system

Abstract

The system according to this embodiment aims to appropriately manage the drying process of dried fish and support the production of high-quality dried fish in the optimal environment and time. [Solution] The system according to the embodiment comprises an acquisition unit, an evaluation unit, a measurement unit, a camera unit, and a voice assistant unit. The acquisition unit acquires environmental data from the internet. The evaluation unit evaluates the drying status of the dried fish based on the environmental data acquired by the acquisition unit. The measurement unit measures the weight change of the dried fish in real time. The camera unit checks the shape and color changes of the dried fish. The voice assistant unit provides advice and notifications of progress in a conversational format.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to confirm the drying environment of dried foods and quantitatively manage the moisture content, and it is difficult to visually confirm the progress of drying.

[0005] The system according to the embodiment aims to appropriately manage the drying status of dried foods and support the production of high-quality dried foods in an optimal environment and time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an evaluation unit, a measurement unit, a camera unit, and a voice assistant unit. The acquisition unit acquires environmental data from the internet. The evaluation unit evaluates the drying status of the dried fish based on the environmental data acquired by the acquisition unit. The measurement unit measures the weight change of the dried fish in real time. The camera unit checks the shape and color changes of the dried fish. The voice assistant unit provides advice and notifications of progress in a conversational format. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately manage the drying process of dried fish and support the production of high-quality dried fish in the optimal environment and time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dried fish drying management system according to an embodiment of the present invention is a system for appropriately managing the drying environment of dried fish and producing high-quality dried fish. This dried fish drying management system acquires environmental data from the internet, evaluates the drying status of the dried fish, measures the weight change of the dried fish in real time, checks the changes in shape and color of the dried fish, and provides interactive advice and progress notifications. For example, the dried fish drying management system can acquire real-time weather and humidity online using location information. For example, it can acquire weather forecasts and current humidity information for the place where the dried fish is to be dried and determine whether the environment is suitable for drying the dried fish. Next, the dried fish drying management system can measure the weight change of the dried fish in real time using a weighing scale incorporated into a dedicated drying net. For example, it can check whether the weight of the dried fish is decreasing and determine whether drying is progressing. Furthermore, the dried fish drying management system can use a dedicated camera for visual change analysis to check the changes in shape and color of the dried fish and evaluate the environmental conditions. For example, it can check whether the color of the dried fish is changing and determine whether drying is progressing. Finally, the dried fish drying management system can use a voice assistant to provide interactive advice and progress notifications. For example, it can provide voice notifications about the progress of the drying process and offer necessary advice. This system allows for proper management of the drying process, supporting the creation of high-quality dried fish in the optimal environment and time. Even DIY users can produce dried fish at a professional level. For instance, it is designed to be easy for beginners to use, and quality control is simple, allowing even beginners to easily make delicious dried fish. Furthermore, products for cooking enthusiasts come in a variety of sizes and shapes, possessing not only the necessary functions for dried fish production but also user-friendly and beautiful designs. In addition, products for those who want to make professional-level dried fish at home are equipped with advanced functions, allowing for free adjustment of temperature, humidity, and airflow, enabling fully automatic dried fish production. In summary, the dried fish drying management system can properly manage the drying process, supporting the creation of high-quality dried fish in the optimal environment and time.

[0029] The dried fish drying management system according to this embodiment comprises an acquisition unit, an evaluation unit, a measurement unit, a camera unit, and a voice assistant unit. The acquisition unit acquires environmental data from the internet. The acquisition unit can, for example, acquire real-time weather and humidity online using location information. The evaluation unit evaluates the drying status of the dried fish based on the environmental data acquired by the acquisition unit. The evaluation unit can, for example, evaluate the drying status of the dried fish based on environmental data such as temperature, humidity, and wind speed. The measurement unit measures the weight change of the dried fish in real time. The measurement unit can, for example, measure the weight change of the dried fish in real time using a weighing scale incorporated into a dedicated drying net. The camera unit checks the shape and color changes of the dried fish. The camera unit can, for example, check the shape and color changes of the dried fish using image analysis and also evaluate environmental conditions. The voice assistant unit provides advice and progress notifications in a conversational format. The voice assistant unit can, for example, notify the user by voice whether the drying of the dried fish is progressing and provide necessary advice. As a result, the dried fish drying management system according to this embodiment can appropriately manage the drying status of the dried fish and support the production of high-quality dried fish in the optimal environment and time.

[0030] The data acquisition unit obtains environmental data from the internet. For example, it can use location information to obtain real-time weather and humidity online. Specifically, the unit uses GPS functionality to determine its current location and accesses a weather database based on that location information. From the weather database, it can obtain detailed environmental data such as current weather, temperature, humidity, wind speed, and precipitation. This data is extremely important in the dried fish drying process and is essential for maintaining an appropriate drying environment. Furthermore, the data acquisition unit can periodically update environmental data and respond to real-time changing weather conditions. For example, if the weather suddenly changes or the humidity rises, the data acquisition unit can immediately acquire new data and reflect it throughout the entire system. This ensures that the drying conditions for dried fish are always kept in an optimal state. In addition, the data acquisition unit can collect information from multiple weather data sources via the internet, improving the reliability and accuracy of the data. For example, by integrating data not only from the Japan Meteorological Agency but also from private weather services and local weather stations, it can obtain more accurate environmental data. As a result, the data acquisition unit can provide optimal environmental conditions in the dried fish drying process and support the production of high-quality dried fish.

[0031] The evaluation unit assesses the drying status of dried fish based on environmental data acquired by the acquisition unit. The evaluation unit can assess the drying status of dried fish based on environmental data such as temperature, humidity, and wind speed. Specifically, the evaluation unit analyzes the acquired environmental data and determines the optimal conditions for drying the dried fish. For example, if the temperature is too high, there is a risk of over-drying the dried fish, and if the humidity is too high, there is a risk of mold growth. Based on this data, the evaluation unit makes adjustments to maintain appropriate drying conditions. The evaluation unit uses AI to analyze environmental data and monitors the drying process of the dried fish in real time. Based on past data and statistical information, the AI ​​predicts how current environmental conditions will affect the drying of the dried fish and provides optimal drying conditions. For example, the AI ​​can learn from past drying process data, determine which past conditions the current environmental conditions resemble, and propose the optimal drying method based on those conditions. Furthermore, the evaluation unit can adjust the drying conditions according to the type and size of the dried fish. For example, since large dried fish take a long time to dry, the temperature and wind speed can be adjusted to optimize the drying time. This allows the evaluation unit to accurately assess the drying status of the dried fish and provide optimal drying conditions, thereby supporting the production of high-quality dried fish.

[0032] The measurement unit measures the weight change of dried fish in real time. For example, the measurement unit can measure the weight change of dried fish in real time using a weighing scale integrated into a dedicated drying net. Specifically, the measurement unit accurately measures the weight change of dried fish during the drying process and transmits the data to the evaluation unit. The weighing scale is used to analyze the rate and pattern of weight loss of the dried fish and to understand the progress of drying. For example, if the weight of the dried fish decreases rapidly, it may be drying too much, so the drying rate can be controlled by adjusting the temperature and humidity. By monitoring the weight change of dried fish in real time, the measurement unit can accurately understand the progress of the drying process. Furthermore, the measurement unit can measure the weight of multiple dried fish simultaneously, so it can handle drying large quantities of dried fish at once. This allows the measurement unit to monitor the drying status of the dried fish in real time and provide data to maintain optimal drying conditions. In addition, the measurement unit can save the weight data of the dried fish to the cloud and compare it with past data to improve and optimize the drying process. This allows the measurement unit to efficiently and effectively manage the drying process of dried fish, supporting the production of high-quality dried fish.

