system

Abstract

This system allows even beginners in pickled plum making to understand the proper process and progress, and to predict the optimal weather conditions. [Solution] The system comprises a recommendation unit, a feedback unit, a weather forecast unit, an instruction generation unit, and a real-time feedback unit. The recommendation unit provides recommendations for appropriate steps when making pickled plums at home. The feedback unit analyzes whether each step is being done correctly and provides feedback when the user posts photos or questions about their ongoing pickled plum making process. The weather forecast unit analyzes weather data for the user's area to predict and suggest the best weather days for making pickled plums. The instruction generation unit generates and provides visual images of the specific steps involved in making pickled plums. The real-time feedback unit displays feedback visually in real time by linking with a smartphone or AR glasses.

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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for beginners in making umeboshi to grasp appropriate processes and progress, and it is also difficult to predict the optimal weather.

[0005] The system according to the embodiment aims to enable beginners in making umeboshi to grasp appropriate processes and progress and predict the optimal weather.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recommendation unit, a feedback unit, a weather forecasting unit, an instruction generation unit, and a real-time feedback unit. The recommendation unit provides recommendations for appropriate steps when a user makes pickled plums at home. The feedback unit analyzes whether each step is being performed correctly and provides feedback when a user posts photos or questions about their ongoing pickled plum making process. The weather forecasting unit analyzes weather data for the user's area to predict and suggest the best weather days for making pickled plums. The instruction generation unit uses AI to generate images of the specific steps involved in the user's ongoing pickled plum making process, providing instructions visually. The real-time feedback unit, by linking with a smartphone or AR glasses, visually displays real-time feedback while the user views the progress of their pickled plum making. [Effects of the Invention]

[0007] The system according to this embodiment allows even beginners in pickled plum making to understand the appropriate process and progress, and to predict the optimal weather conditions. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged 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 including 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. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 AI ​​assistant system according to an embodiment of the present invention is a system for assisting beginners in making pickled plums (umeboshi). This system provides users with recommendations for appropriate steps, progress checks, supplementary advice, and answers to simple questions when they make umeboshi at home. When a user posts photos of their ongoing umeboshi making process or questions, the AI ​​analyzes whether each step is being performed correctly and provides feedback. It also analyzes weather data in the user's area to predict and suggest the best weather days for umeboshi making. Furthermore, the AI ​​generates images of the specific steps the user is currently making umeboshi, providing visual instructions. By linking with a smartphone or AR glasses, the AI ​​visually displays real-time feedback while viewing the progress of the umeboshi making process. This mechanism creates an environment where even beginners can easily make umeboshi, thus contributing to the cultural transmission of traditional knowledge about umeboshi making. In this way, the AI ​​assistant system can provide an environment where even beginners can easily make umeboshi.

[0029] The AI ​​assistant system according to this embodiment comprises a recommendation unit, a feedback unit, a weather forecasting unit, an instruction generation unit, and a real-time feedback unit. The recommendation unit provides recommendations for appropriate steps when a user makes pickled plums at home. The recommendation unit provides detailed instructions for each step, such as how to select plums, the amount of salt, and the timing of pickling. The recommendation unit can also use AI to analyze the user's past pickled plum making history and provide optimal recommendations. When a user posts photos or questions about their ongoing pickled plum making, the feedback unit analyzes whether each step is being done correctly and provides feedback. The feedback unit analyzes, for example, the color and shape of the plums and the state of the salt, and provides appropriate advice. The feedback unit can also use AI to estimate the user's emotions and adjust the way the feedback is expressed based on the estimated emotions of the user. The weather forecasting unit analyzes weather data for the user's area to predict the best weather for making pickled plums and suggests it to the user. The weather forecasting unit predicts suitable days for drying plums based on weather forecasts and notifies the user. The weather forecasting unit can also improve the accuracy of its predictions by referring to past weather data using AI. The instruction generation unit uses AI to generate images of specific steps in the user's ongoing plum-making process, providing instructions visually. The instruction generation unit shows specific steps, such as how to pickle and dry the plums, with images. The instruction generation unit can also use AI to estimate the user's emotions and adjust the way the instructions are presented based on the estimated emotions. The real-time feedback unit, by linking with a smartphone or AR glasses, visually displays real-time feedback while the user views the progress of the plum-making process. The real-time feedback unit can, for example, take a picture of the plums' condition using the smartphone's camera, analyze it on the spot, and provide feedback. The real-time feedback unit can also use AI to estimate the user's emotions and adjust the way the real-time feedback is presented based on the estimated emotions.This allows the AI ​​assistant system according to the embodiment to provide an environment where even beginners can easily make pickled plums.

[0030] The recommendation department provides recommendations for the appropriate steps when users make pickled plums at home. For example, it provides detailed instructions for each step, such as how to select plums, the amount of salt, and the timing of pickling. Specifically, regarding plum selection, it presents selection criteria such as plum variety, ripeness, and size to help users choose the best plums. For salt quantity, it calculates the appropriate salt concentration relative to the weight of the plums and instructs the user on the specific amount. Regarding pickling timing, it considers the condition of the plums and environmental conditions such as temperature and humidity to suggest the optimal timing. The recommendation department can also use AI to analyze the user's past pickled plum-making history and provide optimal recommendations. The AI ​​learns data on the quality and process of pickled plums previously made by the user, understanding the user's preferences and tendencies. This allows it to suggest the most suitable steps and increase the success rate. Furthermore, the recommendation department can receive real-time feedback from users and dynamically update advice according to the progress of the process. For example, after a user starts pickling plums, the system can instruct them to adjust the pickling time according to changes in temperature and humidity. This allows the recommendation system to support the user in making pickled plums under optimal conditions, helping them to produce high-quality pickled plums.

[0031] The feedback system analyzes whether each step of the umeboshi (pickled plum) making process is being carried out correctly when users post photos and questions about their ongoing process, and provides feedback. For example, the feedback system analyzes the color and shape of the plums, the state of the salt, etc., and provides appropriate advice. Specifically, the AI ​​analyzes photos of plums taken by the user and evaluates whether the color is appropriate, whether the shape is intact, and whether the salt is uniform. This allows the system to confirm whether the user is following the process correctly and provides advice for correction as needed. The feedback system can also use AI to estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. The AI ​​analyzes the emotions from the user's posts and photos, and provides encouraging words if the user is feeling anxious, or conversely, encourages further challenges if the user is confident. This helps maintain the user's motivation and allows them to enjoy making umeboshi. Furthermore, the feedback system can respond quickly to user questions. For example, if a user asks, "The color of the plums is changing, is that okay?", the AI ​​generates an answer based on past data and expertise and provides the user with appropriate advice. This allows the feedback system to provide support to users so they can confidently proceed with making pickled plums, thereby increasing their success rate.

[0032] The weather forecasting unit analyzes weather data for the user's region to predict and suggest the best weather days for making pickled plums. For example, based on weather forecasts, the unit predicts suitable days for drying pickled plums and notifies the user. Specifically, it collects weather forecast data and analyzes elements such as temperature, humidity, probability of precipitation, and wind speed to identify the optimal conditions for drying pickled plums. This allows the user to dry the pickled plums at the optimal time and produce high-quality pickled plums. The weather forecasting unit can also improve the accuracy of its predictions by using AI, for example, by referring to past weather data. The AI ​​learns from past weather data and successful pickled plum making data to understand how specific weather conditions affect pickled plum making. This allows for more accurate weather predictions and the suggestion of the optimal timing for the user. Furthermore, the weather forecasting unit can continuously revise its prediction results based on real-time updated weather data to respond to the latest conditions. For example, if the predicted weather suddenly changes, the weather forecasting unit immediately incorporates new data and provides the user with the latest information. This allows the weather forecasting unit to provide support to users so that they can always make pickled plums under optimal conditions, helping them to produce high-quality pickled plums.

