system

Abstract

We provide the system. [Solution] Means of acquiring climate data, Means of obtaining market data, Means for predicting consumer trends, Means for optimizing the food supply network, A system that includes this.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern society, there is a large gap between food production and consumption, and the increase in food waste and food shortages in certain regions have become serious problems. In particular, due to climate change and market fluctuations, the stability of food supply is threatened, so it is necessary to efficiently solve these problems.

Means for Solving the Problems

[0005] The present invention provides a system that effectively utilizes climate data and market data to predict consumption trends, thereby optimizing the food supply network and implementing inventory management to reduce food waste. In addition, the generated production plan is displayed to the user and instructions are given to enhance the sustainability of food supply.

[0006] "Climate data" refers to information about weather and meteorological conditions in a specific region, including temperature, precipitation, and wind speed.

[0007] "Market data" refers to information about the supply and demand for goods and services, including price fluctuations and sales volume trends.

[0008] "Consumption trends" refer to predictions based on consumer purchasing behavior and preference patterns, indicating increases or decreases in demand for specific products.

[0009] A "food supply network" refers to the route by which food moves from farms and processing facilities to consumers, and includes all stages of production, distribution, and sales.

[0010] "Inventory management" refers to the process of optimizing the balance between supply and demand for goods, and includes methods for maintaining appropriate inventory levels.

[0011] A "production plan" refers to a schedule of production activities over a certain period, and includes resource allocation and adjustments to the manufacturing process.

[0012] "User" refers to anyone who uses this system to make decisions regarding food supply, and includes companies and individuals. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled 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 memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is an AI system aimed at improving the efficiency of food production and distribution. The system includes a server, terminals, and a user interface. The server functions as the central processing unit, acquiring climate and market data from external sources. Using this data, the server performs data analysis and generates predictions of consumption trends. The analysis applies advanced algorithms, enabling detailed demand forecasts for each region.

[0035] Based on the analysis results, the server plans to create an optimized food supply network. This ensures proper inventory management and reduces food waste. The server develops and updates production plans in real time, including food production volumes and distribution routes.

[0036] The terminal plays the role of providing information from the server to the user. This includes a visually displayed production plan with an intuitive interface that the user can easily understand. The terminal also supports the user in modifying the plan.

[0037] Users can make concrete decisions based on the information presented on the terminal. For example, if a user wants to adjust production volume based on food demand, they can input this information into the terminal, and the server will automatically generate a new production plan.

[0038] For example, if the supply of rice becomes unstable due to weather in a certain region, the server updates its forecast using climate data and analyzes the demand for alternative food products from market data. The server then proposes an optimized delivery route, allowing the user to manage inventory appropriately and reduce food waste.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server retrieves real-time climate data from external weather data providers. This includes ingesting data such as the latest temperature, precipitation, and wind speed for a specific region.

[0042] Step 2:

[0043] The server collects market data from domestic and international markets. This data includes price fluctuations for goods, trends in demand, and the competitive landscape of the market.

[0044] Step 3:

[0045] The server analyzes this data and uses a generative AI model to predict consumption trends. This analysis predicts how consumer demand will evolve in each region.

[0046] Step 4:

[0047] Based on the prediction results, the server develops a plan to optimize the food supply network. This includes adjusting production volumes and optimizing logistics routes.

[0048] Step 5:

[0049] The server sends an optimized production plan to the terminal for user review. The terminal visually displays this information in a dashboard format.

[0050] Step 6:

[0051] The user checks the production plan displayed on the terminal and makes adjustments as needed. At this time, the user inputs the corrected data via the terminal and sends it to the server.

[0052] Step 7:

[0053] The server receives input from the user and, if necessary, sends the updated production plan back to the terminal in real time.

[0054] Step 8:

[0055] The user approves the final plan and begins its implementation. The terminal transmits operational instructions based on the approved plan to the relevant logistics systems and production departments.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In recent years, the problems of resource waste and food waste have become increasingly serious in food production and distribution. Furthermore, the uncertainty caused by climate change is impacting production plans, necessitating the establishment of an efficient food supply system. Against this backdrop, achieving increased efficiency in food production and the creation of a sustainable supply system is an urgent priority.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring information on climate, means for acquiring information on the market, and means for predicting consumption trends. This enables the optimization of an efficient food supply system, reduces food waste, and allows for the creation of sustainable production plans.

[0061] "Means of obtaining climate-related information" refers to devices or programs that obtain meteorological data from external sources and analyze environmental conditions that affect food production.

[0062] "Means of acquiring information in the market" refers to devices or programs for collecting data on market trends and demand forecasts, and for analyzing consumer trends.

[0063] "Means for predicting consumption trends" refer to algorithms and devices that use acquired data to analyze consumer behavior patterns and estimate future demand.

[0064] "Means for optimizing the food supply system" refers to a device or program for formulating plans to streamline the entire production and distribution process, thereby reducing costs and stabilizing supply.

[0065] "Methods for performing data analysis using machine learning algorithms" refers to algorithms and methods for training models on datasets and performing predictions or pattern recognition.

[0066] "Means for formulating food supply plans" refer to processes and systems for creating efficient production and distribution plans based on collected and analyzed data.

[0067] "Means of inventory management" refer to methods and devices for monitoring the inflow and outflow of food products and maintaining the necessary quantities appropriately.

[0068] "Means for visually displaying the generated production plan and supporting user operation" refers to an interface or system that visualizes the analysis results, allowing users to easily understand and modify the plan.

[0069] This invention implements an AI system for improving the efficiency of food production and distribution. The system comprises a server, terminals, and a user interface.

[0070] The server functions as the central processing unit, first acquiring climate data from external sources. Specifically, it retrieves real-time weather data via APIs and stores it in a database. In addition, it collects market supply and demand-related data from online market information providers. These two datasets are integrated, and machine learning models are trained using software such as Python and TENSORFLOW® to predict consumption trends. This enables detailed demand forecasting by region.

[0071] Based on the analyzed data, the server uses linear programming techniques to optimize the food supply network and formulate a production plan. This production plan includes optimal production volumes and distribution routes, which are constantly updated in real time. For example, if climate data indicates that rice production will be affected, the server uses that forecast data to analyze the demand for alternative food products and create an appropriate supply plan.

[0072] The terminal serves as an interface that visually displays the production plan generated from the server to the user. The software installed on the terminal graphically represents the data, allowing the user to intuitively understand the situation. Based on the information provided by the terminal, the user can make decisions and modify the plan as needed. Using the touchscreen or mouse, the user can adjust production volumes and set new delivery routes, and any resulting changes are immediately fed back to the server.

[0073] For example, if unstable rice production is predicted in a specific region due to weather conditions, the server analyzes market data and predicts the demand for alternative food products, such as pasta or bread. Based on this information, it proposes efficient delivery routes to the user and assists with inventory management.

[0074] An example of a prompt to input into the generating AI model is, "Please show the regional consumption forecast for next month." This will allow for a more detailed understanding of regional consumption trends and utilize this information in production and distribution planning.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server uses the API of a weather information provider to retrieve real-time climate data from external sources. This input data includes temperature, precipitation, wind speed, and other parameters. The server stores this data in a database and converts it into a format usable for later analysis.

[0078] Step 2:

[0079] The server collects market data related to supply and demand from online market information providers. This data includes market prices, supply quantities, and consumer purchasing patterns. The input market data is retrieved in XML or JSON format and converted into a format suitable for analysis.

[0080] Step 3:

[0081] The server integrates climate and market data and uses machine learning algorithms to predict consumption trends. In this process, the server uses Python and TensorFlow to analyze the data and generate regional demand forecasting models. The output results are generated as predicted demand values.

[0082] Step 4:

[0083] The server uses linear programming techniques to develop an optimal food supply plan based on the generated demand forecast. Inputs include the forecasted demand and current supply chain information. The server determines the optimal production volume and distribution route while minimizing costs, and outputs the results.