[0033] The camera unit monitors changes in the shape and color of the dried fish. For example, the camera unit can use image analysis to check changes in the shape and color of the dried fish and also evaluate environmental conditions. Specifically, the camera unit periodically takes images of the dried fish using a high-resolution camera and evaluates the drying status by analyzing these images. By using image analysis technology, it is possible to detect changes in the shape and color of the dried fish and understand the progress of drying. For example, by checking whether the color of the dried fish is changing uniformly and whether the shape is not collapsing, it is possible to determine whether drying is progressing appropriately. Furthermore, the camera unit can also evaluate environmental conditions. For example, by using the camera to check the intensity of light and the presence or absence of shadows in the area where the dried fish is placed, it is possible to evaluate whether the drying environment is appropriate. In this way, the camera unit can visually check the drying status of the dried fish and provide data to maintain optimal drying conditions. In addition, the camera unit can save the captured images to the cloud and compare them with past images to monitor the progress of the drying process over the long term. In this way, the camera unit can accurately grasp the drying status of the dried fish and support the production of high-quality dried fish.

[0034] The voice assistant unit provides advice and progress notifications in a conversational format. For example, the voice assistant unit can notify the user by voice whether the drying of dried fish is progressing and provide necessary advice. Specifically, the voice assistant unit uses AI to converse with the user and provide advice according to the drying status of the dried fish. For example, if drying is progressing well, it can notify the user, "The dried fish is drying smoothly. Please continue to maintain the same environment," and if drying is slow, it can advise, "The humidity is high, so please improve ventilation or raise the temperature." The voice assistant unit can also respond to user questions. For example, if the user asks, "What is the current drying status of the dried fish?", the voice assistant unit can notify the user by voice of the current drying status based on data from the acquisition unit, evaluation unit, measurement unit, and camera unit. This allows the user to understand the drying status of the dried fish in real time and take appropriate action. In addition, the voice assistant unit can collect user feedback and use it to improve the overall system. For example, by recording the responses and results to the advice provided by the user, the AI ​​can learn and provide more accurate advice. This allows the voice assistant unit to support the drying process of dried fish through interaction with the user, enabling the production of high-quality dried fish.

[0035] The acquisition unit can acquire real-time weather and humidity online using location information. For example, the acquisition unit can acquire real-time weather and humidity using a weather data API based on location information. For example, the acquisition unit can acquire the current location's weather and humidity using GPS data. For example, the acquisition unit can acquire the current location's weather and humidity using an IP address. This allows for the acquisition of real-time weather and humidity using location information, enabling appropriate management of the drying environment for dried goods.

[0036] The measuring unit can measure the weight change of dried fish in real time using a weighing scale built into the special drying net. For example, the measuring unit can incorporate an electronic balance into the special drying net to measure the weight change of dried fish in real time. For example, the measuring unit can incorporate a load cell into the special drying net to measure the weight change of dried fish in real time. For example, the measuring unit can incorporate a pressure sensor into the special drying net to measure the weight change of dried fish in real time. This allows for accurate monitoring of the drying progress by measuring the weight change of dried fish in real time.

[0037] The camera unit can check the shape and color changes of dried fish through image analysis and also evaluate environmental conditions. For example, the camera unit can use machine learning algorithms to check the shape and color changes of dried fish. For example, the camera unit can use image processing technology to check the shape and color changes of dried fish. For example, the camera unit can use deep learning to check the shape and color changes of dried fish. This allows for the visual evaluation of the drying status by checking the shape and color changes of dried fish using image analysis.

[0038] The voice assistant unit can provide advice and progress notifications in a conversational format. For example, the voice assistant unit can notify the user of the drying status of dried fish using voice dialogue. For example, the voice assistant unit can notify the user of the drying status of dried fish using text dialogue. For example, the voice assistant unit can provide advice regarding the drying of dried fish using voice dialogue. This makes it easier for the user to understand the drying status of the dried fish by providing advice and progress notifications in a conversational format.

[0039] The data acquisition unit has the function of selecting the optimal environmental data according to the type of dried food. For example, in the case of dried fish, the data acquisition unit can prioritize the acquisition of specific humidity and temperature conditions. For example, in the case of dried meat, the data acquisition unit can prioritize the acquisition of specific wind speed and sunlight conditions. For example, in the case of dried fruit, the data acquisition unit can prioritize the acquisition of specific ultraviolet radiation levels and temperature conditions. By selecting the optimal environmental data according to the type of dried food, the accuracy of dried food production is improved.

[0040] The data acquisition unit has the function of predicting future weather and humidity by referring to past environmental data. For example, the data acquisition unit can predict the weather for the next 24 hours based on past weather data. For example, the data acquisition unit can predict humidity changes for the next 48 hours based on past humidity data. For example, the data acquisition unit can predict temperature changes for the next 72 hours based on past temperature data. This makes it easier to plan the drying process by predicting future weather and humidity by referring to past environmental data.

[0041] The data acquisition unit can prioritize acquiring region-specific environmental data, taking into account the user's geographical location. For example, if the user is in a coastal area, the unit can prioritize acquiring data related to sea breezes and tides. If the user is in a mountainous area, the unit can prioritize acquiring data related to altitude and atmospheric pressure. If the user is in an urban area, the unit can prioritize acquiring data related to urban-specific temperature and humidity. By acquiring region-specific environmental data while considering the user's geographical location, it becomes possible to provide more accurate information.

[0042] The data acquisition unit can analyze a user's social media activity and acquire relevant environmental data. For example, if a user posts about making dried fish on social media, the data acquisition unit can acquire relevant environmental data based on the content of that post. For example, if a user shares information about a specific region on social media, the data acquisition unit can prioritize acquiring environmental data for that region. For example, if a user shares information about specific weather conditions on social media, the data acquisition unit can acquire data related to those weather conditions. By analyzing a user's social media activity and acquiring relevant environmental data, it becomes possible to provide information tailored to the user.

[0043] The evaluation unit has the function of applying different evaluation algorithms depending on the type of dried food. For example, in the case of dried fish, the evaluation unit can apply an algorithm that evaluates based on specific humidity and temperature conditions. For example, in the case of dried meat, the evaluation unit can apply an algorithm that evaluates based on specific wind speed and sunlight conditions. For example, in the case of dried fruit, the evaluation unit can apply an algorithm that evaluates based on specific ultraviolet radiation levels and temperature conditions. This improves the accuracy of the evaluation by applying different evaluation algorithms depending on the type of dried food.

[0044] The evaluation unit has the function of improving the accuracy of evaluations by referring to past evaluation data. For example, the evaluation unit can correct current evaluation results based on past evaluation data. For example, the evaluation unit can analyze past evaluation data and improve the evaluation algorithm. For example, the evaluation unit can improve the reliability of evaluation results by referring to past evaluation data. As a result, by improving the accuracy of evaluations by referring to past evaluation data, more accurate evaluations become possible.