[0033] The instruction generation unit uses AI to generate images of specific steps in the user's ongoing umeboshi (pickled plum) making process, providing instructions visually. For example, the unit shows images of specific steps such as how to pickle and dry the plums. Specifically, the AI ​​analyzes the user's progress and generates images that visually indicate the next steps to take. This allows the user to accurately proceed through the process while referring to the visual guide. The instruction generation unit can also use AI to estimate the user's emotions and adjust the way the instructions are presented based on those emotions. The AI ​​analyzes the user's posts and progress to determine their emotions, providing detailed explanations if the user is feeling anxious, and concise instructions if they are confident. This allows for the provision of optimal instructions tailored to the user's level of understanding and emotions. Furthermore, the instruction generation unit can receive user feedback and continuously improve the instruction content. For example, if a user provides feedback stating that "this step is difficult to understand," the AI ​​will revise the instructions based on that feedback, providing clearer instructions to future users. This allows the instruction generation unit to provide support to users in making pickled plums accurately and efficiently, thereby increasing the success rate.

[0034] The real-time feedback unit, by linking with smartphones and AR glasses, visually displays real-time feedback while users view the progress of the umeboshi (pickled plum) making process. For example, the real-time feedback unit can take a picture of the umeboshi using a smartphone camera, analyze it on the spot, and provide feedback. Specifically, when a user takes a picture of the umeboshi with their smartphone camera, the AI ​​analyzes the image and evaluates the color, shape, and salt content of the plums. This allows the user to check the progress of the process in real time and receive advice for corrections as needed. The real-time feedback unit can also use AI to estimate the user's emotions and adjust the way real-time feedback is expressed based on the estimated emotions. The AI ​​analyzes the user's emotions from their facial expressions and tone of voice, and provides encouraging words if the user is feeling anxious, or conversely, feedback that encourages further challenges if the user is confident. This helps maintain the user's motivation and makes the umeboshi making process enjoyable. Furthermore, the real-time feedback unit allows users to receive feedback hands-free by using AR glasses. For example, if a user is wearing AR glasses, they can visually check the state of the umeboshi while the AI-provided feedback is displayed in real time. This allows users to proceed with the process without using their hands, enabling them to make pickled plums efficiently.

[0035] The recommendation unit can provide detailed instructions for each step of the process, such as how to select plums, the amount of salt to use, and the timing of pickling. For example, regarding how to select plums, the recommendation unit can provide criteria such as ripeness, size, and color. It can also provide an appropriate salt ratio to the weight of the plums. Furthermore, regarding the timing of pickling, the recommendation unit can provide the optimal timing based on the condition of the plums and weather conditions. This allows even beginners to proceed with making pickled plums without getting lost. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's past pickled plum making history into a generating AI and have the generating AI execute the optimal recommendation.

[0036] The feedback unit can analyze the color and shape of the plums, the state of the salt, etc., based on photos and questions submitted by the user, and provide appropriate advice. For example, if the plums have changed color, the feedback unit can explain the cause and countermeasures. It can also advise on the appropriate amount of salt if the state of the salt is not appropriate. Furthermore, if the shape of the plums is distorted, the feedback unit can provide points to be careful about in the next step. This allows the user to proceed while confirming whether their work is correct. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input a photo submitted by the user into a generating AI, have the AI ​​analyze the state of the plums, and have the generating AI execute appropriate advice.

[0037] The weather forecasting unit can predict the best day to dry umeboshi (pickled plums) based on weather forecasts and notify the user. The weather forecasting unit analyzes meteorological data such as temperature, humidity, and sunshine duration to predict the optimal day for drying umeboshi. The weather forecasting unit can also make predictions based on meteorological data for the user's area, taking into account region-specific weather patterns. Furthermore, the weather forecasting unit can improve the accuracy of its predictions by referring to past weather data. This allows users to proceed with making umeboshi at the optimal time, regardless of the weather. Some or all of the above-described processes in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input past weather data into a generating AI and have the generating AI perform analysis to improve the accuracy of its predictions.

[0038] The instruction generation unit can show specific steps, such as how to pickle and dry plums, using images. For example, the instruction generation unit can show specific steps for pickling plums using images. It can also show specific steps for drying plums using images. Furthermore, the instruction generation unit can generate images of the specific steps in the user's ongoing plum pickling process, providing instructions visually. This makes it easier for users to understand visually, allowing even beginners to easily make plum pickles. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the user's ongoing plum pickling process into a generation AI, and have the generation AI execute the generation of specific step images.

[0039] The real-time feedback unit can take a picture of the umeboshi (pickled plum) using a smartphone camera, analyze it on the spot, and provide feedback. For example, the real-time feedback unit can take a picture of the umeboshi using a smartphone camera, analyze it on the spot using AI, and provide feedback. In addition, by linking with a smartphone or AR glasses, the real-time feedback unit can also visually display feedback in real time while viewing the progress of umeboshi making. This allows the user to proceed while checking in real time whether their work is appropriate. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input an image of the umeboshi taken with a smartphone camera into a generating AI, analyze it on the spot, and have the generating AI execute the feedback.

[0040] The recommendation unit can analyze the user's past history of making pickled plums and provide optimal recommendations. For example, the recommendation unit can recommend similar procedures based on the user's past successful processes. It can also provide advice to help the user avoid processes that have failed in the past. Furthermore, the recommendation unit can highlight points to be careful of in specific processes based on the user's past history. This enables the provision of optimal recommendations based on the user's past history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past history of making pickled plums into a generating AI and have the generating AI execute optimal recommendations.

[0041] The recommendation unit can provide customized instructions based on the user's current lifestyle and areas of interest. For example, if the user is busy, the recommendation unit will prioritize suggesting tasks that can be completed in a short amount of time. If the user is health-conscious, the recommendation unit can also suggest how to make healthy pickled plums. Furthermore, if the user is working with family, the recommendation unit can suggest tasks that the whole family can enjoy. This allows for the provision of customized instructions tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI execute customized instructions.

[0042] The recommendation unit can provide region-specific advice by taking into account the user's geographical location. For example, the recommendation unit can suggest a method of making pickled plums suited to the climate of the user's region. It can also suggest a process suited to a plum variety specific to the region. Furthermore, the recommendation unit can introduce and suggest traditional methods of making pickled plums in the region. In this way, by providing region-specific advice, the recommendation unit can provide the user with the most suitable recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific advice.

[0043] The recommendation unit can analyze a user's social media activity and provide relevant advice. For example, the recommendation unit can provide advice based on photos and comments about pickled plum making that the user has shared on social media. The recommendation unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the recommendation unit can prioritize suggesting processes that the user is interested in based on their social media activity. This allows the recommendation unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant advice.

[0044] The feedback unit can provide detailed advice based on the condition of the pickled plums. For example, if the plums have changed color, the feedback unit will explain the cause and countermeasures. The feedback unit can also advise on the appropriate amount of salt if the salt level is not appropriate. Furthermore, if the shape of the plums has been damaged, the feedback unit can provide points to note for the next step. This allows for the provision of detailed advice based on the condition of the pickled plums. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the condition of the pickled plums into a generating AI and have the generating AI execute detailed advice.

[0045] The feedback unit can provide optimal advice by referring to the user's past feedback history. For example, the feedback unit can provide advice to avoid similar problems based on feedback the user has received in the past. It can also recommend similar procedures based on the user's past successes. Furthermore, the feedback unit can advise on areas for improvement based on the user's past failures. This allows the feedback unit to provide optimal advice based on the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the user's past feedback history into a generating AI and have the generating AI execute optimal advice.

[0046] The feedback unit can provide region-specific advice by taking into account the user's geographical location. For example, the feedback unit can provide advice on making pickled plums that are suited to the climate of the user's region. It can also provide feedback tailored to the region's specific plum varieties. Furthermore, the feedback unit can provide advice based on traditional local methods of making pickled plums. In this way, by providing region-specific advice, the user can receive the most optimal feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific advice.