[0084] Step 5:

[0085] The terminal visually presents production plan information to the user. The entered plan data is displayed in a graphical interface, making it easy for the user to understand. The terminal provides bar graphs, line graphs, and map views to help the user intuitively grasp the situation in each region.

[0086] Step 6:

[0087] Based on the information provided, users make modifications to production plans and delivery routes. Users input changes using the terminal's touch panel or keyboard. These modifications are sent to the server, where they are immediately re-analyzed and the plan is updated. The server generates a new production plan and outputs the result reflecting the changes.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Modern food supply systems face problems such as frequent food surpluses and shortages due to their inability to respond quickly to climate change and market fluctuations. Furthermore, selecting efficient logistics routes is difficult, leading to increased transportation costs and times. Additionally, there is a lack of means to provide real-time, visual information and enable rapid decision-making.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring climate data, means for acquiring market data, means for predicting consumption trends, means for optimizing the food supply network, means for suggesting the optimal delivery route, and means for providing information visually. This enables more efficient logistics and a rapid response to fluctuations in consumption.

[0093] "Climate data" refers to numerical information about the climate of a specific region, such as temperature, precipitation, and wind speed.

[0094] "Market data" refers to information related to economic activity in the market, such as consumer purchasing trends and product prices.

[0095] "Means of predicting consumption trends" refers to the process of analyzing past data to estimate future consumption patterns.

[0096] "Means of optimizing the food supply network" refers to methods for streamlining each stage of the supply chain, thereby reducing costs and shortening delivery times.

[0097] "Means of suggesting the optimal delivery route" refers to the process of calculating and presenting the most efficient delivery route based on geographical information and transportation conditions.

[0098] "Means of providing information visually" refers to methods of conveying information intuitively through a user interface via digital devices.

[0099] A system implementing this invention consists of a server for acquiring and analyzing climate data and market data, and a terminal for providing information from the server to the user.

[0100] The server obtains climate data from external weather information services and collects market data from databases and online market analysis tools. This data is analyzed using the Python programming language and the scikit-learn library to predict consumption trends. Specifically, it uses a linear regression model based on historical data to estimate future demand. Based on the results of this analysis, the server builds an optimal food supply network and calculates the most optimal delivery routes.

[0101] The terminal provides information to the user visually. The terminal is a smart glasses or other wearable device that allows the user to check production and logistics plans in real time. Furthermore, picking instructions and logistics route information are displayed intuitively based on gaze and touch gestures.

[0102] For example, if the temperature changes rapidly in a certain region, the data is immediately analyzed on the server, and the results are reflected on the terminal in real time. This allows users to instantly check the optimal production volume and delivery route, enabling them to make quick decisions. An example of a prompt message to the generated AI model might be, "Based on the received climate and market data, predict the demand trends for food products and plan the optimal supply network."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server acquires climate and market data. It obtains temperature and precipitation data from weather information services using APIs, and collects consumer purchasing pattern data from online market analysis tools. This data is stored in a database and used for subsequent analysis.

[0106] Step 2:

[0107] The server predicts consumption trends using acquired climate and market data. It applies a linear regression model from scikit-learn, generating a predictive model based on the data. Based on this, it calculates future demand numerically. The output is data representing the predicted consumption trends.

[0108] Step 3:

[0109] The server optimizes the food supply network based on predictive data. Using an optimization algorithm, it analyzes the efficiency of each stage of the supply chain and creates a plan to complete distribution at the lowest possible cost. The output is the optimized supply network plan.

[0110] Step 4:

[0111] The server then calculates the optimal delivery route. Using a Geographic Information System (GIS), it generates a route based on traffic conditions and distance information. This allows it to output the most efficient delivery route.

[0112] Step 5:

[0113] The terminal receives data transmitted from the server and presents the information to the user visually. In smart glasses, the display shows plans and route information, designed to be intuitively understandable to the user. The user reviews the information and adjusts picking and routes as needed.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention is an AI system that streamlines food production and distribution and also has the ability to recognize user emotions. The system includes a server, terminals, a user interface, and an emotion engine. The server acquires climate and market data from external sources and uses this to predict consumption trends. These predictions are used to optimize the food supply network and help in formulating production plans.

[0116] The emotion engine recognizes and analyzes user emotions in real time. Based on this emotion data, the server can adjust production plans and logistics strategies, making it possible to build an optimal food supply network that takes user stress and satisfaction into consideration.

[0117] The terminal not only provides users with visual information from the server, but also presents instructions that have been adjusted based on the analysis results of the emotion engine. This allows users to receive planning information that is best suited to their own emotional state.

[0118] Users review the plan displayed on their device and provide emotion-based feedback as needed. This feedback is then used as further data by the emotion engine, contributing to the overall improvement of the system.

[0119] For example, if a user reports high stress levels, the emotion engine instructs the server to prioritize simpler, less risky logistics plans. The server, upon receiving this instruction, generates a plan that includes risk mitigation measures while maintaining efficiency as much as possible within given constraints, and sends it to the terminal. This process enables a sustainable food supply system that incorporates emotional factors.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server retrieves climate data from external weather data providers. This involves collecting real-time updated weather information for each region.

[0123] Step 2:

[0124] The server collects market data from each market. This data includes product prices, demand trends, and sales data.

[0125] Step 3:

[0126] The server analyzes acquired climate and market data and uses a generative AI model to predict consumption trends. This prediction allows for an estimate of how future demand will change.

[0127] Step 4:

[0128] The server creates a plan to optimize the food supply network based on consumption trend forecasts. This plan includes adjusting production volumes and selecting transportation routes.

[0129] Step 5:

[0130] The server sends the plan details to the terminal for the user to review. The terminal then displays this information to the user in an easy-to-understand format.

[0131] Step 6:

[0132] The emotion engine collects and analyzes the user's real-time emotional state. This emotional data indicates the user's stress level, satisfaction level, and other factors.

[0133] Step 7:

[0134] Based on information obtained from the emotion engine, the server optimizes production operations. At this stage, adjustments to the plan may be made based on the user's emotions.

[0135] Step 8:

[0136] The device presents the user with a plan that has been adjusted based on sentiment analysis. The user can provide feedback on the presented plan.

[0137] Step 9:

[0138] User feedback is evaluated by an emotion engine, and the results are fed back to the server. The server then uses the feedback information to further improve the plan.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In recent years, as there has been a growing demand for increased efficiency in food production and distribution, the challenge has become building a flexible supply network that takes into account climate change, market uncertainties, and the emotions of individual consumers. Traditional systems have struggled to adequately consider consumer emotional factors in planning and adjustments, resulting in increased food waste and decreased customer satisfaction.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for information processing for predicting consumption trends, means for planning for optimizing the supply network, means for analysis for recognizing emotional data, and means for adjusting the plan based on emotional data. This makes it possible to build a flexible and sustainable food supply network that takes consumer emotions into consideration.

[0144] "Climate information" refers to data on current or expected weather conditions in a specific region, such as temperature, precipitation, humidity, wind direction, and wind speed.

[0145] "Market information" refers to data on various conditions in a specific market, such as supply and demand, product prices, and consumer trends.

[0146] "Consumer trends" refer to predictive data that shows changes in consumer purchasing behavior and preferences over a certain period.

[0147] A "supply network" refers to the structure of a supply chain that shows how the production, distribution, and sale of a product are organized.

[0148] "Planning means" refers to a method or apparatus for formulating the procedures and schedules necessary to achieve a specific objective.

[0149] "Emotional data" refers to information that represents the emotional state of individual consumers, and includes data related to emotions such as joy, sadness, anger, and surprise.

[0150] "Analysis means" refers to a method or apparatus for analyzing acquired data and extracting useful information or insights.

[0151] "Means of adjusting a plan" refers to methods or devices for modifying or improving an existing plan based on changing conditions or new information.

[0152] This invention aims to streamline food production and distribution and build a supply network that takes user emotions into consideration. Therefore, the system includes a server, terminals, a user interface, and an emotion recognition engine.