[0045] The evaluation unit has the function of customizing evaluation results based on the user's lifestyle. For example, if the user is busy, the evaluation unit can present a concise and to-the-point evaluation result. For example, if the user is relaxed, the evaluation unit can present a detailed evaluation result. For example, if the user has a specific lifestyle pattern, the evaluation unit can customize the evaluation result based on that pattern. This makes it possible to provide information that is more suitable for the user by customizing the evaluation result based on the user's lifestyle.

[0046] The evaluation unit has the function of personalizing evaluation results by referring to the user's purchase history. For example, the evaluation unit can customize evaluation results based on the types of dried fish that the user has purchased in the past. For example, the evaluation unit can analyze the user's purchase history and personalize evaluation results. For example, if the user has purchased dried fish of a specific brand, the evaluation unit can customize evaluation results based on that brand. This makes it possible to provide information that is more relevant to the user by personalizing evaluation results by referring to the user's purchase history.

[0047] The measuring unit has the function of selecting the optimal measurement method according to the type of dried food. For example, in the case of dried fish, the measuring unit can select a measurement method based on specific humidity and temperature conditions. For example, in the case of dried meat, the measuring unit can select a measurement method based on specific wind speed and sunlight conditions. For example, in the case of dried fruit, the measuring unit can select a measurement method based on specific ultraviolet radiation levels and temperature conditions. By selecting the optimal measurement method according to the type of dried food, the accuracy of the measurement is improved.

[0048] The measurement unit has a function to improve measurement accuracy by referring to past measurement data. For example, the measurement unit can correct the current measurement result based on past measurement data. For example, the measurement unit can analyze past measurement data and improve the measurement algorithm. For example, the measurement unit can improve the reliability of the measurement result by referring to past measurement data. As a result, by improving measurement accuracy by referring to past measurement data, more accurate measurements become possible.

[0049] The measurement unit has the function of applying region-specific measurement methods, taking into account the user's geographical location information. For example, if the user is in a coastal area, the measurement unit can apply a measurement method based on sea breeze and tides. For example, if the user is in a mountainous area, the measurement unit can apply a measurement method based on altitude and atmospheric pressure. For example, if the user is in an urban area, the measurement unit can apply a measurement method based on urban-specific temperature and humidity. This makes it possible to provide more accurate information by applying region-specific measurement methods, taking into account the user's geographical location information.

[0050] The measurement unit has the function of analyzing the user's social media activity and acquiring relevant measurement data. For example, if a user posts about making dried fish on social media, the measurement unit can acquire relevant measurement data based on the content of that post. For example, if a user shares information about a specific region on social media, the measurement unit can prioritize acquiring measurement data for that region. For example, if a user shares information about specific weather conditions on social media, the measurement unit can acquire data related to those weather conditions. By analyzing the user's social media activity and acquiring relevant measurement data, it becomes possible to provide information that is appropriate for the user.

[0051] The camera unit has the function of applying the optimal image analysis algorithm according to the type of dried food. For example, in the case of dried fish, the camera unit can apply an algorithm that performs image analysis based on specific color and shape changes. For example, in the case of dried meat, the camera unit can apply an algorithm that performs image analysis based on specific drying patterns. For example, in the case of dried fruit, the camera unit can apply an algorithm that performs image analysis based on specific color and texture changes. As a result, the accuracy of image analysis is improved by applying the optimal image analysis algorithm according to the type of dried food.

[0052] The camera unit has a function to improve the accuracy of image analysis by referring to past image data. For example, the camera unit can correct the current image analysis result based on past image data. For example, the camera unit can analyze past image data and improve the image analysis algorithm. For example, the camera unit can improve the reliability of the image analysis result by referring to past image data. As a result, by improving the accuracy of image analysis by referring to past image data, more accurate image analysis becomes possible.

[0053] The camera unit has the function of applying region-specific image analysis methods, taking into account the user's geographical location information. For example, if the user is in a coastal area, the camera unit can apply image analysis methods based on sea breezes and tides. For example, if the user is in a mountainous area, the camera unit can apply image analysis methods based on altitude and atmospheric pressure. For example, if the user is in an urban area, the camera unit can apply image analysis methods based on urban-specific temperature and humidity. This allows for the provision of more accurate information by applying region-specific image analysis methods, taking into account the user's geographical location information.

[0054] The camera unit has the function of analyzing the user's social media activity and acquiring relevant image data. For example, if a user posts about making dried fish on social media, the camera unit can acquire relevant image data based on the content of that post. For example, if a user shares information about a specific region on social media, the camera unit can prioritize acquiring image data of that region. For example, if a user shares information about specific weather conditions on social media, the camera unit can acquire image data related to those weather conditions. This makes it possible to provide information tailored to the user by analyzing the user's social media activity and acquiring relevant image data.

[0055] The voice assistant unit has the function of providing different advice depending on the type of dried food. For example, in the case of dried fish, the voice assistant unit can provide advice based on specific humidity and temperature conditions. For example, in the case of dried meat, the voice assistant unit can provide advice based on specific wind speed and sunlight conditions. For example, in the case of dried fruit, the voice assistant unit can provide advice based on specific UV radiation levels and temperature conditions. This improves the accuracy of the advice by providing different advice depending on the type of dried food.

[0056] The voice assistant unit has a function to improve the accuracy of advice by referring to past advice data. For example, the voice assistant unit can correct the current advice content based on past advice data. For example, the voice assistant unit can analyze past advice data and improve the advice algorithm. For example, the voice assistant unit can improve the reliability of the advice content by referring to past advice data. As a result, by improving the accuracy of advice by referring to past advice data, more accurate advice becomes possible.

[0057] The voice assistant has the ability to customize advice based on the user's lifestyle. For example, if the user is busy, the voice assistant can provide concise and to-the-point advice. For example, if the user is relaxed, the voice assistant can provide detailed advice. For example, if the user has a specific lifestyle pattern, the voice assistant can customize advice based on that pattern. This allows for the provision of information tailored to the user by customizing advice based on their lifestyle.

[0058] The voice assistant unit has the function of personalizing advice by referring to the user's purchase history. For example, the voice assistant unit can customize advice based on the types of dried fish the user has purchased in the past. For example, the voice assistant unit can analyze the user's purchase history and personalize advice. For example, if the user has purchased dried fish of a specific brand, the voice assistant unit can customize advice based on that brand. This makes it possible to provide information that is more relevant to the user by referencing their purchase history and personalizing advice.

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

[0060] The data acquisition unit has the function of selecting the optimal environmental data according to the type of dried food. For example, in the case of dried fish, it can prioritize the acquisition of specific humidity and temperature conditions. In the case of dried meat, it can prioritize the acquisition of specific wind speed and sunlight conditions. Furthermore, in the case of dried fruit, it can prioritize the acquisition of specific ultraviolet radiation levels and temperature conditions. By selecting the optimal environmental data according to the type of dried food, the accuracy of dried food production is improved.

[0061] The data acquisition unit has the function of predicting future weather and humidity by referring to past environmental data. For example, it can predict the weather for the next 24 hours based on past weather data. It can also predict humidity changes for the next 48 hours based on past humidity data. Furthermore, it can predict temperature changes for the next 72 hours based on past temperature data. This makes it easier to plan the drying process by predicting future weather and humidity by referring to past environmental data.