[0047] The feedback unit can analyze the user's social media activity and provide relevant advice. For example, the feedback unit can provide feedback based on photos and comments about pickled plum making that the user has shared on social media. The feedback unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the feedback unit can provide feedback on processes that the user is interested in based on their social media activity. This allows the feedback unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant advice.

[0048] The weather forecasting unit can improve the accuracy of its forecasts by referring to past weather data. For example, the weather forecasting unit can analyze weather patterns in a specific region based on past weather data. It can also predict seasonal weather variations based on past weather data. Furthermore, the weather forecasting unit can calculate the probability of specific weather conditions occurring based on past weather data. This enables the provision of highly accurate weather forecasts based on past weather data. Some or all of the above-described processes in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input past weather data into a generating AI and have the generating AI perform analysis to improve the accuracy of the forecast.

[0049] The weather forecasting unit can make forecasts while considering weather patterns specific to the user's region. For example, the weather forecasting unit can make weather forecasts based on specific weather patterns in the user's region. The weather forecasting unit can also make forecasts while considering seasonal weather patterns in the user's region. Furthermore, the weather forecasting unit can improve the accuracy of its forecasts based on historical weather data in the user's region. This enables the provision of highly accurate weather forecasts that take into account region-specific weather patterns. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input data on weather patterns specific to the user's region into a generating AI and have the generating AI perform analysis for making forecasts.

[0050] The weather forecasting unit can provide region-specific forecasts by taking into account the user's geographical location information. For example, the weather forecasting unit can provide weather forecasts tailored to the climate of the user's region. It can also provide weather forecasts that take into account region-specific weather patterns. Furthermore, the weather forecasting unit can improve the accuracy of its forecasts based on historical weather data for the region. This allows the unit to provide the user with the most suitable weather forecast by providing region-specific forecasts. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific forecasts.

[0051] The weather forecasting unit can analyze a user's social media activity and provide relevant forecasts. For example, the weather forecasting unit can provide forecasts based on weather information shared by the user on social media. The weather forecasting unit can also refer to advice from weather experts followed by the user. Furthermore, the weather forecasting unit can provide information on weather topics of interest based on the user's social media activity. This enables the provision of relevant forecasts based on the user's social media activity. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or not. For example, the weather forecasting unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant forecasts.

[0052] The instruction generation unit can provide detailed instructions based on the condition of the pickled plums. For example, if the plums have changed color, the instruction generation unit will explain the cause and countermeasures. The instruction generation unit can also advise on the appropriate amount of salt if the salt level is not appropriate. Furthermore, if the shape of the plums has been damaged, the instruction generation unit can provide points to note for the next step. This allows for the provision of detailed instructions based on the condition of the pickled plums. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the condition of the pickled plums into a generating AI and have the generating AI execute the detailed instructions.

[0053] The instruction generation unit can provide the optimal procedure by referring to the user's past instruction history. For example, the instruction generation unit can recommend similar procedures based on procedures the user has successfully followed in the past. It can also provide advice to help the user avoid procedures they have failed at in the past. Furthermore, the instruction generation unit can highlight points to note regarding specific procedures based on the user's past history. This allows the instruction generation unit to provide the optimal procedure based on the user's past instruction history. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the user's past instruction history into a generation AI and have the generation AI execute the optimal procedure.

[0054] The instruction generation unit can provide region-specific instructions by taking into account the user's geographical location information. For example, the instruction generation unit can suggest a procedure for making pickled plums that is suited to the climate of the user's region. It can also suggest a procedure that is suited to a plum variety specific to the region. Furthermore, the instruction generation unit can introduce and suggest traditional methods of making pickled plums in the region. In this way, by providing region-specific instructions, the instruction generation unit can provide the user with the most suitable instructions. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the user's geographical location information into a generation AI and have the generation AI execute region-specific procedures.

[0055] The instruction generation unit can analyze the user's social media activity and provide relevant instructions. For example, the instruction generation unit can provide instructions based on photos and comments about pickled plum making that the user has shared on social media. The instruction generation unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the instruction generation unit can prioritize and suggest instructions that the user is interested in based on their social media activity. This allows the instruction generation unit to provide relevant instructions based on the user's social media activity. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or not. For example, the instruction generation unit can input data on the user's social media activity into a generation AI and have the generation AI execute the relevant instructions.

[0056] The real-time feedback unit can provide detailed advice based on the condition of the pickled plums. For example, if the plums have changed color, the real-time feedback unit will explain the cause and countermeasures. It can also advise on the appropriate amount of salt if the salt level is not appropriate. Furthermore, if the plums have lost their shape, the real-time feedback unit can provide points to note for the next step. This allows for the provision of detailed advice based on the condition of the pickled plums. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the condition of the pickled plums into a generating AI and have the generating AI provide detailed advice.

[0057] The real-time feedback unit can provide optimal advice by referring to the user's past feedback history. For example, the real-time feedback unit can provide advice to avoid similar problems based on feedback the user has received in the past. It can also recommend similar procedures based on the user's past successes. Furthermore, the real-time feedback unit can advise on areas for improvement based on the user's past failures. This enables the provision of optimal advice based on the user's past feedback history. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's past feedback history into a generating AI and have the generating AI execute optimal advice.

[0058] The real-time feedback unit can provide region-specific advice by taking into account the user's geographical location. For example, the real-time feedback unit can provide advice on making pickled plums that are suited to the climate of the user's region. It can also provide feedback tailored to the region's specific plum varieties. Furthermore, the real-time feedback unit can provide advice based on traditional local methods of making pickled plums. In this way, by providing region-specific advice, the system can provide the user with the most optimal real-time feedback. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific advice.

[0059] The real-time feedback unit can analyze the user's social media activity and provide relevant advice. For example, the real-time feedback unit can provide feedback based on photos and comments about pickled plum making that the user has shared on social media. The real-time feedback unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the real-time feedback unit can provide feedback on processes that the user is interested in based on their social media activity. This allows the unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or not using AI. For example, the real-time feedback unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant advice.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The AI ​​assistant system can also acquire the user's health data and adjust the umeboshi (pickled plum) making process to suit their health condition. For example, if the user has high blood pressure, it can advise reducing salt intake. If the user has allergies, it can recommend ingredients that do not contain allergens. Furthermore, based on the user's exercise level and sleep data, it can prioritize and suggest easier steps if the user is fatigued. This allows the system to provide optimal umeboshi making advice tailored to the user's health condition.

[0062] The AI ​​assistant system can further provide customized advice based on the user's family structure and lifestyle. For example, in households with children, it can suggest steps that can be enjoyed with the children. In dual-income households, it can prioritize suggesting steps that can be completed in a short time. Furthermore, it can provide recipes for small quantities for users living alone. This allows the system to provide optimal pickled plum making advice tailored to the user's lifestyle.

[0063] The AI ​​assistant system can further provide relevant information based on the user's hobbies and interests. For example, it can suggest recipes using pickled plums to a user who enjoys cooking. It can also advise on how to grow plum trees to a user who enjoys gardening. Furthermore, it can provide information about the history and cultural background of pickled plums to a user interested in history and culture. This allows the system to provide relevant information tailored to the user's hobbies and interests.

[0064] The AI ​​assistant system can also acquire local event information and suggest events and workshops related to pickled plum making. For example, it can introduce local pickled plum making classes and plum harvesting events. It can also provide information on traditional local pickled plum making festivals and exhibitions. Furthermore, it can suggest online pickled plum making workshops. This provides users with opportunities to participate in local events and deepen their knowledge of pickled plum making.