[0153] The server obtains climate and market information from external APIs. Specifically, it uses a weather data acquisition API for climate information and various statistical data libraries for market information. Based on this data, the server utilizes generative AI models such as TensorFlow and PyTorch to predict consumption trends. Based on the consumption trends calculated by these AI models, the server formulates and optimizes production and logistics plans.

[0154] For emotion recognition, the system acquires the user's facial expressions and voice from the device's built-in camera and microphone, and analyzes this data using OpenCV and an emotion analysis API. The user's emotions are recognized in real time and sent to the server as emotion data. The server uses this emotion data to adjust plans, taking into account the user's stress level and satisfaction level. This adjustment process also includes risk management for logistics plans and optimization of delivery routes.

[0155] The terminal visually presents information from the server to the user. The user reviews the plan through the terminal and provides emotion-based feedback. This feedback is then used as further data by the emotion recognition engine, contributing to the overall improvement of the system.

[0156] For example, when a user provides emotional feedback to the system stating "I've been feeling stressed lately," the server automatically generates a plan prioritizing risk management and delivers it to the user via their device. This generated plan provides the most appropriate information for the user's emotional state.

[0157] An example of a prompt message might be: "The user has reported experiencing high levels of stress. Please propose an optimal food supply plan that takes this into consideration."

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server retrieves climate and market information via an external API. Input requires an API key and region specification, while output consists of climate data (e.g., temperature, precipitation) and market data (e.g., supply and demand indicators). This data is imported into the server in JSON format.

[0161] Step 2:

[0162] The server uses a generative AI model based on the acquired data to predict consumption trends. Specifically, it uses TensorFlow for prediction. The inputs required are climate information and market information acquired in step 1, and the output is a predicted value of future consumption trends. This predicted value will be used in the optimization process in the next step.

[0163] Step 3:

[0164] The server optimizes production planning and supply networks. It requires the predicted consumption trends obtained in step 2 as input. Linear programming is used to develop a plan that considers supply costs and production capacity. The output is an optimized production and logistics plan.

[0165] Step 4:

[0166] The device uses a camera and microphone to capture the user's real-time facial expressions and voice. The input is this sensor data, and the output is an analysis result indicating the user's emotions. This analysis is performed using OpenCV.

[0167] Step 5:

[0168] The server analyzes the emotional data sent from the terminal to determine the user's emotional state. The input is the emotional analysis results from step 4, and the output is numerical data indicating the user's stress level and satisfaction level. This data is used by the server to readjust the plan.

[0169] Step 6:

[0170] The server takes emotional data into account and readjusts production plans and supply chains. Inputs include the optimized plan from step 3 and the emotional data from step 5. The output is a new plan that takes into account the user's emotional state and logistics risks. Specifically, it involves changing delivery routes and re-evaluating supplier options.

[0171] Step 7:

[0172] The terminal presents the user with the adjusted plan. The input is the plan from step 6, and the output is visualized information through a graphical user interface. The user then provides emotion-based feedback based on this information.

[0173] Step 8:

[0174] Users provide feedback based on the information displayed on their devices. The input is the information displayed on the device, and the output is data sent to the server as feedback. This feedback is used to improve the overall system adaptability.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] In modern society, the processes of food production and distribution are plagued by problems such as decreased efficiency and increased food waste. Furthermore, one-sided food supply, without considering the emotional state of consumers, can lead to decreased consumer satisfaction. It is necessary to address these challenges and realize a sustainable food supply system.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for predicting consumption trends, means for optimizing the food supply network, means for recognizing and analyzing the user's emotional state, and means for adjusting the food supply plan based on the user's emotional state. This enables an efficient and sustainable food supply system that takes consumer emotions into consideration.

[0180] "Means for acquiring climate information" refers to devices or software that have the function of acquiring information on current and future weather conditions from external weather databases or sensors.

[0181] "Means for acquiring market information" refers to devices or software for collecting data such as market trends, prices, and consumer purchasing tendencies from various data sources.

[0182] "Means for predicting consumption trends" refers to devices or software that have the function of predicting future consumption behavior trends using mathematical models or analytical methods based on acquired market information and climate information.

[0183] "Means for optimizing food supply networks" refers to devices or software that have the function of optimizing food production, distribution, and delivery based on predicted consumption trends, and for building an efficient supply system.

[0184] "Means for recognizing and analyzing a user's emotional state" refers to a device or software that has the function of identifying and evaluating a user's current emotional state in real time by analyzing the user's facial expressions, voice, behavioral data, etc.

[0185] "Means for adjusting the food supply plan based on the user's emotional state" refers to a device or software that utilizes the results of the user's emotional analysis to dynamically adjust and modify the food supply plan to optimize it for that emotional state.

[0186] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0187] The server connects to an external weather database to obtain climate information, acquiring current and predicted weather data for each region in real time. In parallel, it acquires market information from various data sources, including market trends and consumer purchasing tendencies. Based on this information, it uses a consumer trend prediction algorithm to forecast future consumer behavior. The hardware or software used is typically a standard server computer connected to the internet; weather APIs are commonly used to acquire climate information, and machine learning models are used to predict consumer trends.

[0188] The user's device uses its camera and microphone to capture facial expressions and voice data, and recognizes their emotional state in real time via emotion recognition software. This emotional data is sent to a server, and the food supply plan is adjusted based on the user's emotional state. This process uses emotion recognition APIs such as Microsoft® Azure® Cognitive Services. The device then visualizes the adjusted supply plan and suggests the best food options and delivery plans for the user.

[0189] Furthermore, users can provide feedback on their emotional state through their device. This feedback is further analyzed by the emotion engine and used to generate future plans. For example, if a user expresses fatigue, the system can suggest foods or smoothies that will help restore their energy.

[0190] An example of a prompt message is: "If the user's emotion is identified as 'fatigue,' prioritize suggesting products that help with recovery."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server uses a weather API to retrieve climate information from an external weather database. The input is the region's location, and based on this, it retrieves current and predicted weather data. The output is climate data for the relevant region. This data is used to predict future consumption trends.

[0194] Step 2:

[0195] The server uses a market trend API to acquire market information. The input is category information for the target product. This allows the server to collect market supply and demand balance and price fluctuation data. The output is a dataset showing market trends. This enables the consumer trend prediction algorithm to function.

[0196] Step 3:

[0197] The server runs a generative AI model that integrates climate and market information to predict consumption trends. The inputs are climate and market data from steps 1 and 2. The output is predictive data showing future consumption trends. This predictive data is used to optimize the food supply network.

[0198] Step 4:

[0199] The device uses the user's camera and microphone to acquire facial expressions and audio data. The input is the user's real-time image and audio. Based on this data, an emotion recognition API is used to analyze the user's emotions in real time. The output is the user's current emotional state.

[0200] Step 5:

[0201] The server performs a mechanism to adjust the food supply plan using the user's emotional state data. The inputs are the predicted consumption trend data obtained in step 3 and the emotional data obtained in step 4. The output is the adjusted food supply plan. This plan reflects the user's emotional state in the optimal delivery route and suggestions.

[0202] Step 6:

[0203] The user's device visually presents the adjusted food supply plan. The input is the adjusted plan from step 5. The output is the supply plan and options displayed on the user's screen. This allows the user to review the plan and provide feedback as needed.

[0204] 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.

[0205] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0206] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0211] 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.

[0212] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0213] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0214] 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.

[0215] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0216] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0217] The 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.

[0218] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0219] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0220] This invention is an AI system aimed at improving the efficiency of food production and distribution. The system includes a server, terminals, and a user interface. The server functions as the central processing unit, acquiring climate and market data from external sources. Using this data, the server performs data analysis and generates predictions of consumption trends. The analysis applies advanced algorithms, enabling detailed demand forecasts for each region.

[0221] Based on the analysis results, the server plans to create an optimized food supply network. This ensures proper inventory management and reduces food waste. The server develops and updates production plans in real time, including food production volumes and distribution routes.

[0222] The terminal plays the role of providing information from the server to the user. This includes a visually displayed production plan with an intuitive interface that the user can easily understand. The terminal also supports the user in modifying the plan.

