivery to customers. For example, it can create shipping plans based on demand forecasts for shipping destinations. The supply chain management section can also analyze data from the entire supply chain and make improvement proposals for efficiency. For example, it can propose reducing logistics costs and improving inventory turnover. This makes it possible to manage all processes from raw material procurement to product shipment, building an efficient supply chain.

[0074] The sake brewing support system can further include a marketing support unit. The marketing support unit can analyze market trends for products and propose effective marketing strategies. For example, it can analyze consumer preferences and purchase history to identify target markets. The marketing support unit can also support product branding and promotional activities and make proposals for improving brand value. For example, it can propose promotion strategies that utilize social media and online advertising. The marketing support unit can also support product sales forecasts and inventory management based on sales data. For example, it can forecast seasonal demand and maintain appropriate inventory levels. This makes it possible to analyze market trends for products and propose effective marketing strategies.

[0075] The sake brewing support system can further include a customer feedback collection unit. The customer feedback collection unit can collect feedback from consumers and make product improvements based on that data. For example, it can collect consumer opinions through online surveys and review sites. The customer feedback collection unit can also analyze the collected feedback and use it to improve products and develop new products. For example, it can analyze consumer preferences and trends to develop sake with new flavors and designs. The customer feedback collection unit can also make suggestions to strengthen communication with consumers and increase brand loyalty. For example, it can build relationships with consumers through regular events and campaigns. This allows it to collect feedback from consumers and make product improvements based on that data.

[0076] The sake brewing support system can further use an emotion estimation function to analyze the emotions of the master brewer and make suggestions to improve work efficiency. For example, the emotion estimation function can analyze the stress and fatigue felt by the master brewer while working, and make suggestions to improve work efficiency based on that data. For example, it can suggest adjustments to work hours and break times. The emotion estimation function can also be used to analyze the satisfaction and sense of accomplishment felt by the master brewer while working, and make suggestions to improve work efficiency based on that data. For example, it can provide feedback according to the progress of the work. The emotion estimation function can also be used to analyze changes in the emotions felt by the master brewer while working in real time, and make suggestions to improve work efficiency based on that data. For example, it can suggest adjusting the temperature and humidity of the work environment. In this way, it is possible to analyze the emotions of the master brewer and make suggestions to improve work efficiency.

[0077] The sake brewing support system can further use an emotion estimation function to analyze the emotions felt by the next-generation master brewers while they are studying and make suggestions to maximize the effectiveness of their studies. For example, the emotion estimation function can analyze the stress and fatigue felt by the next-generation master brewers while studying and make suggestions to maximize the effectiveness of their studies based on that data. For example, it can suggest adjustments to study time and break times. The emotion estimation function can also be used to analyze the satisfaction and sense of accomplishment felt by the next-generation master brewers while studying and make suggestions to maximize the effectiveness of their studies based on that data. For example, it can provide feedback according to the progress of their studies. The emotion estimation function can also be used to analyze changes in the emotions felt by the next-generation master brewers while studying in real time and make suggestions to maximize the effectiveness of their studies based on that data. For example, it can suggest adjusting the temperature and humidity of the study environment. In this way, it is possible to analyze the emotions felt by the next-generation master brewers while studying and make suggestions to maximize the effectiveness of their studies.

[0078] The sake brewing support system can further use an emotion estimation function to analyze consumer emotions and optimize marketing strategies. For example, the emotion estimation function can analyze the emotions consumers have when tasting a product, and the marketing strategy can be optimized based on that data. For example, promotional activities can be adjusted based on consumer preferences and reactions. The emotion estimation function can also be used to analyze the emotions consumers have when purchasing a product, and the marketing strategy can be optimized based on that data. For example, post-purchase satisfaction and repeat purchase rates can be analyzed and reflected in the marketing strategy. The emotion estimation function can also be used to analyze changes in consumers' emotions toward a product in real time, and the marketing strategy can be optimized based on that data. For example, customized promotions can be proposed based on the consumer's emotions. In this way, consumer emotions can be analyzed and marketing strategies can be optimized.