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

[0066] Step 1: The recommendation section provides recommendations for the appropriate steps when users make pickled plums at home. For example, it provides detailed instructions for each step, such as how to select plums, the amount of salt to use, and the timing of pickling. It can also use AI to analyze the user's past pickled plum-making history and provide optimal recommendations. Step 2: The feedback section analyzes whether each step of the plum pickling process is being done correctly when users post photos and questions about their ongoing process, and provides feedback. For example, it analyzes the color and shape of the plums, the state of the salt, etc., and provides appropriate advice. It can also use AI to estimate the user's emotions and adjust the way the feedback is expressed based on the estimated emotions. Step 3: The weather forecasting unit analyzes weather data for the user's area to predict and suggest the best weather days for making pickled plums. For example, it predicts suitable days for drying pickled plums based on weather forecasts and notifies the user. It can also use AI to improve the accuracy of predictions by referring to past weather data. Step 4: The instruction generation unit uses AI to generate images of specific steps in the user's ongoing umeboshi (pickled plum) making process, providing instructions visually. For example, it shows specific steps such as how to pickle and dry the plums with images. The AI ​​can also be used to estimate the user's emotions and adjust the way the instructions are presented based on the estimated emotions. Step 5: The real-time feedback unit, by linking with a smartphone or AR glasses, visually displays real-time feedback while the user watches the progress of the pickled plum making process. For example, the user can take a picture of the pickled plum's condition using their smartphone camera, and the system will analyze the image on the spot to provide feedback. It can also use AI to estimate the user's emotions and adjust the way the real-time feedback is presented based on those emotions.

[0067] (Example of form 2) The AI ​​assistant system according to an embodiment of the present invention is a system for assisting beginners in making pickled plums (umeboshi). This system provides users with recommendations for appropriate steps, progress checks, supplementary advice, and answers to simple questions when they make umeboshi at home. When a user posts photos of their ongoing umeboshi making process or questions, the AI ​​analyzes whether each step is being performed correctly and provides feedback. It also analyzes weather data in the user's area to predict and suggest the best weather days for umeboshi making. Furthermore, the AI ​​generates images of the specific steps the user is currently making umeboshi, providing visual instructions. By linking with a smartphone or AR glasses, the AI ​​visually displays real-time feedback while viewing the progress of the umeboshi making process. This mechanism creates an environment where even beginners can easily make umeboshi, thus contributing to the cultural transmission of traditional knowledge about umeboshi making. In this way, the AI ​​assistant system can provide an environment where even beginners can easily make umeboshi.

[0068] The AI ​​assistant system according to this embodiment comprises a recommendation unit, a feedback unit, a weather forecasting unit, an instruction generation unit, and a real-time feedback unit. The recommendation unit provides recommendations for appropriate steps when a user makes pickled plums at home. The recommendation unit provides detailed instructions for each step, such as how to select plums, the amount of salt, and the timing of pickling. The recommendation unit can also use AI to analyze the user's past pickled plum making history and provide optimal recommendations. When a user posts photos or questions about their ongoing pickled plum making, the feedback unit analyzes whether each step is being done correctly and provides feedback. The feedback unit analyzes, for example, the color and shape of the plums and the state of the salt, and provides appropriate advice. The feedback unit can also use AI to estimate the user's emotions and adjust the way the feedback is expressed based on the estimated emotions of the user. The weather forecasting unit analyzes weather data for the user's area to predict the best weather for making pickled plums and suggests it to the user. The weather forecasting unit predicts suitable days for drying plums based on weather forecasts and notifies the user. The weather forecasting unit can also improve the accuracy of its predictions by referring to past weather data using AI. The instruction generation unit uses AI to generate images of specific steps in the user's ongoing plum-making process, providing instructions visually. The instruction generation unit shows specific steps, such as how to pickle and dry the plums, with images. The instruction generation unit can also use AI to estimate the user's emotions and adjust the way the instructions are presented based on the estimated emotions. The real-time feedback unit, by linking with a smartphone or AR glasses, visually displays real-time feedback while the user views the progress of the plum-making process. The real-time feedback unit can, for example, take a picture of the plums' condition using the smartphone's camera, analyze it on the spot, and provide feedback. The real-time feedback unit can also use AI to estimate the user's emotions and adjust the way the real-time feedback is presented based on the estimated emotions.This allows the AI ​​assistant system according to the embodiment to provide an environment where even beginners can easily make pickled plums.

[0069] The recommendation department provides recommendations for the appropriate steps when users make pickled plums at home. For example, it provides detailed instructions for each step, such as how to select plums, the amount of salt, and the timing of pickling. Specifically, regarding plum selection, it presents selection criteria such as plum variety, ripeness, and size to help users choose the best plums. For salt quantity, it calculates the appropriate salt concentration relative to the weight of the plums and instructs the user on the specific amount. Regarding pickling timing, it considers the condition of the plums and environmental conditions such as temperature and humidity to suggest the optimal timing. The recommendation department can also use AI to analyze the user's past pickled plum-making history and provide optimal recommendations. The AI ​​learns data on the quality and process of pickled plums previously made by the user, understanding the user's preferences and tendencies. This allows it to suggest the most suitable steps and increase the success rate. Furthermore, the recommendation department can receive real-time feedback from users and dynamically update advice according to the progress of the process. For example, after a user starts pickling plums, the system can instruct them to adjust the pickling time according to changes in temperature and humidity. This allows the recommendation system to support the user in making pickled plums under optimal conditions, helping them to produce high-quality pickled plums.

[0070] The feedback system analyzes whether each step of the umeboshi (pickled plum) making process is being carried out correctly when users post photos and questions about their ongoing process, and provides feedback. For example, the feedback system analyzes the color and shape of the plums, the state of the salt, etc., and provides appropriate advice. Specifically, the AI ​​analyzes photos of plums taken by the user and evaluates whether the color is appropriate, whether the shape is intact, and whether the salt is uniform. This allows the system to confirm whether the user is following the process correctly and provides advice for correction as needed. The feedback system can also use AI to estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. The AI ​​analyzes the emotions from the user's posts and photos, and provides encouraging words if the user is feeling anxious, or conversely, encourages further challenges if the user is confident. This helps maintain the user's motivation and allows them to enjoy making umeboshi. Furthermore, the feedback system can respond quickly to user questions. For example, if a user asks, "The color of the plums is changing, is that okay?", the AI ​​generates an answer based on past data and expertise and provides the user with appropriate advice. This allows the feedback system to provide support to users so they can confidently proceed with making pickled plums, thereby increasing their success rate.

[0071] The weather forecasting unit analyzes weather data for the user's region to predict and suggest the best weather days for making pickled plums. For example, based on weather forecasts, the unit predicts suitable days for drying pickled plums and notifies the user. Specifically, it collects weather forecast data and analyzes elements such as temperature, humidity, probability of precipitation, and wind speed to identify the optimal conditions for drying pickled plums. This allows the user to dry the pickled plums at the optimal time and produce high-quality pickled plums. The weather forecasting unit can also improve the accuracy of its predictions by using AI, for example, by referring to past weather data. The AI ​​learns from past weather data and successful pickled plum making data to understand how specific weather conditions affect pickled plum making. This allows for more accurate weather predictions and the suggestion of the optimal timing for the user. Furthermore, the weather forecasting unit can continuously revise its prediction results based on real-time updated weather data to respond to the latest conditions. For example, if the predicted weather suddenly changes, the weather forecasting unit immediately incorporates new data and provides the user with the latest information. This allows the weather forecasting unit to provide support to users so that they can always make pickled plums under optimal conditions, helping them to produce high-quality pickled plums.

[0072] The instruction generation unit uses AI to generate images of specific steps in the user's ongoing umeboshi (pickled plum) making process, providing instructions visually. For example, the unit shows images of specific steps such as how to pickle and dry the plums. Specifically, the AI ​​analyzes the user's progress and generates images that visually indicate the next steps to take. This allows the user to accurately proceed through the process while referring to the visual guide. The instruction generation unit can also use AI to estimate the user's emotions and adjust the way the instructions are presented based on those emotions. The AI ​​analyzes the user's posts and progress to determine their emotions, providing detailed explanations if the user is feeling anxious, and concise instructions if they are confident. This allows for the provision of optimal instructions tailored to the user's level of understanding and emotions. Furthermore, the instruction generation unit can receive user feedback and continuously improve the instruction content. For example, if a user provides feedback stating that "this step is difficult to understand," the AI ​​will revise the instructions based on that feedback, providing clearer instructions to future users. This allows the instruction generation unit to provide support to users in making pickled plums accurately and efficiently, thereby increasing the success rate.