[0223] Users can make concrete decisions based on the information presented on the terminal. For example, if a user wants to adjust production volume based on food demand, they can input this information into the terminal, and the server will automatically generate a new production plan.

[0224] For example, if the supply of rice becomes unstable due to weather in a certain region, the server updates its forecast using climate data and analyzes the demand for alternative food products from market data. The server then proposes an optimized delivery route, allowing the user to manage inventory appropriately and reduce food waste.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server retrieves real-time climate data from external weather data providers. This includes ingesting data such as the latest temperature, precipitation, and wind speed for a specific region.

[0228] Step 2:

[0229] The server collects market data from domestic and international markets. This data includes price fluctuations for goods, trends in demand, and the competitive landscape of the market.

[0230] Step 3:

[0231] The server analyzes this data and uses a generative AI model to predict consumption trends. This analysis predicts how consumer demand will evolve in each region.

[0232] Step 4:

[0233] Based on the prediction results, the server develops a plan to optimize the food supply network. This includes adjusting production volumes and optimizing logistics routes.

[0234] Step 5:

[0235] The server sends an optimized production plan to the terminal for user review. The terminal visually displays this information in a dashboard format.

[0236] Step 6:

[0237] The user checks the production plan displayed on the terminal and makes adjustments as needed. At this time, the user inputs the corrected data via the terminal and sends it to the server.

[0238] Step 7:

[0239] The server receives input from the user and, if necessary, sends the updated production plan back to the terminal in real time.

[0240] Step 8:

[0241] The user approves the final plan and begins its implementation. The terminal transmits operational instructions based on the approved plan to the relevant logistics systems and production departments.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] In recent years, the problems of resource waste and food waste have become increasingly serious in food production and distribution. Furthermore, the uncertainty caused by climate change is impacting production plans, necessitating the establishment of an efficient food supply system. Against this backdrop, achieving increased efficiency in food production and the creation of a sustainable supply system is an urgent priority.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes means for acquiring information on climate, means for acquiring information on the market, and means for predicting consumption trends. This enables the optimization of an efficient food supply system, reduces food waste, and allows for the creation of sustainable production plans.

[0247] "Means of obtaining climate-related information" refers to devices or programs that obtain meteorological data from external sources and analyze environmental conditions that affect food production.

[0248] "Means of acquiring information in the market" refers to devices or programs for collecting data on market trends and demand forecasts, and for analyzing consumer trends.

[0249] "Means for predicting consumption trends" refer to algorithms and devices that use acquired data to analyze consumer behavior patterns and estimate future demand.

[0250] "Means for optimizing the food supply system" refers to a device or program for formulating plans to streamline the entire production and distribution process, thereby reducing costs and stabilizing supply.

[0251] "Methods for performing data analysis using machine learning algorithms" refers to algorithms and methods for training models on datasets and performing predictions or pattern recognition.

[0252] "Means for formulating food supply plans" refer to processes and systems for creating efficient production and distribution plans based on collected and analyzed data.

[0253] "Means of inventory management" refer to methods and devices for monitoring the inflow and outflow of food products and maintaining the necessary quantities appropriately.

[0254] "Means for visually displaying the generated production plan and supporting user operation" refers to an interface or system that visualizes the analysis results, allowing users to easily understand and modify the plan.

[0255] This invention implements an AI system for improving the efficiency of food production and distribution. The system comprises a server, terminals, and a user interface.

[0256] The server functions as the central processing unit, first acquiring climate data from external sources. Specifically, it retrieves real-time weather data via APIs and stores it in a database. In addition, it collects market supply and demand-related data from online market information providers. These two datasets are integrated, and machine learning models are trained using software such as Python and TensorFlow to perform consumption trend forecasts. This enables detailed demand forecasts by region.

[0257] Based on the analyzed data, the server uses linear programming techniques to optimize the food supply network and formulate a production plan. This production plan includes optimal production volumes and distribution routes, which are constantly updated in real time. For example, if climate data indicates that rice production will be affected, the server uses that forecast data to analyze the demand for alternative food products and create an appropriate supply plan.

[0258] The terminal serves as an interface that visually displays the production plan generated from the server to the user. The software installed on the terminal graphically represents the data, allowing the user to intuitively understand the situation. Based on the information provided by the terminal, the user can make decisions and modify the plan as needed. Using the touchscreen or mouse, the user can adjust production volumes and set new delivery routes, and any resulting changes are immediately fed back to the server.

[0259] For example, if unstable rice production is predicted in a specific region due to weather conditions, the server analyzes market data and predicts the demand for alternative food products, such as pasta or bread. Based on this information, it proposes efficient delivery routes to the user and assists with inventory management.

[0260] An example of a prompt to input into the generating AI model is, "Please show the regional consumption forecast for next month." This will allow for a more detailed understanding of regional consumption trends and utilize this information in production and distribution planning.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server uses the API of a weather information provider to retrieve real-time climate data from external sources. This input data includes temperature, precipitation, wind speed, and other parameters. The server stores this data in a database and converts it into a format usable for later analysis.

[0264] Step 2:

[0265] The server collects market data related to supply and demand from online market information providers. This data includes market prices, supply quantities, and consumer purchasing patterns. The input market data is retrieved in XML or JSON format and converted into a format suitable for analysis.

[0266] Step 3:

[0267] The server integrates climate and market data and uses machine learning algorithms to predict consumption trends. In this process, the server uses Python and TensorFlow to analyze the data and generate regional demand forecasting models. The output results are generated as predicted demand values.

[0268] Step 4:

[0269] The server uses linear programming techniques to develop an optimal food supply plan based on the generated demand forecast. Inputs include the forecasted demand and current supply chain information. The server determines the optimal production volume and distribution route while minimizing costs, and outputs the results.

[0270] Step 5:

[0271] The terminal visually presents production plan information to the user. The entered plan data is displayed in a graphical interface, making it easy for the user to understand. The terminal provides bar graphs, line graphs, and map views to help the user intuitively grasp the situation in each region.

[0272] Step 6:

[0273] Based on the information provided, users make modifications to production plans and delivery routes. Users input changes using the terminal's touch panel or keyboard. These modifications are sent to the server, where they are immediately re-analyzed and the plan is updated. The server generates a new production plan and outputs the result reflecting the changes.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] Modern food supply systems face problems such as frequent food surpluses and shortages due to their inability to respond quickly to climate change and market fluctuations. Furthermore, selecting efficient logistics routes is difficult, leading to increased transportation costs and times. Additionally, there is a lack of means to provide real-time, visual information and enable rapid decision-making.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for acquiring climate data, means for acquiring market data, means for predicting consumption trends, means for optimizing the food supply network, means for suggesting the optimal delivery route, and means for providing information visually. This enables more efficient logistics and a rapid response to fluctuations in consumption.

[0279] "Climate data" refers to numerical information about the climate of a specific region, such as temperature, precipitation, and wind speed.

[0280] "Market data" refers to information related to economic activity in the market, such as consumer purchasing trends and product prices.

[0281] "Means of predicting consumption trends" refers to the process of analyzing past data to estimate future consumption patterns.

[0282] "Means of optimizing the food supply network" refers to methods for streamlining each stage of the supply chain, thereby reducing costs and shortening delivery times.

[0283] The "means for presenting the optimal delivery route" refers to the process of calculating and presenting the most efficient delivery route based on geographical information and transportation conditions.

[0284] The "means for visually providing information" refers to the method of intuitively conveying information through a digital device via a user interface.

[0285] The system for implementing this invention consists of a server for acquiring and analyzing climate data and market data, and a terminal for providing information from the server to the user.

[0286] The server acquires climate data from an external weather information service and collects market data from a database or an online market analysis tool. These data are analyzed using the Python programming language and the scikit-learn library to predict consumption trends. Specifically, a linear regression model is used to infer future demand based on past data. Based on the analysis results, the server constructs an optimal food supply network and further calculates the optimal delivery route.

[0287] The terminal visually provides information to the user. The terminal is smart glasses or other wearable devices, and the user can check production plans and logistics plans in real time. Also, picking instructions and logistics route information are intuitively displayed based on eye gaze and touch gestures.