[0062] The evaluation unit has the function of applying different evaluation algorithms depending on the type of dried food. For example, in the case of dried fish, an algorithm can be applied that evaluates based on specific humidity and temperature conditions. In the case of dried meat, an algorithm can be applied that evaluates based on specific wind speed and sunlight conditions. Furthermore, in the case of dried fruit, an algorithm can be applied that evaluates based on specific ultraviolet radiation levels and temperature conditions. By applying different evaluation algorithms depending on the type of dried food, the accuracy of the evaluation is improved.

[0063] The measuring unit has a function to select the optimal measurement method according to the type of dried food. For example, in the case of dried fish, it can select a measurement method based on specific humidity and temperature conditions. In the case of dried meat, it can select a measurement method based on specific wind speed and sunlight conditions. Furthermore, in the case of dried fruit, it can select a measurement method based on specific ultraviolet radiation levels and temperature conditions. By selecting the optimal measurement method according to the type of dried food, the accuracy of the measurement is improved.

[0064] The camera unit has the function of applying the optimal image analysis algorithm according to the type of dried food. For example, in the case of dried fish, an algorithm that performs image analysis based on specific color and shape changes can be applied. In the case of dried meat, an algorithm that performs image analysis based on specific drying patterns can be applied. Furthermore, in the case of dried fruit, an algorithm that performs image analysis based on specific color and texture changes can be applied. As a result, the accuracy of image analysis is improved by applying the optimal image analysis algorithm according to the type of dried food.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The acquisition unit acquires environmental data from the internet. For example, it can use location information to acquire real-time weather and humidity online. Step 2: The evaluation unit evaluates the drying status of the dried fish based on the environmental data acquired by the acquisition unit. For example, the drying status of the dried fish can be evaluated based on environmental data such as temperature, humidity, and wind speed. Step 3: The measuring unit measures the weight change of the dried fish in real time. For example, a weighing scale built into the special drying net can be used to measure the weight change of the dried fish in real time. Step 4: The camera unit checks the changes in shape and color of the dried fish. For example, by analyzing the image, it is possible to check the changes in shape and color of the dried fish and also evaluate the environmental conditions. Step 5: The voice assistant unit provides advice and progress notifications in a conversational format. For example, it can notify users via voice whether the drying process is progressing well and provide necessary advice.

[0067] (Example of form 2) The dried fish drying management system according to an embodiment of the present invention is a system for appropriately managing the drying environment of dried fish and producing high-quality dried fish. This dried fish drying management system acquires environmental data from the internet, evaluates the drying status of the dried fish, measures the weight change of the dried fish in real time, checks the changes in shape and color of the dried fish, and provides interactive advice and progress notifications. For example, the dried fish drying management system can acquire real-time weather and humidity online using location information. For example, it can acquire weather forecasts and current humidity information for the place where the dried fish is to be dried and determine whether the environment is suitable for drying the dried fish. Next, the dried fish drying management system can measure the weight change of the dried fish in real time using a weighing scale incorporated into a dedicated drying net. For example, it can check whether the weight of the dried fish is decreasing and determine whether drying is progressing. Furthermore, the dried fish drying management system can use a dedicated camera for visual change analysis to check the changes in shape and color of the dried fish and evaluate the environmental conditions. For example, it can check whether the color of the dried fish is changing and determine whether drying is progressing. Finally, the dried fish drying management system can use a voice assistant to provide interactive advice and progress notifications. For example, it can provide voice notifications about the progress of the drying process and offer necessary advice. This system allows for proper management of the drying process, supporting the creation of high-quality dried fish in the optimal environment and time. Even DIY users can produce dried fish at a professional level. For instance, it is designed to be easy for beginners to use, and quality control is simple, allowing even beginners to easily make delicious dried fish. Furthermore, products for cooking enthusiasts come in a variety of sizes and shapes, possessing not only the necessary functions for dried fish production but also user-friendly and beautiful designs. In addition, products for those who want to make professional-level dried fish at home are equipped with advanced functions, allowing for free adjustment of temperature, humidity, and airflow, enabling fully automatic dried fish production. In summary, the dried fish drying management system can properly manage the drying process, supporting the creation of high-quality dried fish in the optimal environment and time.

[0068] The dried fish drying management system according to this embodiment comprises an acquisition unit, an evaluation unit, a measurement unit, a camera unit, and a voice assistant unit. The acquisition unit acquires environmental data from the internet. The acquisition unit can, for example, acquire real-time weather and humidity online using location information. The evaluation unit evaluates the drying status of the dried fish based on the environmental data acquired by the acquisition unit. The evaluation unit can, for example, evaluate the drying status of the dried fish based on environmental data such as temperature, humidity, and wind speed. The measurement unit measures the weight change of the dried fish in real time. The measurement unit can, for example, measure the weight change of the dried fish in real time using a weighing scale incorporated into a dedicated drying net. The camera unit checks the shape and color changes of the dried fish. The camera unit can, for example, check the shape and color changes of the dried fish using image analysis and also evaluate environmental conditions. The voice assistant unit provides advice and progress notifications in a conversational format. The voice assistant unit can, for example, notify the user by voice whether the drying of the dried fish is progressing and provide necessary advice. As a result, the dried fish drying management system according to this embodiment can appropriately manage the drying status of the dried fish and support the production of high-quality dried fish in the optimal environment and time.

[0069] The data acquisition unit obtains environmental data from the internet. For example, it can use location information to obtain real-time weather and humidity online. Specifically, the unit uses GPS functionality to determine its current location and accesses a weather database based on that location information. From the weather database, it can obtain detailed environmental data such as current weather, temperature, humidity, wind speed, and precipitation. This data is extremely important in the dried fish drying process and is essential for maintaining an appropriate drying environment. Furthermore, the data acquisition unit can periodically update environmental data and respond to real-time changing weather conditions. For example, if the weather suddenly changes or the humidity rises, the data acquisition unit can immediately acquire new data and reflect it throughout the entire system. This ensures that the drying conditions for dried fish are always kept in an optimal state. In addition, the data acquisition unit can collect information from multiple weather data sources via the internet, improving the reliability and accuracy of the data. For example, by integrating data not only from the Japan Meteorological Agency but also from private weather services and local weather stations, it can obtain more accurate environmental data. As a result, the data acquisition unit can provide optimal environmental conditions in the dried fish drying process and support the production of high-quality dried fish.

[0070] The evaluation unit assesses the drying status of dried fish based on environmental data acquired by the acquisition unit. The evaluation unit can assess the drying status of dried fish based on environmental data such as temperature, humidity, and wind speed. Specifically, the evaluation unit analyzes the acquired environmental data and determines the optimal conditions for drying the dried fish. For example, if the temperature is too high, there is a risk of over-drying the dried fish, and if the humidity is too high, there is a risk of mold growth. Based on this data, the evaluation unit makes adjustments to maintain appropriate drying conditions. The evaluation unit uses AI to analyze environmental data and monitors the drying process of the dried fish in real time. Based on past data and statistical information, the AI ​​predicts how current environmental conditions will affect the drying of the dried fish and provides optimal drying conditions. For example, the AI ​​can learn from past drying process data, determine which past conditions the current environmental conditions resemble, and propose the optimal drying method based on those conditions. Furthermore, the evaluation unit can adjust the drying conditions according to the type and size of the dried fish. For example, since large dried fish take a long time to dry, the temperature and wind speed can be adjusted to optimize the drying time. This allows the evaluation unit to accurately assess the drying status of the dried fish and provide optimal drying conditions, thereby supporting the production of high-quality dried fish.