[0073] The real-time feedback unit, by linking with smartphones and AR glasses, visually displays real-time feedback while users view the progress of the umeboshi (pickled plum) making process. For example, the real-time feedback unit can take a picture of the umeboshi using a smartphone camera, analyze it on the spot, and provide feedback. Specifically, when a user takes a picture of the umeboshi with their smartphone camera, the AI ​​analyzes the image and evaluates the color, shape, and salt content of the plums. This allows the user to check the progress of the process in real time and receive advice for corrections as needed. The real-time feedback unit can also use AI to estimate the user's emotions and adjust the way real-time feedback is expressed based on the estimated emotions. The AI ​​analyzes the user's emotions from their facial expressions and tone of voice, and provides encouraging words if the user is feeling anxious, or conversely, feedback that encourages further challenges if the user is confident. This helps maintain the user's motivation and makes the umeboshi making process enjoyable. Furthermore, the real-time feedback unit allows users to receive feedback hands-free by using AR glasses. For example, if a user is wearing AR glasses, they can visually check the state of the umeboshi while the AI-provided feedback is displayed in real time. This allows users to proceed with the process without using their hands, enabling them to make pickled plums efficiently.

[0074] The recommendation unit can provide detailed instructions for each step of the process, such as how to select plums, the amount of salt to use, and the timing of pickling. For example, regarding how to select plums, the recommendation unit can provide criteria such as ripeness, size, and color. It can also provide an appropriate salt ratio to the weight of the plums. Furthermore, regarding the timing of pickling, the recommendation unit can provide the optimal timing based on the condition of the plums and weather conditions. This allows even beginners to proceed with making pickled plums without getting lost. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's past pickled plum making history into a generating AI and have the generating AI execute the optimal recommendation.

[0075] The feedback unit can analyze the color and shape of the plums, the state of the salt, etc., based on photos and questions submitted by the user, and provide appropriate advice. For example, if the plums have changed color, the feedback unit can explain the cause and countermeasures. It can also advise on the appropriate amount of salt if the state of the salt is not appropriate. Furthermore, if the shape of the plums is distorted, the feedback unit can provide points to be careful about in the next step. This allows the user to proceed while confirming whether their work is correct. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input a photo submitted by the user into a generating AI, have the AI ​​analyze the state of the plums, and have the generating AI execute appropriate advice.

[0076] The weather forecasting unit can predict the best day to dry umeboshi (pickled plums) based on weather forecasts and notify the user. The weather forecasting unit analyzes meteorological data such as temperature, humidity, and sunshine duration to predict the optimal day for drying umeboshi. The weather forecasting unit can also make predictions based on meteorological data for the user's area, taking into account region-specific weather patterns. Furthermore, the weather forecasting unit can improve the accuracy of its predictions by referring to past weather data. This allows users to proceed with making umeboshi at the optimal time, regardless of the weather. Some or all of the above-described processes in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input past weather data into a generating AI and have the generating AI perform analysis to improve the accuracy of its predictions.

[0077] The instruction generation unit can show specific steps, such as how to pickle and dry plums, using images. For example, the instruction generation unit can show specific steps for pickling plums using images. It can also show specific steps for drying plums using images. Furthermore, the instruction generation unit can generate images of the specific steps in the user's ongoing plum pickling process, providing instructions visually. This makes it easier for users to understand visually, allowing even beginners to easily make plum pickles. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the user's ongoing plum pickling process into a generation AI, and have the generation AI execute the generation of specific step images.

[0078] The real-time feedback unit can take a picture of the umeboshi (pickled plum) using a smartphone camera, analyze it on the spot, and provide feedback. For example, the real-time feedback unit can take a picture of the umeboshi using a smartphone camera, analyze it on the spot using AI, and provide feedback. In addition, by linking with a smartphone or AR glasses, the real-time feedback unit can also visually display feedback in real time while viewing the progress of umeboshi making. This allows the user to proceed while checking in real time whether their work is appropriate. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input an image of the umeboshi taken with a smartphone camera into a generating AI, analyze it on the spot, and have the generating AI execute the feedback.

[0079] The recommendation unit can estimate the user's emotions and adjust the recommendation content based on the estimated emotions. For example, if the user is stressed, the recommendation unit can provide simple and intuitive instructions to simplify the process. If the user is relaxed, the recommendation unit can also provide instructions that include detailed explanations and background information. Furthermore, if the user is anxious, the recommendation unit can provide instructions that include encouraging messages to provide a sense of security. This allows the recommendation unit to provide appropriate recommendations that match 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. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into the generative AI and have the generative AI perform analysis to adjust the recommendation content.

[0080] The recommendation unit can analyze the user's past history of making pickled plums and provide optimal recommendations. For example, the recommendation unit can recommend similar procedures based on the user's past successful processes. It can also provide advice to help the user avoid processes that have failed in the past. Furthermore, the recommendation unit can highlight points to be careful of in specific processes based on the user's past history. This enables the provision of optimal recommendations based on the user's past history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past history of making pickled plums into a generating AI and have the generating AI execute optimal recommendations.

[0081] The recommendation unit can provide customized instructions based on the user's current lifestyle and areas of interest. For example, if the user is busy, the recommendation unit will prioritize suggesting tasks that can be completed in a short amount of time. If the user is health-conscious, the recommendation unit can also suggest how to make healthy pickled plums. Furthermore, if the user is working with family, the recommendation unit can suggest tasks that the whole family can enjoy. This allows for the provision of customized instructions tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI execute customized instructions.

[0082] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated emotions. For example, if the user is feeling stressed, the recommendation unit will prioritize suggesting the simplest and most effective process. If the user is relaxed, the recommendation unit can also prioritize suggesting processes that can be enjoyed over time. Furthermore, if the user is feeling anxious, the recommendation unit can prioritize suggesting processes that provide a sense of security. This allows for the provision of recommendations with priorities tailored 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI perform analysis to determine the priority of recommendations.

[0083] The recommendation unit can provide region-specific advice by taking into account the user's geographical location. For example, the recommendation unit can suggest a method of making pickled plums suited to the climate of the user's region. It can also suggest a process suited to a plum variety specific to the region. Furthermore, the recommendation unit can introduce and suggest traditional methods of making pickled plums in the region. In this way, by providing region-specific advice, the recommendation unit can provide the user with the most suitable recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific advice.

[0084] The recommendation unit can analyze a user's social media activity and provide relevant advice. For example, the recommendation unit can provide advice based on photos and comments about pickled plum making that the user has shared on social media. The recommendation unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the recommendation unit can prioritize suggesting processes that the user is interested in based on their social media activity. This allows the recommendation unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant advice.

[0085] The feedback unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated emotions. For example, if the user is stressed, the feedback unit provides concise and clear feedback. If the user is relaxed, the feedback unit can also provide feedback that includes detailed explanations. Furthermore, if the user is anxious, the feedback unit can provide feedback that includes encouraging messages. This allows for the provision of appropriate feedback 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. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI perform analysis to adjust the way the feedback is expressed.

[0086] The feedback unit can provide detailed advice based on the condition of the pickled plums. For example, if the plums have changed color, the feedback unit will explain the cause and countermeasures. The feedback unit can also advise on the appropriate amount of salt if the salt level is not appropriate. Furthermore, if the shape of the plums has been damaged, the feedback unit can provide points to note for the next step. This allows for the provision of detailed advice based on the condition of the pickled plums. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the condition of the pickled plums into a generating AI and have the generating AI execute detailed advice.

[0087] The feedback unit can provide optimal advice by referring to the user's past feedback history. For example, the feedback unit can provide advice to avoid similar problems based on feedback the user has received in the past. It can also recommend similar procedures based on the user's past successes. Furthermore, the feedback unit can advise on areas for improvement based on the user's past failures. This allows the feedback unit to provide optimal advice based on the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input the user's past feedback history into a generating AI and have the generating AI execute optimal advice.