[0288] For example, when the temperature changes rapidly in a certain area, the data is immediately analyzed by the server and the results are reflected in the terminal in real time. As a result, the user can immediately check the optimal production volume and delivery route and make a quick decision. An example of a prompt sentence for the generative AI model would be in the form of "Based on the received climate data and market data, predict the demand trend for food products and plan an optimal supply network."

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The server acquires climate and market data. It obtains temperature and precipitation data from weather information services using APIs, and collects consumer purchasing pattern data from online market analysis tools. This data is stored in a database and used for subsequent analysis.

[0292] Step 2:

[0293] The server predicts consumption trends using acquired climate and market data. It applies a linear regression model from scikit-learn, generating a predictive model based on the data. Based on this, it calculates future demand numerically. The output is data representing the predicted consumption trends.

[0294] Step 3:

[0295] The server optimizes the food supply network based on predictive data. Using an optimization algorithm, it analyzes the efficiency of each stage of the supply chain and creates a plan to complete distribution at the lowest possible cost. The output is the optimized supply network plan.

[0296] Step 4:

[0297] The server then calculates the optimal delivery route. Using a Geographic Information System (GIS), it generates a route based on traffic conditions and distance information. This allows it to output the most efficient delivery route.

[0298] Step 5:

[0299] The terminal receives data transmitted from the server and presents the information to the user visually. In smart glasses, the display shows plans and route information, designed to be intuitively understandable to the user. The user reviews the information and adjusts picking and routes as needed.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention is an AI system that streamlines food production and distribution and also has the ability to recognize user emotions. The system includes a server, terminals, a user interface, and an emotion engine. The server acquires climate and market data from external sources and uses this to predict consumption trends. These predictions are used to optimize the food supply network and help in formulating production plans.

[0302] The emotion engine recognizes and analyzes user emotions in real time. Based on this emotion data, the server can adjust production plans and logistics strategies, making it possible to build an optimal food supply network that takes user stress and satisfaction into consideration.

[0303] The terminal not only provides users with visual information from the server, but also presents instructions that have been adjusted based on the analysis results of the emotion engine. This allows users to receive planning information that is best suited to their own emotional state.

[0304] Users review the plan displayed on their device and provide emotion-based feedback as needed. This feedback is then used as further data by the emotion engine, contributing to the overall improvement of the system.

[0305] For example, if a user reports high stress levels, the emotion engine instructs the server to prioritize simpler, less risky logistics plans. The server, upon receiving this instruction, generates a plan that includes risk mitigation measures while maintaining efficiency as much as possible within given constraints, and sends it to the terminal. This process enables a sustainable food supply system that incorporates emotional factors.

[0306] The following describes the process flow.

[0307] Step 1:

[0308] The server acquires climate data from an external weather data providing service. This is an action to collect weather information of each region updated in real time.

[0309] Step 2:

[0310] The server collects market data from each market. This data includes product prices, demand trends, and sales data.

[0311] Step 3:

[0312] The server analyzes the acquired climate data and market data, and predicts consumption trends using the generated AI model. This prediction can estimate how future demand will change.

[0313] Step 4:

[0314] Based on the prediction of consumption trends, the server creates a plan to optimize the food supply network. The plan includes adjustments to production volume and selection of transportation routes.

[0315] Step 5:

[0316] The server sends the plan details to the terminal so that the user can view them. The terminal displays this information to the user in an easy-to-understand format.

[0317] Step 6:

[0318] The emotion engine collects and analyzes the user's real-time emotional state. The emotion data indicates the user's stress level, satisfaction, etc.

[0319] Step 7:

[0320] Based on information obtained from the emotion engine, the server optimizes production operations. At this stage, adjustments to the plan may be made based on the user's emotions.

[0321] Step 8:

[0322] The device presents the user with a plan that has been adjusted based on sentiment analysis. The user can provide feedback on the presented plan.

[0323] Step 9:

[0324] User feedback is evaluated by an emotion engine, and the results are fed back to the server. The server then uses the feedback information to further improve the plan.

[0325] (Example 2)

[0326] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0327] In recent years, as there has been a growing demand for increased efficiency in food production and distribution, the challenge has become building a flexible supply network that takes into account climate change, market uncertainties, and the emotions of individual consumers. Traditional systems have struggled to adequately consider consumer emotional factors in planning and adjustments, resulting in increased food waste and decreased customer satisfaction.

[0328] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0329] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for information processing for predicting consumption trends, means for planning for optimizing the supply network, means for analysis for recognizing emotional data, and means for adjusting the plan based on emotional data. This makes it possible to build a flexible and sustainable food supply network that takes consumer emotions into consideration.

[0330] "Climate information" refers to data on current or expected weather conditions in a specific region, such as temperature, precipitation, humidity, wind direction, and wind speed.

[0331] "Market information" refers to data on various conditions in a specific market, such as supply and demand, product prices, and consumer trends.

[0332] "Consumer trends" refer to predictive data that shows changes in consumer purchasing behavior and preferences over a certain period.

[0333] A "supply network" refers to the structure of a supply chain that shows how the production, distribution, and sale of a product are organized.

[0334] "Planning means" refers to a method or apparatus for formulating the procedures and schedules necessary to achieve a specific objective.

[0335] "Emotional data" refers to information that represents the emotional state of individual consumers, and includes data related to emotions such as joy, sadness, anger, and surprise.

[0336] "Analysis means" refers to a method or apparatus for analyzing acquired data and extracting useful information or insights.

[0337] "Means of adjusting a plan" refers to methods or devices for modifying or improving an existing plan based on changing conditions or new information.

[0338] This invention aims to streamline food production and distribution and build a supply network that takes user emotions into consideration. Therefore, the system includes a server, terminals, a user interface, and an emotion recognition engine.

[0339] The server obtains climate and market information from external APIs. Specifically, it uses a weather data acquisition API for climate information and various statistical data libraries for market information. Based on this data, the server utilizes generative AI models such as TensorFlow and PyTorch to predict consumption trends. Based on the consumption trends calculated by these AI models, the server formulates and optimizes production and logistics plans.

[0340] For emotion recognition, the system acquires the user's facial expressions and voice from the device's built-in camera and microphone, and analyzes this data using OpenCV and an emotion analysis API. The user's emotions are recognized in real time and sent to the server as emotion data. The server uses this emotion data to adjust plans, taking into account the user's stress level and satisfaction level. This adjustment process also includes risk management for logistics plans and optimization of delivery routes.

[0341] The terminal visually presents information from the server to the user. The user reviews the plan through the terminal and provides emotion-based feedback. This feedback is then used as further data by the emotion recognition engine, contributing to the overall improvement of the system.

[0342] For example, when a user provides emotional feedback to the system stating "I've been feeling stressed lately," the server automatically generates a plan prioritizing risk management and delivers it to the user via their device. This generated plan provides the most appropriate information for the user's emotional state.

[0343] An example of a prompt message might be: "The user has reported experiencing high levels of stress. Please propose an optimal food supply plan that takes this into consideration."

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The server retrieves climate and market information via an external API. Input requires an API key and region specification, while output consists of climate data (e.g., temperature, precipitation) and market data (e.g., supply and demand indicators). This data is imported into the server in JSON format.

[0347] Step 2:

[0348] The server uses a generative AI model based on the acquired data to predict consumption trends. Specifically, it uses TensorFlow for prediction. The inputs required are climate information and market information acquired in step 1, and the output is a predicted value of future consumption trends. This predicted value will be used in the optimization process in the next step.

[0349] Step 3:

[0350] The server optimizes production planning and supply networks. It requires the predicted consumption trends obtained in step 2 as input. Linear programming is used to develop a plan that considers supply costs and production capacity. The output is an optimized production and logistics plan.

[0351] Step 4:

[0352] The device uses a camera and microphone to capture the user's real-time facial expressions and voice. The input is this sensor data, and the output is an analysis result indicating the user's emotions. This analysis is performed using OpenCV.