[0071] The measurement unit measures the weight change of dried fish in real time. For example, the measurement unit can measure the weight change of dried fish in real time using a weighing scale integrated into a dedicated drying net. Specifically, the measurement unit accurately measures the weight change of dried fish during the drying process and transmits the data to the evaluation unit. The weighing scale is used to analyze the rate and pattern of weight loss of the dried fish and to understand the progress of drying. For example, if the weight of the dried fish decreases rapidly, it may be drying too much, so the drying rate can be controlled by adjusting the temperature and humidity. By monitoring the weight change of dried fish in real time, the measurement unit can accurately understand the progress of the drying process. Furthermore, the measurement unit can measure the weight of multiple dried fish simultaneously, so it can handle drying large quantities of dried fish at once. This allows the measurement unit to monitor the drying status of the dried fish in real time and provide data to maintain optimal drying conditions. In addition, the measurement unit can save the weight data of the dried fish to the cloud and compare it with past data to improve and optimize the drying process. This allows the measurement unit to efficiently and effectively manage the drying process of dried fish, supporting the production of high-quality dried fish.

[0072] The camera unit monitors changes in the shape and color of the dried fish. For example, the camera unit can use image analysis to check changes in the shape and color of the dried fish and also evaluate environmental conditions. Specifically, the camera unit periodically takes images of the dried fish using a high-resolution camera and evaluates the drying status by analyzing these images. By using image analysis technology, it is possible to detect changes in the shape and color of the dried fish and understand the progress of drying. For example, by checking whether the color of the dried fish is changing uniformly and whether the shape is not collapsing, it is possible to determine whether drying is progressing appropriately. Furthermore, the camera unit can also evaluate environmental conditions. For example, by using the camera to check the intensity of light and the presence or absence of shadows in the area where the dried fish is placed, it is possible to evaluate whether the drying environment is appropriate. In this way, the camera unit can visually check the drying status of the dried fish and provide data to maintain optimal drying conditions. In addition, the camera unit can save the captured images to the cloud and compare them with past images to monitor the progress of the drying process over the long term. In this way, the camera unit can accurately grasp the drying status of the dried fish and support the production of high-quality dried fish.

[0073] The voice assistant unit provides advice and progress notifications in a conversational format. For example, the voice assistant unit can notify the user by voice whether the drying of dried fish is progressing and provide necessary advice. Specifically, the voice assistant unit uses AI to interact with the user and provide advice according to the drying status of the dried fish. For example, if drying is progressing well, it can notify the user, "The dried fish is drying smoothly. Please continue to maintain the same environment," and if drying is slow, it can advise, "The humidity is high, so please improve ventilation or raise the temperature." The voice assistant unit can also respond to user questions. For example, if the user asks, "What is the current drying status of the dried fish?", the voice assistant unit can notify the user by voice of the current drying status based on data from the acquisition unit, evaluation unit, measurement unit, and camera unit. This allows the user to understand the drying status of the dried fish in real time and take appropriate action. In addition, the voice assistant unit can collect user feedback and use it to improve the overall system. For example, by recording the responses and results to the advice provided by the user, the AI ​​can learn and provide more accurate advice. This allows the voice assistant unit to support the drying process of dried fish through interaction with the user, enabling the production of high-quality dried fish.

[0074] The acquisition unit can acquire real-time weather and humidity online using location information. For example, the acquisition unit can acquire real-time weather and humidity using a weather data API based on location information. For example, the acquisition unit can acquire the current location's weather and humidity using GPS data. For example, the acquisition unit can acquire the current location's weather and humidity using an IP address. This allows for the acquisition of real-time weather and humidity using location information, enabling appropriate management of the drying environment for dried goods.

[0075] The measuring unit can measure the weight change of dried fish in real time using a weighing scale built into the special drying net. For example, the measuring unit can incorporate an electronic balance into the special drying net to measure the weight change of dried fish in real time. For example, the measuring unit can incorporate a load cell into the special drying net to measure the weight change of dried fish in real time. For example, the measuring unit can incorporate a pressure sensor into the special drying net to measure the weight change of dried fish in real time. This allows for accurate monitoring of the drying progress by measuring the weight change of dried fish in real time.

[0076] The camera unit can check the shape and color changes of dried fish through image analysis and also evaluate environmental conditions. For example, the camera unit can use machine learning algorithms to check the shape and color changes of dried fish. For example, the camera unit can use image processing technology to check the shape and color changes of dried fish. For example, the camera unit can use deep learning to check the shape and color changes of dried fish. This allows for the visual evaluation of the drying status by checking the shape and color changes of dried fish using image analysis.

[0077] The voice assistant unit can provide advice and progress notifications in a conversational format. For example, the voice assistant unit can notify the user of the drying status of dried fish using voice dialogue. For example, the voice assistant unit can notify the user of the drying status of dried fish using text dialogue. For example, the voice assistant unit can provide advice regarding the drying of dried fish using voice dialogue. This makes it easier for the user to understand the drying status of the dried fish by providing advice and progress notifications in a conversational format.

[0078] The data acquisition unit can estimate the user's emotions and adjust the frequency of environmental data acquisition based on the estimated user emotions. The data acquisition unit can estimate the user's emotions using, for example, facial recognition. The data acquisition unit can estimate the user's emotions using, for example, speech analysis. The data acquisition unit can estimate the user's emotions using, for example, text analysis. This makes it possible to provide information that is appropriate for the user by adjusting the frequency of environmental data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The data acquisition unit has the function of selecting the optimal environmental data according to the type of dried food. For example, in the case of dried fish, the data acquisition unit can prioritize the acquisition of specific humidity and temperature conditions. For example, in the case of dried meat, the data acquisition unit can prioritize the acquisition of specific wind speed and sunlight conditions. For example, in the case of dried fruit, the data acquisition unit can prioritize the acquisition of specific ultraviolet radiation levels and temperature conditions. By selecting the optimal environmental data according to the type of dried food, the accuracy of dried food production is improved.

[0080] The data acquisition unit has the function of predicting future weather and humidity by referring to past environmental data. For example, the data acquisition unit can predict the weather for the next 24 hours based on past weather data. For example, the data acquisition unit can predict humidity changes for the next 48 hours based on past humidity data. For example, the data acquisition unit can predict temperature changes for the next 72 hours based on past temperature data. This makes it easier to plan the drying process by predicting future weather and humidity by referring to past environmental data.

[0081] The data acquisition unit can estimate the user's emotions and determine the priority of environmental data to acquire based on the estimated user emotions. The data acquisition unit can estimate the user's emotions using, for example, facial recognition. The data acquisition unit can estimate the user's emotions using, for example, speech analysis. The data acquisition unit can estimate the user's emotions using, for example, text analysis. This makes it possible to provide information that is appropriate for the user by determining the priority of environmental data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The data acquisition unit can prioritize acquiring region-specific environmental data, taking into account the user's geographical location. For example, if the user is in a coastal area, the unit can prioritize acquiring data related to sea breezes and tides. If the user is in a mountainous area, the unit can prioritize acquiring data related to altitude and atmospheric pressure. If the user is in an urban area, the unit can prioritize acquiring data related to urban-specific temperature and humidity. By acquiring region-specific environmental data while considering the user's geographical location, it becomes possible to provide more accurate information.