[0088] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can prioritize providing the most important feedback. If the user is relaxed, the feedback unit can also prioritize providing detailed feedback. Furthermore, if the user is anxious, the feedback unit can prioritize providing reassuring feedback. This allows for the provision of feedback with priorities tailored 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform analysis to determine the priority of feedback.

[0089] The feedback unit can provide region-specific advice by taking into account the user's geographical location. For example, the feedback unit can provide advice on making pickled plums that are suited to the climate of the user's region. It can also provide feedback tailored to the region's specific plum varieties. Furthermore, the feedback unit can provide advice based on traditional local methods of making pickled plums. In this way, by providing region-specific advice, the user can receive the most optimal feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific advice.

[0090] The feedback unit can analyze the user's social media activity and provide relevant advice. For example, the feedback unit can provide feedback based on photos and comments about pickled plum making that the user has shared on social media. The feedback unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the feedback unit can provide feedback on processes that the user is interested in based on their social media activity. This allows the feedback unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant advice.

[0091] The weather forecasting unit can estimate the user's emotions and adjust the weather forecast notification method based on the estimated emotions. For example, if the user is feeling stressed, the weather forecasting unit can provide a concise and clear notification. If the user is relaxed, the weather forecasting unit can also provide a notification with a detailed explanation. Furthermore, if the user is feeling anxious, the weather forecasting unit can provide a reassuring notification. This allows for the provision of an appropriate weather forecast notification method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or not using AI. For example, the weather forecasting unit can input user emotion data into the generative AI and have the generative AI perform analysis to adjust the notification method.

[0092] The weather forecasting unit can improve the accuracy of its forecasts by referring to past weather data. For example, the weather forecasting unit can analyze weather patterns in a specific region based on past weather data. It can also predict seasonal weather variations based on past weather data. Furthermore, the weather forecasting unit can calculate the probability of specific weather conditions occurring based on past weather data. This enables the provision of highly accurate weather forecasts based on past weather data. Some or all of the above-described processes in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input past weather data into a generating AI and have the generating AI perform analysis to improve the accuracy of the forecast.

[0093] The weather forecasting unit can make forecasts while considering weather patterns specific to the user's region. For example, the weather forecasting unit can make weather forecasts based on specific weather patterns in the user's region. The weather forecasting unit can also make forecasts while considering seasonal weather patterns in the user's region. Furthermore, the weather forecasting unit can improve the accuracy of its forecasts based on historical weather data in the user's region. This enables the provision of highly accurate weather forecasts that take into account region-specific weather patterns. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input data on weather patterns specific to the user's region into a generating AI and have the generating AI perform analysis for making forecasts.

[0094] The weather forecasting unit can estimate the user's emotions and determine the priority of weather forecasts based on the estimated emotions. For example, if the user is feeling stressed, the weather forecasting unit can prioritize providing the most important weather forecast. It can also prioritize providing detailed weather forecasts if the user is relaxed. Furthermore, if the user is feeling anxious, it can prioritize providing reassuring weather forecasts. This allows for the provision of weather forecasts with priorities tailored 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the weather forecasting unit may be performed using AI or not. For example, the weather forecasting unit can input user emotion data into a generative AI and have the generative AI perform analysis to determine the priority of weather forecasts.

[0095] The weather forecasting unit can provide region-specific forecasts by taking into account the user's geographical location information. For example, the weather forecasting unit can provide weather forecasts tailored to the climate of the user's region. It can also provide weather forecasts that take into account region-specific weather patterns. Furthermore, the weather forecasting unit can improve the accuracy of its forecasts based on historical weather data for the region. This allows the unit to provide the user with the most suitable weather forecast by providing region-specific forecasts. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or without AI. For example, the weather forecasting unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific forecasts.

[0096] The weather forecasting unit can analyze a user's social media activity and provide relevant forecasts. For example, the weather forecasting unit can provide forecasts based on weather information shared by the user on social media. The weather forecasting unit can also refer to advice from weather experts followed by the user. Furthermore, the weather forecasting unit can provide information on weather topics of interest based on the user's social media activity. This enables the provision of relevant forecasts based on the user's social media activity. Some or all of the above processing in the weather forecasting unit may be performed using AI, for example, or not. For example, the weather forecasting unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant forecasts.

[0097] The instruction generation unit can estimate the user's emotions and adjust the way the instructions are expressed based on the estimated emotions. For example, if the user is feeling stressed, the instruction generation unit can provide concise and clear instructions. If the user is relaxed, the instruction generation unit can also provide instructions that include detailed explanations. Furthermore, if the user is feeling anxious, the instruction generation unit can provide instructions that include encouraging messages. This allows for the provision of appropriate instructions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the instruction generation unit may be performed using AI, or not. For example, the instruction generation unit can input user emotion data into the generative AI and have the generative AI perform analysis to adjust the way the instructions are expressed.

[0098] The instruction generation unit can provide detailed instructions based on the condition of the pickled plums. For example, if the plums have changed color, the instruction generation unit will explain the cause and countermeasures. The instruction generation unit can also advise on the appropriate amount of salt if the salt level is not appropriate. Furthermore, if the shape of the plums has been damaged, the instruction generation unit can provide points to note for the next step. This allows for the provision of detailed instructions based on the condition of the pickled plums. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the condition of the pickled plums into a generating AI and have the generating AI execute the detailed instructions.

[0099] The instruction generation unit can provide the optimal procedure by referring to the user's past instruction history. For example, the instruction generation unit can recommend similar procedures based on procedures the user has successfully followed in the past. It can also provide advice to help the user avoid procedures they have failed at in the past. Furthermore, the instruction generation unit can highlight points to note regarding specific procedures based on the user's past history. This allows the instruction generation unit to provide the optimal procedure based on the user's past instruction history. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the user's past instruction history into a generation AI and have the generation AI execute the optimal procedure.

[0100] The instruction generation unit can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. For example, if the user is feeling stressed, the instruction generation unit will prioritize suggesting the simplest and most effective steps. If the user is relaxed, the instruction generation unit can also prioritize suggesting steps that the user can enjoy taking their time with. Furthermore, if the user is feeling anxious, the instruction generation unit can prioritize suggesting steps that provide a sense of security. This allows the instruction generation unit to provide instructions with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction generation unit may be performed using AI, or not using AI. For example, the instruction generation unit can input user emotion data into the generative AI and have the generative AI perform analysis to determine the priority of instructions.

[0101] The instruction generation unit can provide region-specific instructions by taking into account the user's geographical location information. For example, the instruction generation unit can suggest a procedure for making pickled plums that is suited to the climate of the user's region. It can also suggest a procedure that is suited to a plum variety specific to the region. Furthermore, the instruction generation unit can introduce and suggest traditional methods of making pickled plums in the region. In this way, by providing region-specific instructions, the instruction generation unit can provide the user with the most suitable instructions. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or without AI. For example, the instruction generation unit can input the user's geographical location information into a generation AI and have the generation AI execute region-specific procedures.

[0102] The instruction generation unit can analyze the user's social media activity and provide relevant instructions. For example, the instruction generation unit can provide instructions based on photos and comments about pickled plum making that the user has shared on social media. The instruction generation unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the instruction generation unit can prioritize and suggest instructions that the user is interested in based on their social media activity. This allows the instruction generation unit to provide relevant instructions based on the user's social media activity. Some or all of the above processing in the instruction generation unit may be performed using AI, for example, or not. For example, the instruction generation unit can input data on the user's social media activity into a generation AI and have the generation AI execute the relevant instructions.