[0353] Step 5:

[0354] The server analyzes the emotional data sent from the terminal to determine the user's emotional state. The input is the emotional analysis results from step 4, and the output is numerical data indicating the user's stress level and satisfaction level. This data is used by the server to readjust the plan.

[0355] Step 6:

[0356] The server takes emotional data into account and readjusts production plans and supply chains. Inputs include the optimized plan from step 3 and the emotional data from step 5. The output is a new plan that takes into account the user's emotional state and logistics risks. Specifically, it involves changing delivery routes and re-evaluating supplier options.

[0357] Step 7:

[0358] The terminal presents the user with the adjusted plan. The input is the plan from step 6, and the output is visualized information through a graphical user interface. The user then provides emotion-based feedback based on this information.

[0359] Step 8:

[0360] Users provide feedback based on the information displayed on their devices. The input is the information displayed on the device, and the output is data sent to the server as feedback. This feedback is used to improve the overall system adaptability.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] In modern society, the processes of food production and distribution are plagued by problems such as decreased efficiency and increased food waste. Furthermore, one-sided food supply, without considering the emotional state of consumers, can lead to decreased consumer satisfaction. It is necessary to address these challenges and realize a sustainable food supply system.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for predicting consumption trends, means for optimizing the food supply network, means for recognizing and analyzing the user's emotional state, and means for adjusting the food supply plan based on the user's emotional state. This enables an efficient and sustainable food supply system that takes consumer emotions into consideration.

[0366] "Means for acquiring climate information" refers to devices or software that have the function of acquiring information on current and future weather conditions from external weather databases or sensors.

[0367] "Means for acquiring market information" refers to devices or software for collecting data such as market trends, prices, and consumer purchasing tendencies from various data sources.

[0368] "Means for predicting consumption trends" refers to devices or software that have the function of predicting future consumption behavior trends using mathematical models or analytical methods based on acquired market information and climate information.

[0369] "Means for optimizing food supply networks" refers to devices or software that have the function of optimizing food production, distribution, and delivery based on predicted consumption trends, and for building an efficient supply system.

[0370] "Means for recognizing and analyzing a user's emotional state" refers to a device or software that has the function of identifying and evaluating a user's current emotional state in real time by analyzing the user's facial expressions, voice, behavioral data, etc.

[0371] "Means for adjusting the food supply plan based on the user's emotional state" refers to a device or software that utilizes the results of the user's emotional analysis to dynamically adjust and modify the food supply plan to optimize it for that emotional state.

[0372] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0373] The server connects to an external weather database to obtain climate information, acquiring current and predicted weather data for each region in real time. In parallel, it acquires market information from various data sources, including market trends and consumer purchasing tendencies. Based on this information, it uses a consumer trend prediction algorithm to forecast future consumer behavior. The hardware or software used is typically a standard server computer connected to the internet; weather APIs are commonly used to acquire climate information, and machine learning models are used to predict consumer trends.

[0374] The user's device uses its camera and microphone to capture facial expressions and voice data, and recognizes their emotional state in real time via emotion recognition software. This emotional data is sent to a server, and the food supply plan is adjusted based on the user's emotional state. This process uses emotion recognition APIs such as Microsoft Azure Cognitive Services. The device then visualizes the adjusted supply plan and suggests the best food options and delivery plans for the user.

[0375] Furthermore, users can provide feedback on their emotional state through their device. This feedback is further analyzed by the emotion engine and used to generate future plans. For example, if a user expresses fatigue, the system can suggest foods or smoothies that will help restore their energy.

[0376] An example of a prompt message is: "If the user's emotion is identified as 'fatigue,' prioritize suggesting products that help with recovery."

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The server uses a weather API to retrieve climate information from an external weather database. The input is the region's location, and based on this, it retrieves current and predicted weather data. The output is climate data for the relevant region. This data is used to predict future consumption trends.

[0380] Step 2:

[0381] The server uses a market trend API to acquire market information. The input is category information for the target product. This allows the server to collect market supply and demand balance and price fluctuation data. The output is a dataset showing market trends. This enables the consumer trend prediction algorithm to function.

[0382] Step 3:

[0383] The server runs a generative AI model that integrates climate and market information to predict consumption trends. The inputs are climate and market data from steps 1 and 2. The output is predictive data showing future consumption trends. This predictive data is used to optimize the food supply network.

[0384] Step 4:

[0385] The device uses the user's camera and microphone to acquire facial expressions and audio data. The input is the user's real-time image and audio. Based on this data, an emotion recognition API is used to analyze the user's emotions in real time. The output is the user's current emotional state.

[0386] Step 5:

[0387] The server performs a mechanism to adjust the food supply plan using the user's emotional state data. The inputs are the predicted consumption trend data obtained in step 3 and the emotional data obtained in step 4. The output is the adjusted food supply plan. This plan reflects the user's emotional state in the optimal delivery route and suggestions.

[0388] Step 6:

[0389] The user's device visually presents the adjusted food supply plan. The input is the adjusted plan from step 5. The output is the supply plan and options displayed on the user's screen. This allows the user to review the plan and provide feedback as needed.

[0390] 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.

[0391] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0397] 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.

[0398] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0399] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0400] 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.

[0401] 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.

[0402] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0403] The 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.

[0404] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0405] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0406] This invention is an AI system aimed at improving the efficiency of food production and distribution. The system includes a server, terminals, and a user interface. The server functions as the central processing unit, acquiring climate and market data from external sources. Using this data, the server performs data analysis and generates predictions of consumption trends. The analysis applies advanced algorithms, enabling detailed demand forecasts for each region.

[0407] Based on the analysis results, the server plans to create an optimized food supply network. This ensures proper inventory management and reduces food waste. The server develops and updates production plans in real time, including food production volumes and distribution routes.

[0408] The terminal plays the role of providing information from the server to the user. This includes a visually displayed production plan with an intuitive interface that the user can easily understand. The terminal also supports the user in modifying the plan.

[0409] Users can make concrete decisions based on the information presented on the terminal. For example, if a user wants to adjust production volume based on food demand, they can input this information into the terminal, and the server will automatically generate a new production plan.

[0410] For example, if the supply of rice becomes unstable due to weather in a certain region, the server updates its forecast using climate data and analyzes the demand for alternative food products from market data. The server then proposes an optimized delivery route, allowing the user to manage inventory appropriately and reduce food waste.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The server retrieves real-time climate data from external weather data providers. This includes ingesting data such as the latest temperature, precipitation, and wind speed for a specific region.

[0414] Step 2:

[0415] The server collects market data from domestic and international markets. This data includes price fluctuations for goods, trends in demand, and the competitive landscape of the market.

[0416] Step 3:

[0417] The server analyzes this data and uses a generative AI model to predict consumption trends. This analysis predicts how consumer demand will evolve in each region.

[0418] Step 4:

[0419] Based on the prediction results, the server develops a plan to optimize the food supply network. This includes adjusting production volumes and optimizing logistics routes.

[0420] Step 5:

[0421] The server sends an optimized production plan to the terminal for user review. The terminal visually displays this information in a dashboard format.

[0422] Step 6:

[0423] The user checks the production plan displayed on the terminal and makes adjustments as needed. At this time, the user inputs the corrected data via the terminal and sends it to the server.

[0424] Step 7:

[0425] The server receives input from the user and, if necessary, sends the updated production plan back to the terminal in real time.

[0426] Step 8:

[0427] The user approves the final plan and begins its implementation. The terminal transmits operational instructions based on the approved plan to the relevant logistics systems and production departments.

[0428] (Example 1)

[0429] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0430] In recent years, the problems of resource waste and food waste have become increasingly serious in food production and distribution. Furthermore, the uncertainty caused by climate change is impacting production plans, necessitating the establishment of an efficient food supply system. Against this backdrop, achieving increased efficiency in food production and the creation of a sustainable supply system is an urgent priority.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes means for acquiring information on climate, means for acquiring information on the market, and means for predicting consumption trends. This enables the optimization of an efficient food supply system, reduces food waste, and allows for the creation of sustainable production plans.