[0079] The sake brewing support system can further use an emotion estimation function to analyze the emotions felt by the master brewer during the brewing process and make suggestions for improving the brewing process. For example, the emotion estimation function can analyze the stress and fatigue felt by the master brewer during the brewing process and make suggestions for improving the brewing process based on that data. For example, it can propose adjustments to work hours and break times. The emotion estimation function can also be used to analyze the satisfaction and sense of accomplishment felt by the master brewer during the brewing process and make suggestions for improving the brewing process based on that data. For example, it can provide feedback according to the progress of the work. The emotion estimation function can also be used to analyze changes in the emotions felt by the master brewer during the brewing process in real time and make suggestions for improving the brewing process based on that data. For example, it can propose adjustments to the temperature and humidity of the work environment. In this way, the emotions felt by the master brewer during the brewing process can be analyzed and suggestions for improving the brewing process can be made.

[0080] The sake brewing support system can further use an emotion estimation function to analyze the emotions felt by the next generation of master brewers while they are studying and provide feedback to maintain their motivation to study. For example, the emotion estimation function can analyze the stress and fatigue felt by the next generation of master brewers while studying and provide feedback to maintain their motivation based on that data. For example, the system can suggest the timing of breaks and ways to refresh. The emotion estimation function can also be used to analyze the satisfaction and sense of accomplishment felt by the next generation of master brewers while studying and provide feedback to maintain their motivation based on that data. For example, the system can send encouraging messages according to the progress of their studies. The emotion estimation function can also be used to analyze changes in the emotions felt by the next generation of master brewers while studying in real time and provide feedback to maintain their motivation based on that data. For example, the system can suggest adjustments to the study content or changes in difficulty. In this way, the system can analyze the emotions felt by the next generation of master brewers while studying and provide feedback to maintain their motivation to study.

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

[0082] Step 1: The Technology and Experience Digitalization Department digitizes the brewer's skills and experience. For example, the brewer's work and decisions are recorded, and the data is analyzed by AI. Specifically, the brewer's decision-making criteria and work procedures for each process, such as rice selection, rice washing, and fermentation management, are recorded in detail. Step 2: The Succession Department passes on the skills and experience digitized by the Skills and Experience Digitization Department to the next generation of master brewers. For example, based on data analyzed by AI, the department provides a curriculum of what the next generation of master brewers should learn. Specifically, the department allows the next generation of master brewers to learn by experiencing actual work through online courses and simulations. Step 3: The optimization unit optimizes the manufacturing process based on the data digitized by the technology and experience digitization unit. For example, it proposes optimal conditions for each process based on the digitized data. Specifically, the AI ​​analyzes and proposes optimal conditions such as fermentation temperature and time, type of rice, and amount of water.

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

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

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

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

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

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

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

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

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

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

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

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

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Claims

1. The Technology and Experience Digitalization Department digitizes the skills and experience of the master brewers, The Technology and Experience Digitization Department will pass on the digitized skills and experience to the next generation of master brewers. an optimization unit that optimizes the manufacturing process based on the data digitized by the technology and experience digitization unit; A system characterized by:

2. The Technology and Experience Digitization Department: The brewer's subtle movements and facial expressions while working are recorded with a high-precision camera, and the AI ​​analyzes these movements and changes in facial expressions to digitize the nuances of the technique.

2. The system of claim 1.

3. The Technology and Experience Digitization Department: The AI ​​analyzes the selection criteria for tools and ingredients used by the master brewer and digitizes the reasons for selection and background knowledge.

2. The system of claim 1.

4. The Technology and Experience Digitization Department: The stress and satisfaction felt by the master brewer while working will be analyzed, and this emotional data will be saved as part of the digitalization of the technology.

2. The system of claim 1.

5. The Technology and Experience Digitization Department: When digitizing the techniques and experience of the master brewers, we will also apply this to traditional crafts and agricultural techniques, creating a wide-ranging technology inheritance system.

2. The system of claim 1.

Images