[0083] The data acquisition unit can analyze a user's social media activity and acquire relevant environmental data. For example, if a user posts about making dried fish on social media, the data acquisition unit can acquire relevant environmental data based on the content of that post. For example, if a user shares information about a specific region on social media, the data acquisition unit can prioritize acquiring environmental data for that region. For example, if a user shares information about specific weather conditions on social media, the data acquisition unit can acquire data related to those weather conditions. By analyzing a user's social media activity and acquiring relevant environmental data, it becomes possible to provide information tailored to the user.

[0084] The evaluation unit can estimate the user's emotions and adjust the presentation method of the evaluation results based on the estimated user emotions. For example, the evaluation unit can estimate the user's emotions using facial recognition. For example, the evaluation unit can estimate the user's emotions using speech analysis. For example, the evaluation unit can estimate the user's emotions using text analysis. This makes it possible to provide information that is appropriate for the user by adjusting the presentation method of the evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The evaluation unit has the function of applying different evaluation algorithms depending on the type of dried food. For example, in the case of dried fish, the evaluation unit can apply an algorithm that evaluates based on specific humidity and temperature conditions. For example, in the case of dried meat, the evaluation unit can apply an algorithm that evaluates based on specific wind speed and sunlight conditions. For example, in the case of dried fruit, the evaluation unit can apply an algorithm that evaluates based on specific ultraviolet radiation levels and temperature conditions. This improves the accuracy of the evaluation by applying different evaluation algorithms depending on the type of dried food.

[0086] The evaluation unit has the function of improving the accuracy of evaluations by referring to past evaluation data. For example, the evaluation unit can correct current evaluation results based on past evaluation data. For example, the evaluation unit can analyze past evaluation data and improve the evaluation algorithm. For example, the evaluation unit can improve the reliability of evaluation results by referring to past evaluation data. As a result, by improving the accuracy of evaluations by referring to past evaluation data, more accurate evaluations become possible.

[0087] The evaluation unit can estimate the user's emotions and adjust the level of detail in the evaluation results based on the estimated user emotions. For example, the evaluation unit can estimate the user's emotions using facial recognition. For example, the evaluation unit can estimate the user's emotions using speech analysis. For example, the evaluation unit can estimate the user's emotions using text analysis. This makes it possible to provide information that is appropriate for the user by adjusting the level of detail in the evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] The evaluation unit has the function of customizing evaluation results based on the user's lifestyle. For example, if the user is busy, the evaluation unit can present a concise and to-the-point evaluation result. For example, if the user is relaxed, the evaluation unit can present a detailed evaluation result. For example, if the user has a specific lifestyle pattern, the evaluation unit can customize the evaluation result based on that pattern. This makes it possible to provide information that is more suitable for the user by customizing the evaluation result based on the user's lifestyle.

[0089] The evaluation unit has the function of personalizing evaluation results by referring to the user's purchase history. For example, the evaluation unit can customize evaluation results based on the types of dried fish that the user has purchased in the past. For example, the evaluation unit can analyze the user's purchase history and personalize evaluation results. For example, if the user has purchased dried fish of a specific brand, the evaluation unit can customize evaluation results based on that brand. This makes it possible to provide information that is more relevant to the user by personalizing evaluation results by referring to the user's purchase history.

[0090] The measurement unit can estimate the user's emotions and adjust the measurement frequency based on the estimated emotions. For example, the measurement unit can estimate the user's emotions using facial recognition. For example, the measurement unit can estimate the user's emotions using voice analysis. For example, the measurement unit can estimate the user's emotions using text analysis. This allows for the provision of information tailored to the user by adjusting the measurement frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The measuring unit has the function of selecting the optimal measurement method according to the type of dried food. For example, in the case of dried fish, the measuring unit can select a measurement method based on specific humidity and temperature conditions. For example, in the case of dried meat, the measuring unit can select a measurement method based on specific wind speed and sunlight conditions. For example, in the case of dried fruit, the measuring unit can select a measurement method based on specific ultraviolet radiation levels and temperature conditions. By selecting the optimal measurement method according to the type of dried food, the accuracy of the measurement is improved.

[0092] The measurement unit has a function to improve measurement accuracy by referring to past measurement data. For example, the measurement unit can correct the current measurement result based on past measurement data. For example, the measurement unit can analyze past measurement data and improve the measurement algorithm. For example, the measurement unit can improve the reliability of the measurement result by referring to past measurement data. As a result, by improving measurement accuracy by referring to past measurement data, more accurate measurements become possible.

[0093] The measurement unit can estimate the user's emotions and adjust the display method of the measurement results based on the estimated user emotions. For example, the measurement unit can estimate the user's emotions using facial recognition. For example, the measurement unit can estimate the user's emotions using voice analysis. For example, the measurement unit can estimate the user's emotions using text analysis. This makes it possible to provide information that is appropriate for the user by adjusting the display method of the measurement results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The measurement unit has the function of applying region-specific measurement methods, taking into account the user's geographical location information. For example, if the user is in a coastal area, the measurement unit can apply a measurement method based on sea breeze and tides. For example, if the user is in a mountainous area, the measurement unit can apply a measurement method based on altitude and atmospheric pressure. For example, if the user is in an urban area, the measurement unit can apply a measurement method based on urban-specific temperature and humidity. This makes it possible to provide more accurate information by applying region-specific measurement methods, taking into account the user's geographical location information.

[0095] The measurement unit has the function of analyzing the user's social media activity and acquiring relevant measurement data. For example, if a user posts about making dried fish on social media, the measurement unit can acquire relevant measurement data based on the content of that post. For example, if a user shares information about a specific region on social media, the measurement unit can prioritize acquiring measurement data for that region. For example, if a user shares information about specific weather conditions on social media, the measurement unit can acquire data related to those weather conditions. By analyzing the user's social media activity and acquiring relevant measurement data, it becomes possible to provide information that is appropriate for the user.

[0096] The camera unit can estimate the user's emotions and adjust the frequency of image analysis based on the estimated emotions. For example, the camera unit can estimate the user's emotions using facial recognition. For example, the camera unit can estimate the user's emotions using voice analysis. For example, the camera unit can estimate the user's emotions using text analysis. This allows for the provision of information tailored to the user by adjusting the frequency of image analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The camera unit has the function of applying the optimal image analysis algorithm according to the type of dried food. For example, in the case of dried fish, the camera unit can apply an algorithm that performs image analysis based on specific color and shape changes. For example, in the case of dried meat, the camera unit can apply an algorithm that performs image analysis based on specific drying patterns. For example, in the case of dried fruit, the camera unit can apply an algorithm that performs image analysis based on specific color and texture changes. As a result, the accuracy of image analysis is improved by applying the optimal image analysis algorithm according to the type of dried food.

[0098] The camera unit has a function to improve the accuracy of image analysis by referring to past image data. For example, the camera unit can correct the current image analysis result based on past image data. For example, the camera unit can analyze past image data and improve the image analysis algorithm. For example, the camera unit can improve the reliability of the image analysis result by referring to past image data. As a result, by improving the accuracy of image analysis by referring to past image data, more accurate image analysis becomes possible.