[0433] "Means of obtaining climate-related information" refers to devices or programs that obtain meteorological data from external sources and analyze environmental conditions that affect food production.

[0434] "Means of acquiring information in the market" refers to devices or programs for collecting data on market trends and demand forecasts, and for analyzing consumer trends.

[0435] "Means for predicting consumption trends" refer to algorithms and devices that use acquired data to analyze consumer behavior patterns and estimate future demand.

[0436] "Means for optimizing the food supply system" refers to a device or program for formulating plans to streamline the entire production and distribution process, thereby reducing costs and stabilizing supply.

[0437] "Methods for performing data analysis using machine learning algorithms" refers to algorithms and methods for training models on datasets and performing predictions or pattern recognition.

[0438] "Means for formulating food supply plans" refer to processes and systems for creating efficient production and distribution plans based on collected and analyzed data.

[0439] "Means of inventory management" refer to methods and devices for monitoring the inflow and outflow of food products and maintaining the necessary quantities appropriately.

[0440] "Means for visually displaying the generated production plan and supporting user operation" refers to an interface or system that visualizes the analysis results, allowing users to easily understand and modify the plan.

[0441] This invention implements an AI system for improving the efficiency of food production and distribution. The system comprises a server, terminals, and a user interface.

[0442] The server functions as the central processing unit, first acquiring climate data from external sources. Specifically, it retrieves real-time weather data via APIs and stores it in a database. In addition, it collects market supply and demand-related data from online market information providers. These two datasets are integrated, and machine learning models are trained using software such as Python and TensorFlow to perform consumption trend forecasts. This enables detailed demand forecasts by region.

[0443] Based on the analyzed data, the server uses linear programming techniques to optimize the food supply network and formulate a production plan. This production plan includes optimal production volumes and distribution routes, which are constantly updated in real time. For example, if climate data indicates that rice production will be affected, the server uses that forecast data to analyze the demand for alternative food products and create an appropriate supply plan.

[0444] The terminal serves as an interface that visually displays the production plan generated from the server to the user. The software installed on the terminal graphically represents the data, allowing the user to intuitively understand the situation. Based on the information provided by the terminal, the user can make decisions and modify the plan as needed. Using the touchscreen or mouse, the user can adjust production volumes and set new delivery routes, and any resulting changes are immediately fed back to the server.

[0445] For example, if unstable rice production is predicted in a specific region due to weather conditions, the server analyzes market data and predicts the demand for alternative food products, such as pasta or bread. Based on this information, it proposes efficient delivery routes to the user and assists with inventory management.

[0446] An example of a prompt to input into the generating AI model is, "Please show the regional consumption forecast for next month." This will allow for a more detailed understanding of regional consumption trends and utilize this information in production and distribution planning.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The server uses the API of a weather information provider to retrieve real-time climate data from external sources. This input data includes temperature, precipitation, wind speed, and other parameters. The server stores this data in a database and converts it into a format usable for later analysis.

[0450] Step 2:

[0451] The server collects market data related to supply and demand from online market information providers. This data includes market prices, supply quantities, and consumer purchasing patterns. The input market data is retrieved in XML or JSON format and converted into a format suitable for analysis.

[0452] Step 3:

[0453] The server integrates climate and market data and uses machine learning algorithms to predict consumption trends. In this process, the server uses Python and TensorFlow to analyze the data and generate regional demand forecasting models. The output results are generated as predicted demand values.

[0454] Step 4:

[0455] The server uses linear programming techniques to develop an optimal food supply plan based on the generated demand forecast. Inputs include the forecasted demand and current supply chain information. The server determines the optimal production volume and distribution route while minimizing costs, and outputs the results.

[0456] Step 5:

[0457] The terminal visually presents production plan information to the user. The entered plan data is displayed in a graphical interface, making it easy for the user to understand. The terminal provides bar graphs, line graphs, and map views to help the user intuitively grasp the situation in each region.

[0458] Step 6:

[0459] Based on the information provided, users make modifications to production plans and delivery routes. Users input changes using the terminal's touch panel or keyboard. These modifications are sent to the server, where they are immediately re-analyzed and the plan is updated. The server generates a new production plan and outputs the result reflecting the changes.

[0460] (Application Example 1)

[0461] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0462] Modern food supply systems face problems such as frequent food surpluses and shortages due to their inability to respond quickly to climate change and market fluctuations. Furthermore, selecting efficient logistics routes is difficult, leading to increased transportation costs and times. Additionally, there is a lack of means to provide real-time, visual information and enable rapid decision-making.

[0463] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0464] In this invention, the server includes means for acquiring climate data, means for acquiring market data, means for predicting consumption trends, means for optimizing the food supply network, means for suggesting the optimal delivery route, and means for providing information visually. This enables more efficient logistics and a rapid response to fluctuations in consumption.

[0465] "Climate data" refers to numerical information about the climate of a specific region, such as temperature, precipitation, and wind speed.

[0466] "Market data" refers to information related to economic activity in the market, such as consumer purchasing trends and product prices.

[0467] "Means of predicting consumption trends" refers to the process of analyzing past data to estimate future consumption patterns.

[0468] "Means of optimizing the food supply network" refers to methods for streamlining each stage of the supply chain, thereby reducing costs and shortening delivery times.

[0469] "Means of suggesting the optimal delivery route" refers to the process of calculating and presenting the most efficient delivery route based on geographical information and transportation conditions.

[0470] "Means of providing information visually" refers to methods of conveying information intuitively through a user interface via digital devices.

[0471] A system implementing this invention consists of a server for acquiring and analyzing climate data and market data, and a terminal for providing information from the server to the user.

[0472] The server obtains climate data from external weather information services and collects market data from databases and online market analysis tools. This data is analyzed using the Python programming language and the scikit-learn library to predict consumption trends. Specifically, it uses a linear regression model based on historical data to estimate future demand. Based on the results of this analysis, the server builds an optimal food supply network and calculates the most optimal delivery routes.

[0473] The terminal provides information to the user visually. The terminal is a smart glasses or other wearable device that allows the user to check production and logistics plans in real time. Furthermore, picking instructions and logistics route information are displayed intuitively based on gaze and touch gestures.

[0474] For example, if the temperature changes rapidly in a certain region, the data is immediately analyzed on the server, and the results are reflected on the terminal in real time. This allows users to instantly check the optimal production volume and delivery route, enabling them to make quick decisions. An example of a prompt message to the generated AI model might be, "Based on the received climate and market data, predict the demand trends for food products and plan the optimal supply network."

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The server acquires climate and market data. It obtains temperature and precipitation data from weather information services using APIs, and collects consumer purchasing pattern data from online market analysis tools. This data is stored in a database and used for subsequent analysis.

[0478] Step 2:

[0479] The server predicts consumption trends using acquired climate and market data. It applies a linear regression model from scikit-learn, generating a predictive model based on the data. Based on this, it calculates future demand numerically. The output is data representing the predicted consumption trends.

[0480] Step 3:

[0481] The server optimizes the food supply network based on predictive data. Using an optimization algorithm, it analyzes the efficiency of each stage of the supply chain and creates a plan to complete distribution at the lowest possible cost. The output is the optimized supply network plan.

[0482] Step 4:

[0483] The server then calculates the optimal delivery route. Using a Geographic Information System (GIS), it generates a route based on traffic conditions and distance information. This allows it to output the most efficient delivery route.

[0484] Step 5:

[0485] The terminal receives data transmitted from the server and presents the information to the user visually. In smart glasses, the display shows plans and route information, designed to be intuitively understandable to the user. The user reviews the information and adjusts picking and routes as needed.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] This invention is an AI system that streamlines food production and distribution and also has the ability to recognize user emotions. The system includes a server, terminals, a user interface, and an emotion engine. The server acquires climate and market data from external sources and uses this to predict consumption trends. These predictions are used to optimize the food supply network and help in formulating production plans.

[0488] The emotion engine recognizes and analyzes user emotions in real time. Based on this emotion data, the server can adjust production plans and logistics strategies, making it possible to build an optimal food supply network that takes user stress and satisfaction into consideration.