[0103] The real-time feedback unit can estimate the user's emotions and adjust the way real-time feedback is presented based on the estimated emotions. For example, if the user is feeling stressed, the real-time feedback unit can provide concise and clear feedback. If the user is relaxed, the real-time feedback unit can also provide feedback that includes detailed explanations. Furthermore, if the user is feeling anxious, the real-time feedback unit can provide feedback that includes encouraging messages. This allows for the provision of appropriate real-time feedback tailored 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the real-time feedback unit may be performed using AI or not. For example, the real-time feedback unit can input user emotion data into the generative AI and have the generative AI perform analysis to adjust the way real-time feedback is presented.

[0104] The real-time feedback unit can provide detailed advice based on the condition of the pickled plums. For example, if the plums have changed color, the real-time feedback unit will explain the cause and countermeasures. It can also advise on the appropriate amount of salt if the salt level is not appropriate. Furthermore, if the plums have lost their shape, the real-time feedback unit can provide points to note for the next step. This allows for the provision of detailed advice based on the condition of the pickled plums. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the condition of the pickled plums into a generating AI and have the generating AI provide detailed advice.

[0105] The real-time feedback unit can provide optimal advice by referring to the user's past feedback history. For example, the real-time feedback unit can provide advice to avoid similar problems based on feedback the user has received in the past. It can also recommend similar procedures based on the user's past successes. Furthermore, the real-time feedback unit can advise on areas for improvement based on the user's past failures. This enables the provision of optimal advice based on the user's past feedback history. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's past feedback history into a generating AI and have the generating AI execute optimal advice.

[0106] The real-time feedback unit can estimate the user's emotions and determine the priority of real-time feedback based on the estimated emotions. For example, if the user is feeling stressed, the real-time feedback unit can prioritize providing the most important feedback. It can also prioritize providing detailed feedback if the user is relaxed. Furthermore, if the user is feeling anxious, the real-time feedback unit can prioritize providing reassuring feedback. This allows for the provision of real-time feedback with priorities tailored 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the real-time feedback unit may be performed using AI or not. For example, the real-time feedback unit can input user emotion data into a generative AI and have the generative AI perform analysis to determine the priority of real-time feedback.

[0107] The real-time feedback unit can provide region-specific advice by taking into account the user's geographical location. For example, the real-time feedback unit can provide advice on making pickled plums that are suited to the climate of the user's region. It can also provide feedback tailored to the region's specific plum varieties. Furthermore, the real-time feedback unit can provide advice based on traditional local methods of making pickled plums. In this way, by providing region-specific advice, the system can provide the user with the most optimal real-time feedback. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's geographical location information into a generating AI and have the generating AI execute region-specific advice.

[0108] The real-time feedback unit can analyze the user's social media activity and provide relevant advice. For example, the real-time feedback unit can provide feedback based on photos and comments about pickled plum making that the user has shared on social media. The real-time feedback unit can also refer to advice from pickled plum making experts that the user follows. Furthermore, the real-time feedback unit can provide feedback on processes that the user is interested in based on their social media activity. This allows the unit to provide relevant advice based on the user's social media activity. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or not using AI. For example, the real-time feedback unit can input data on the user's social media activity into a generating AI and have the generating AI execute relevant advice.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The AI ​​assistant system can also acquire the user's health data and adjust the umeboshi (pickled plum) making process to suit their health condition. For example, if the user has high blood pressure, it can advise reducing salt intake. If the user has allergies, it can recommend ingredients that do not contain allergens. Furthermore, based on the user's exercise level and sleep data, it can prioritize and suggest easier steps if the user is fatigued. This allows the system to provide optimal umeboshi making advice tailored to the user's health condition.

[0111] The AI ​​assistant system can further provide customized advice based on the user's family structure and lifestyle. For example, in households with children, it can suggest steps that can be enjoyed with the children. In dual-income households, it can prioritize suggesting steps that can be completed in a short time. Furthermore, it can provide recipes for small quantities for users living alone. This allows the system to provide optimal pickled plum making advice tailored to the user's lifestyle.

[0112] The AI ​​assistant system can further provide relevant information based on the user's hobbies and interests. For example, it can suggest recipes using pickled plums to a user who enjoys cooking. It can also advise on how to grow plum trees to a user who enjoys gardening. Furthermore, it can provide information about the history and cultural background of pickled plums to a user interested in history and culture. This allows the system to provide relevant information tailored to the user's hobbies and interests.

[0113] The AI ​​assistant system can further estimate the user's emotions, monitor the progress of the pickled plum making process based on those emotions, and provide encouragement and advice at the appropriate time. For example, if the user is feeling stressed, it can send an encouraging message. If the user is relaxed, it can prompt them to proceed to the next step. Furthermore, if the user is feeling anxious, it can provide reassuring advice. This allows the system to provide appropriate support tailored to the user's emotions.

[0114] The AI ​​assistant system can also acquire local event information and suggest events and workshops related to pickled plum making. For example, it can introduce local pickled plum making classes and plum harvesting events. It can also provide information on traditional local pickled plum making festivals and exhibitions. Furthermore, it can suggest online pickled plum making workshops. This provides users with opportunities to participate in local events and deepen their knowledge of pickled plum making.

[0115] The AI ​​assistant system can further estimate the user's emotions, evaluate the progress of the pickled plum making process based on those emotions, and provide appropriate feedback. For example, if the user is feeling stressed, it can provide concise and clear feedback. If the user is relaxed, it can provide feedback with detailed explanations. Furthermore, if the user is feeling anxious, it can provide feedback that includes encouraging messages. This allows the system to provide appropriate feedback tailored to the user's emotions.

[0116] The AI ​​assistant system can further estimate the user's emotions and adjust the umeboshi (pickled plum) making process based on those emotions. For example, if the user is stressed, it can suggest a simple and intuitive process. If the user is relaxed, it can suggest a process that includes detailed explanations and background information. Furthermore, if the user is anxious, it can suggest a process that provides reassurance. This allows the system to provide an appropriate process tailored to the user's emotions.

[0117] The AI ​​assistant system can further estimate the user's emotions, monitor the progress of the pickled plum making process based on those emotions, and suggest breaks at appropriate times. For example, if the user is feeling stressed, it can prompt them to take a break. If the user is relaxed, it can also encourage them to proceed to the next step. Furthermore, if the user is feeling anxious, it can offer reassuring advice. This allows the system to provide appropriate support tailored to the user's emotions.

[0118] The AI ​​assistant system can further estimate the user's emotions, evaluate the progress of the pickled plum making process based on those emotions, and provide appropriate feedback. For example, if the user is feeling stressed, it can provide concise and clear feedback. If the user is relaxed, it can provide feedback with detailed explanations. Furthermore, if the user is feeling anxious, it can provide feedback that includes encouraging messages. This allows the system to provide appropriate feedback tailored to the user's emotions.