[0099] The camera unit can estimate the user's emotions and adjust the display method of the image analysis results based on the estimated user emotions. For example, the camera unit can estimate the user's emotions using facial recognition. For example, the camera unit can estimate the user's emotions using voice analysis. For example, the camera unit can estimate the user's emotions using text analysis. This makes it possible to provide information that is appropriate for the user by adjusting the display method of the image analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The camera unit has the function of applying region-specific image analysis methods, taking into account the user's geographical location information. For example, if the user is in a coastal area, the camera unit can apply image analysis methods based on sea breezes and tides. For example, if the user is in a mountainous area, the camera unit can apply image analysis methods based on altitude and atmospheric pressure. For example, if the user is in an urban area, the camera unit can apply image analysis methods based on urban-specific temperature and humidity. This allows for the provision of more accurate information by applying region-specific image analysis methods, taking into account the user's geographical location information.

[0101] The camera unit has the function of analyzing the user's social media activity and acquiring relevant image data. For example, if a user posts about making dried fish on social media, the camera unit can acquire relevant image data based on the content of that post. For example, if a user shares information about a specific region on social media, the camera unit can prioritize acquiring image data of that region. For example, if a user shares information about specific weather conditions on social media, the camera unit can acquire image data related to those weather conditions. This makes it possible to provide information tailored to the user by analyzing the user's social media activity and acquiring relevant image data.

[0102] The voice assistant unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, the voice assistant unit can estimate the user's emotions using facial recognition. For example, the voice assistant unit can estimate the user's emotions using voice analysis. For example, the voice assistant unit can estimate the user's emotions using text analysis. This allows for the provision of information tailored to the user by adjusting the content of the advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The voice assistant unit has the function of providing different advice depending on the type of dried food. For example, in the case of dried fish, the voice assistant unit can provide advice based on specific humidity and temperature conditions. For example, in the case of dried meat, the voice assistant unit can provide advice based on specific wind speed and sunlight conditions. For example, in the case of dried fruit, the voice assistant unit can provide advice based on specific UV radiation levels and temperature conditions. This improves the accuracy of the advice by providing different advice depending on the type of dried food.

[0104] The voice assistant unit has a function to improve the accuracy of advice by referring to past advice data. For example, the voice assistant unit can correct the current advice content based on past advice data. For example, the voice assistant unit can analyze past advice data and improve the advice algorithm. For example, the voice assistant unit can improve the reliability of the advice content by referring to past advice data. As a result, by improving the accuracy of advice by referring to past advice data, more accurate advice becomes possible.

[0105] The voice assistant unit can estimate the user's emotions and adjust the timing of advice based on the estimated emotions. For example, the voice assistant unit can estimate the user's emotions using facial recognition. For example, the voice assistant unit can estimate the user's emotions using voice analysis. For example, the voice assistant unit can estimate the user's emotions using text analysis. This allows for the provision of information tailored to the user by adjusting the timing of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The voice assistant has the ability to customize advice based on the user's lifestyle. For example, if the user is busy, the voice assistant can provide concise and to-the-point advice. For example, if the user is relaxed, the voice assistant can provide detailed advice. For example, if the user has a specific lifestyle pattern, the voice assistant can customize advice based on that pattern. This allows for the provision of information tailored to the user by customizing advice based on their lifestyle.

[0107] The voice assistant unit has the function of personalizing advice by referring to the user's purchase history. For example, the voice assistant unit can customize advice based on the types of dried fish the user has purchased in the past. For example, the voice assistant unit can analyze the user's purchase history and personalize advice. For example, if the user has purchased dried fish of a specific brand, the voice assistant unit can customize advice based on that brand. This makes it possible to provide information that is more relevant to the user by referencing their purchase history and personalizing advice.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The data acquisition unit can estimate the user's emotions and adjust the frequency of environmental data acquisition based on the estimated emotions. For example, if the user is anxious, the unit increases the frequency of environmental data acquisition to enable a quick response. Conversely, if the user is relaxed, the unit decreases the acquisition frequency and provides only the necessary information. Furthermore, if the user is feeling uneasy, the unit can acquire detailed environmental data frequently to provide information that provides a sense of security.

[0110] The evaluation unit can estimate the user's emotions and adjust how the evaluation results are presented based on those estimated emotions. For example, if the user is happy, the evaluation unit will emphasize positive evaluation results to increase user satisfaction. If the user is sad, the evaluation unit will add detailed explanations to deepen understanding. Furthermore, if the user is angry, the evaluation unit can present quick and concise evaluation results to prioritize problem resolution.

[0111] The measurement unit can estimate the user's emotions and adjust the measurement frequency based on the estimated emotions. For example, if the user is anxious, the measurement unit increases the measurement frequency to enable a quick response. Conversely, if the user is relaxed, the measurement unit decreases the measurement frequency and provides only the necessary information. Furthermore, if the user is feeling anxious, the measurement unit can frequently acquire detailed measurement data to provide reassuring information.

[0112] The camera unit can estimate the user's emotions and adjust the frequency of image analysis based on the estimated emotions. For example, if the user is anxious, the camera unit increases the frequency of image analysis to enable a quick response. Conversely, if the user is relaxed, the camera unit decreases the frequency of image analysis, providing only the necessary information. Furthermore, if the user is feeling anxious, the camera unit can perform detailed image analysis frequently to provide information that provides reassurance.

[0113] The voice assistant can estimate the user's emotions and adjust the content of its advice based on those emotions. For example, if the user is anxious, the voice assistant will provide quick and concise advice, prioritizing problem-solving. If the user is relaxed, the voice assistant will provide detailed advice, deepening the user's understanding. Furthermore, if the user is feeling anxious, the voice assistant can provide reassuring advice.

[0114] The data acquisition unit has the function of selecting the optimal environmental data according to the type of dried food. For example, in the case of dried fish, it can prioritize the acquisition of specific humidity and temperature conditions. In the case of dried meat, it can prioritize the acquisition of specific wind speed and sunlight conditions. Furthermore, in the case of dried fruit, it can prioritize the acquisition of specific ultraviolet radiation levels and temperature conditions. By selecting the optimal environmental data according to the type of dried food, the accuracy of dried food production is improved.

[0115] The data acquisition unit has the function of predicting future weather and humidity by referring to past environmental data. For example, it can predict the weather for the next 24 hours based on past weather data. It can also predict humidity changes for the next 48 hours based on past humidity data. Furthermore, it can predict temperature changes for the next 72 hours based on past temperature data. This makes it easier to plan the drying process by predicting future weather and humidity by referring to past environmental data.

[0116] The evaluation unit has the function of applying different evaluation algorithms depending on the type of dried food. For example, in the case of dried fish, an algorithm can be applied that evaluates based on specific humidity and temperature conditions. In the case of dried meat, an algorithm can be applied that evaluates based on specific wind speed and sunlight conditions. Furthermore, in the case of dried fruit, an algorithm can be applied that evaluates based on specific ultraviolet radiation levels and temperature conditions. By applying different evaluation algorithms depending on the type of dried food, the accuracy of the evaluation is improved.

[0117] The measuring unit has a function to select the optimal measurement method according to the type of dried food. For example, in the case of dried fish, it can select a measurement method based on specific humidity and temperature conditions. In the case of dried meat, it can select a measurement method based on specific wind speed and sunlight conditions. Furthermore, in the case of dried fruit, it can select a measurement method based on specific ultraviolet radiation levels and temperature conditions. By selecting the optimal measurement method according to the type of dried food, the accuracy of the measurement is improved.

[0118] The camera unit has the function of applying the optimal image analysis algorithm according to the type of dried food. For example, in the case of dried fish, an algorithm that performs image analysis based on specific color and shape changes can be applied. In the case of dried meat, an algorithm that performs image analysis based on specific drying patterns can be applied. Furthermore, in the case of dried fruit, an algorithm that performs image analysis based on specific color and texture changes can be applied. As a result, the accuracy of image analysis is improved by applying the optimal image analysis algorithm according to the type of dried food.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The acquisition unit acquires environmental data from the internet. For example, it can use location information to acquire real-time weather and humidity online. Step 2: The evaluation unit evaluates the drying status of the dried fish based on the environmental data acquired by the acquisition unit. For example, the drying status of the dried fish can be evaluated based on environmental data such as temperature, humidity, and wind speed. Step 3: The measuring unit measures the weight change of the dried fish in real time. For example, a weighing scale built into the special drying net can be used to measure the weight change of the dried fish in real time. Step 4: The camera unit checks the changes in shape and color of the dried fish. For example, by analyzing the image, it is possible to check the changes in shape and color of the dried fish and also evaluate the environmental conditions. Step 5: The voice assistant unit provides advice and progress notifications in a conversational format. For example, it can notify users via voice whether the drying process is progressing well and provide necessary advice.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the acquisition unit, evaluation unit, measurement unit, camera unit, and voice assistant unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire environmental data from the internet via the communication I / F 44 of the smart device 14. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the drying status of the dried fish based on the data from the acquisition unit. The measurement unit measures the weight change of the dried fish in real time using a weighing scale built into the dedicated drying net of the smart device 14. The camera unit checks the shape and color changes of the dried fish using the camera 42 of the smart device 14. The voice assistant unit is implemented in the control unit 46A of the smart device 14 and provides conversational advice and progress notifications. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the acquisition unit, evaluation unit, measurement unit, camera unit, and voice assistant unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire environmental data from the internet via the communication I / F 44 of the smart glasses 214. The evaluation unit is implemented in the identification processing unit 290 of the data processing unit 12 and evaluates the drying status of the dried fish based on the data from the acquisition unit. The measurement unit measures the weight change of the dried fish in real time using a weighing scale built into the dedicated drying net of the smart glasses 214. The camera unit checks the shape and color changes of the dried fish using the camera 42 of the smart glasses 214. The voice assistant unit is implemented in the control unit 46A of the smart glasses 214 and provides conversational advice and progress notifications. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the acquisition unit, evaluation unit, measurement unit, camera unit, and voice assistant unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire environmental data from the internet via the communication I / F 44 of the headset terminal 314. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the drying status of the dried fish based on the data from the acquisition unit. The measurement unit measures the weight change of the dried fish in real time using a weighing scale built into the dedicated drying net of the headset terminal 314. The camera unit checks the shape and color changes of the dried fish using the camera 42 of the headset terminal 314. The voice assistant unit is implemented in the control unit 46A of the headset terminal 314 and provides conversational advice and progress notifications. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the acquisition unit, evaluation unit, measurement unit, camera unit, and voice assistant unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire environmental data from the internet via the robot 414's communication I / F 44. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and evaluates the drying status of the dried fish based on the data from the acquisition unit. The measurement unit measures the weight change of the dried fish in real time using, for example, a weighing scale built into the robot 414's dedicated drying net. The camera unit checks the shape and color changes of the dried fish using, for example, the robot 414's camera 42. The voice assistant unit is implemented, for example, by the control unit 46A of the robot 414, and provides conversational advice and progress notifications. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) An acquisition unit that obtains environmental data from the internet, An evaluation unit that evaluates the drying status of dried fish based on environmental data acquired by the acquisition unit, A measuring unit that measures the weight change of dried fish in real time, A camera unit that checks the shape and color changes of dried fish, It includes a voice assistant unit that provides advice and progress notifications in a conversational format. A system characterized by the following features. (Note 2) The acquisition unit is, Use location information to obtain real-time weather and humidity online. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned measuring unit is A weighing scale built into the special drying net measures the weight change of the dried fish in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned camera unit is Image analysis is used to check the shape and color changes of dried fish, and environmental conditions are also evaluated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned voice assistant unit is Provides advice and progress updates in a conversational format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of environmental data acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It has a function to select the optimal environmental data according to the type of dried fish. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, It has a function that predicts future weather and humidity by referring to past environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of environmental data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, Prioritize the acquisition of region-specific environmental data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, Analyze users' social media activity and obtain relevant environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit described above, The system estimates the user's emotions and adjusts the presentation method of evaluation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit described above, It has a function that applies different evaluation algorithms depending on the type of dried fish. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit described above, It has a function to improve the accuracy of evaluations by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit described above, It estimates the user's emotions and adjusts the level of detail in the evaluation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit described above, It has a feature that customizes evaluation results based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit described above, It has a feature that personalizes evaluation results by referring to the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the measurement frequency based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned measuring unit is It has a function to select the optimal measurement method depending on the type of dried fish. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned measuring unit is It has a function to improve measurement accuracy by referring to past measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned measuring unit is It estimates the user's emotions and adjusts how the measurement results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned measuring unit is It has a function that takes the user's geographical location into account and applies region-specific measurement methods. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned measuring unit is It has the ability to analyze users' social media activity and obtain relevant measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned camera unit is It estimates the user's emotions and adjusts the frequency of image analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned camera unit is It has a function to apply the optimal image analysis algorithm depending on the type of dried fish. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned camera unit is It has a function to improve the accuracy of image analysis by referring to past image data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned camera unit is It estimates the user's emotions and adjusts how the image analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned camera unit is It has a function that applies region-specific image analysis methods, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned camera unit is It has the ability to analyze users' social media activity and retrieve relevant image data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned voice assistant unit is It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned voice assistant unit is It has a function that provides different advice depending on the type of dried fish. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned voice assistant unit is It has a function to improve the accuracy of advice by referring to past advice data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned voice assistant unit is It estimates the user's emotions and adjusts the timing of advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned voice assistant unit is It has a feature that customizes advice based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned voice assistant unit is It has a feature that personalizes advice by referring to the user's purchase history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that obtains environmental data from the internet, An evaluation unit that evaluates the drying status of dried fish based on environmental data acquired by the acquisition unit, A measuring unit that measures the weight change of dried fish in real time, A camera unit that checks the shape and color changes of dried fish, It includes a voice assistant unit that provides advice and progress notifications in a conversational format. A system characterized by the following features.

2. The acquisition unit is, Use location information to obtain real-time weather and humidity online. The system according to feature 1.

3. The aforementioned measuring unit is A weighing scale built into the special drying net measures the weight change of the dried fish in real time. The system according to feature 1.

4. The aforementioned camera unit is Image analysis is used to check the shape and color changes of dried fish, and environmental conditions are also evaluated. The system according to feature 1.

5. The aforementioned voice assistant unit is Provides advice and progress updates in a conversational format. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of environmental data acquisition based on the estimated user emotions. The system according to feature 1.

7. The acquisition unit is, It has a function to select the optimal environmental data according to the type of dried fish. The system according to feature 1.

8. The acquisition unit is, It has a function that predicts future weather and humidity by referring to past environmental data. The system according to feature 1.

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