[0119] The AI ​​assistant system can further estimate the user's emotions, monitor the progress of the pickled plum making process based on those emotions, and suggest breaks at appropriate times. For example, if the user is feeling stressed, it can prompt them to take a break. If the user is relaxed, it can also encourage them to proceed to the next step. Furthermore, if the user is feeling anxious, it can offer reassuring advice. This allows the system to provide appropriate support tailored to the user's emotions.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The recommendation section provides recommendations for the appropriate steps when users make pickled plums at home. For example, it provides detailed instructions for each step, such as how to select plums, the amount of salt to use, and the timing of pickling. It can also use AI to analyze the user's past pickled plum-making history and provide optimal recommendations. Step 2: The feedback section analyzes whether each step of the plum pickling process is being done correctly when users post photos and questions about their ongoing process, and provides feedback. For example, it analyzes the color and shape of the plums, the state of the salt, etc., and provides appropriate advice. It can also use AI to estimate the user's emotions and adjust the way the feedback is expressed based on the estimated emotions. Step 3: The weather forecasting unit analyzes weather data for the user's area to predict and suggest the best weather days for making pickled plums. For example, it predicts suitable days for drying pickled plums based on weather forecasts and notifies the user. It can also use AI to improve the accuracy of predictions by referring to past weather data. Step 4: The instruction generation unit uses AI to generate images of specific steps in the user's ongoing umeboshi (pickled plum) making process, providing instructions visually. For example, it shows specific steps such as how to pickle and dry the plums with images. The AI ​​can also be used to estimate the user's emotions and adjust the way the instructions are presented based on the estimated emotions. Step 5: The real-time feedback unit, by linking with a smartphone or AR glasses, visually displays real-time feedback while the user watches the progress of the pickled plum making process. For example, the user can take a picture of the pickled plum's condition using their smartphone camera, and the system will analyze the image on the spot to provide feedback. It can also use AI to estimate the user's emotions and adjust the way the real-time feedback is presented based on those emotions.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the recommendation unit, feedback unit, weather forecasting unit, instruction generation unit, and real-time feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the smart device 14, which analyzes the user's past history of making pickled plums and provides recommendations for the optimal process. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes photos and questions posted by the user and provides appropriate feedback. The weather forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes weather data for the user's area and predicts days with optimal weather. The instruction generation unit is implemented by the control unit 46A of the smart device 14, which generates specific procedure images and provides visual instructions. The real-time feedback unit is implemented by the control unit 46A of the smart device 14, which uses the smartphone camera to photograph the condition of the pickled plums, analyzes it on the spot, and provides feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the recommendation unit, feedback unit, weather forecasting unit, instruction generation unit, and real-time feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the user's past history of making pickled plums and provides recommendations for the optimal process. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes photos and questions posted by the user and provides appropriate feedback. The weather forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes weather data for the user's area and predicts days with optimal weather. The instruction generation unit is implemented by the control unit 46A of the smart glasses 214, which generates specific procedure images and provides instructions visually. The real-time feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214. The smart glasses 214's camera is used to photograph the condition of the pickled plums, analyzes them in real time, and provides feedback. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the recommendation unit, feedback unit, weather forecasting unit, instruction generation unit, and real-time feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the user's past history of making pickled plums and provides recommendations for the optimal process. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes photos and questions posted by the user and provides appropriate feedback. The weather forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes weather data for the user's area and predicts days with optimal weather. The instruction generation unit is implemented by the control unit 46A of the headset terminal 314, which generates specific procedure images and provides instructions visually. The real-time feedback unit is implemented, for example, by the control unit 46A of the headset terminal 314. The headset terminal 314's camera is used to photograph the condition of the pickled plums, analyzes them in real time, and provides feedback. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the recommendation unit, feedback unit, weather forecasting unit, instruction generation unit, and real-time feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the robot 414, which analyzes the user's past history of making pickled plums and provides recommendations for the optimal process. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes photos and questions posted by the user and provides appropriate feedback. The weather forecasting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes weather data for the user's area and predicts days with optimal weather. The instruction generation unit is implemented by the control unit 46A of the robot 414, which generates specific procedure images and provides visual instructions. The real-time feedback unit is implemented by the control unit 46A of the robot 414, which uses the robot 414's camera to photograph the state of the pickled plums, analyzes them in real time, and provides feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A recommendation section provides recommendations for the appropriate steps when users make pickled plums at home, When a user posts photos or questions about their ongoing umeboshi (pickled plum) making process, a feedback department analyzes whether each step is being done correctly and provides feedback. The weather forecasting unit analyzes weather data in the user's area to predict and suggest the best weather days for making pickled plums. An instruction generation unit that uses AI to generate images of the specific steps involved in the user's ongoing process of making pickled plums, and provides instructions visually. It includes a real-time feedback unit that, by linking with a smartphone or AR glasses, displays real-time feedback visually while you watch the progress of making pickled plums. A system characterized by the following features. (Note 2) The aforementioned recommendation unit, It provides detailed instructions for each step, such as how to select plums, the amount of salt to use, and the timing of pickling. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is Based on photos and questions submitted by users, the system analyzes the color and shape of the plums, the condition of the salt, etc., and provides appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned weather forecasting unit is Based on weather forecasts, the system predicts the best day to dry plums and notifies the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The instruction generation unit, This guide shows the specific steps for pickling and drying plums with images. The system described in Appendix 1, characterized by the features described herein. (Note 6) The real-time feedback unit described above is: The system uses a smartphone camera to photograph the condition of the pickled plums, analyzes the data on the spot, and provides feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recommendation unit, It estimates the user's emotions and adjusts the recommendation content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recommendation unit, We analyze the user's past history of making pickled plums and provide optimal recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recommendation unit, Provides customized instructions based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recommendation unit, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recommendation unit, Providing region-specific advice that takes the user's geographical location into account. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation unit, Analyze users' social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned feedback unit is We provide detailed advice based on the condition of the pickled plums. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned feedback unit is We provide optimal advice by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned feedback unit is Providing region-specific advice that takes the user's geographical location into account. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is Analyze users' social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned weather forecasting unit is The system estimates the user's emotions and adjusts the weather forecast notification method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned weather forecasting unit is By referring to past weather data, we can improve the accuracy of our forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned weather forecasting unit is The forecast takes into account the weather patterns specific to the user's region. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned weather forecasting unit is The system estimates user sentiment and prioritizes weather forecasts based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned weather forecasting unit is Provides region-specific predictions that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned weather forecasting unit is Analyzes users' social media activity and provides relevant predictions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The instruction generation unit, It estimates the user's emotions and adjusts the way instructions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The instruction generation unit, Detailed instructions are provided based on the condition of the pickled plums. The system described in Appendix 1, characterized by the features described herein. (Note 27) The instruction generation unit, Referencing the user's past instruction history provides the optimal procedure. The system described in Appendix 1, characterized by the features described herein. (Note 28) The instruction generation unit, It estimates the user's emotions and prioritizes instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The instruction generation unit, Provide region-specific procedures that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The instruction generation unit, Analyze users' social media activity and provide relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The real-time feedback unit described above is: It estimates the user's emotions and adjusts how real-time feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The real-time feedback unit described above is: We provide detailed advice based on the condition of the pickled plums. The system described in Appendix 1, characterized by the features described herein. (Note 33) The real-time feedback unit described above is: We provide optimal advice by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The real-time feedback unit described above is: It estimates the user's emotions and prioritizes real-time feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The real-time feedback unit described above is: Providing region-specific advice that takes the user's geographical location into account. The system described in Appendix 1, characterized by the features described herein. (Note 36) The real-time feedback unit described above is: Analyze users' social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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. A recommendation section provides recommendations for the appropriate steps when users make pickled plums at home, When a user posts photos or questions about their ongoing umeboshi (pickled plum) making process, a feedback department analyzes whether each step is being done correctly and provides feedback. The weather forecasting unit analyzes weather data in the user's area to predict and suggest the best weather days for making pickled plums. An instruction generation unit that uses AI to generate images of the specific steps involved in the user's ongoing umeboshi (pickled plum) making process and provides instructions visually, It includes a real-time feedback unit that, by linking with a smartphone or AR glasses, displays real-time feedback visually while you watch the progress of making pickled plums. A system characterized by the following features.

2. The aforementioned recommendation unit, It provides detailed instructions for each step, such as how to select plums, the amount of salt to use, and the timing of pickling. The system according to feature 1.

3. The aforementioned feedback unit is Based on photos and questions submitted by users, the system analyzes the color and shape of the plums, the condition of the salt, etc., and provides appropriate advice. The system according to feature 1.

4. The aforementioned weather forecasting unit is Based on weather forecasts, the system predicts the best day to dry plums and notifies the user. The system according to feature 1.

5. The instruction generation unit, This guide shows the specific steps for pickling and drying plums with images. The system according to feature 1.

6. The real-time feedback unit described above is: The system uses a smartphone camera to photograph the condition of the pickled plums, analyzes the data on the spot, and provides feedback. The system according to feature 1.

7. The aforementioned recommendation unit, It estimates the user's emotions and adjusts the recommendation content based on those estimated emotions. The system according to feature 1.

8. The aforementioned recommendation unit, We analyze the user's past history of making pickled plums and provide optimal recommendations. The system according to feature 1.

Images