[0489] The terminal not only provides users with visual information from the server, but also presents instructions that have been adjusted based on the analysis results of the emotion engine. This allows users to receive planning information that is best suited to their own emotional state.

[0490] Users review the plan displayed on their device and provide emotion-based feedback as needed. This feedback is then used as further data by the emotion engine, contributing to the overall improvement of the system.

[0491] For example, if a user reports high stress levels, the emotion engine instructs the server to prioritize simpler, less risky logistics plans. The server, upon receiving this instruction, generates a plan that includes risk mitigation measures while maintaining efficiency as much as possible within given constraints, and sends it to the terminal. This process enables a sustainable food supply system that incorporates emotional factors.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The server retrieves climate data from external weather data providers. This involves collecting real-time updated weather information for each region.

[0495] Step 2:

[0496] The server collects market data from each market. This data includes product prices, demand trends, and sales data.

[0497] Step 3:

[0498] The server analyzes acquired climate and market data and uses a generative AI model to predict consumption trends. This prediction allows for an estimate of how future demand will change.

[0499] Step 4:

[0500] The server creates a plan to optimize the food supply network based on consumption trend forecasts. This plan includes adjusting production volumes and selecting transportation routes.

[0501] Step 5:

[0502] The server sends the plan details to the terminal for the user to review. The terminal then displays this information to the user in an easy-to-understand format.

[0503] Step 6:

[0504] The emotion engine collects and analyzes the user's real-time emotional state. This emotional data indicates the user's stress level, satisfaction level, and other factors.

[0505] Step 7:

[0506] Based on information obtained from the emotion engine, the server optimizes production operations. At this stage, adjustments to the plan may be made based on the user's emotions.

[0507] Step 8:

[0508] The device presents the user with a plan that has been adjusted based on sentiment analysis. The user can provide feedback on the presented plan.

[0509] Step 9:

[0510] User feedback is evaluated by an emotion engine, and the results are fed back to the server. The server then uses the feedback information to further improve the plan.

[0511] (Example 2)

[0512] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] In recent years, as there has been a growing demand for increased efficiency in food production and distribution, the challenge has become building a flexible supply network that takes into account climate change, market uncertainties, and the emotions of individual consumers. Traditional systems have struggled to adequately consider consumer emotional factors in planning and adjustments, resulting in increased food waste and decreased customer satisfaction.

[0514] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0515] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for information processing for predicting consumption trends, means for planning for optimizing the supply network, means for analysis for recognizing emotional data, and means for adjusting the plan based on emotional data. This makes it possible to build a flexible and sustainable food supply network that takes consumer emotions into consideration.

[0516] "Climate information" refers to data on current or expected weather conditions in a specific region, such as temperature, precipitation, humidity, wind direction, and wind speed.

[0517] "Market information" refers to data on various conditions in a specific market, such as supply and demand, product prices, and consumer trends.

[0518] "Consumer trends" refer to predictive data that shows changes in consumer purchasing behavior and preferences over a certain period.

[0519] A "supply network" refers to the structure of a supply chain that shows how the production, distribution, and sale of a product are organized.

[0520] "Planning means" refers to a method or apparatus for formulating the procedures and schedules necessary to achieve a specific objective.

[0521] "Emotional data" refers to information that represents the emotional state of individual consumers, and includes data related to emotions such as joy, sadness, anger, and surprise.

[0522] "Analysis means" refers to a method or apparatus for analyzing acquired data and extracting useful information or insights.

[0523] "Means of adjusting a plan" refers to methods or devices for modifying or improving an existing plan based on changing conditions or new information.

[0524] This invention aims to streamline food production and distribution and build a supply network that takes user emotions into consideration. Therefore, the system includes a server, terminals, a user interface, and an emotion recognition engine.

[0525] The server obtains climate and market information from external APIs. Specifically, it uses a weather data acquisition API for climate information and various statistical data libraries for market information. Based on this data, the server utilizes generative AI models such as TensorFlow and PyTorch to predict consumption trends. Based on the consumption trends calculated by these AI models, the server formulates and optimizes production and logistics plans.

[0526] For emotion recognition, the system acquires the user's facial expressions and voice from the device's built-in camera and microphone, and analyzes this data using OpenCV and an emotion analysis API. The user's emotions are recognized in real time and sent to the server as emotion data. The server uses this emotion data to adjust plans, taking into account the user's stress level and satisfaction level. This adjustment process also includes risk management for logistics plans and optimization of delivery routes.

[0527] The terminal visually presents information from the server to the user. The user reviews the plan through the terminal and provides emotion-based feedback. This feedback is then used as further data by the emotion recognition engine, contributing to the overall improvement of the system.

[0528] For example, when a user provides emotional feedback to the system stating "I've been feeling stressed lately," the server automatically generates a plan prioritizing risk management and delivers it to the user via their device. This generated plan provides the most appropriate information for the user's emotional state.

[0529] An example of a prompt message might be: "The user has reported experiencing high levels of stress. Please propose an optimal food supply plan that takes this into consideration."

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The server retrieves climate and market information via an external API. Input requires an API key and region specification, while output consists of climate data (e.g., temperature, precipitation) and market data (e.g., supply and demand indicators). This data is imported into the server in JSON format.

[0533] Step 2:

[0534] The server uses a generative AI model based on the acquired data to predict consumption trends. Specifically, it uses TensorFlow for prediction. The inputs required are climate information and market information acquired in step 1, and the output is a predicted value of future consumption trends. This predicted value will be used in the optimization process in the next step.

[0535] Step 3:

[0536] The server optimizes production planning and supply networks. It requires the predicted consumption trends obtained in step 2 as input. Linear programming is used to develop a plan that considers supply costs and production capacity. The output is an optimized production and logistics plan.

[0537] Step 4:

[0538] The device uses a camera and microphone to capture the user's real-time facial expressions and voice. The input is this sensor data, and the output is an analysis result indicating the user's emotions. This analysis is performed using OpenCV.

[0539] Step 5:

[0540] The server analyzes the emotional data sent from the terminal to determine the user's emotional state. The input is the emotional analysis results from step 4, and the output is numerical data indicating the user's stress level and satisfaction level. This data is used by the server to readjust the plan.

[0541] Step 6:

[0542] The server takes emotional data into account and readjusts production plans and supply chains. Inputs include the optimized plan from step 3 and the emotional data from step 5. The output is a new plan that takes into account the user's emotional state and logistics risks. Specifically, it involves changing delivery routes and re-evaluating supplier options.

[0543] Step 7:

[0544] The terminal presents the user with the adjusted plan. The input is the plan from step 6, and the output is visualized information through a graphical user interface. The user then provides emotion-based feedback based on this information.

[0545] Step 8:

[0546] Users provide feedback based on the information displayed on their devices. The input is the information displayed on the device, and the output is data sent to the server as feedback. This feedback is used to improve the overall system adaptability.

[0547] (Application Example 2)

[0548] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] In modern society, the processes of food production and distribution are plagued by problems such as decreased efficiency and increased food waste. Furthermore, one-sided food supply, without considering the emotional state of consumers, can lead to decreased consumer satisfaction. It is necessary to address these challenges and realize a sustainable food supply system.

[0550] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0551] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for predicting consumption trends, means for optimizing the food supply network, means for recognizing and analyzing the user's emotional state, and means for adjusting the food supply plan based on the user's emotional state. This enables an efficient and sustainable food supply system that takes consumer emotions into consideration.

[0552] "Means for acquiring climate information" refers to devices or software that have the function of acquiring information on current and future weather conditions from external weather databases or sensors.

[0553] "Means for acquiring market information" refers to devices or software for collecting data such as market trends, prices, and consumer purchasing tendencies from various data sources.

[0554] "Means for predicting consumption trends" refers to devices or software that have the function of predicting future consumption behavior trends using mathematical models or analytical methods based on acquired market information and climate information.

[0555] "Means for optimizing food supply networks" refers to devices or software that have the function of optimizing food production, distribution, and delivery based on predicted consumption trends, and for building an efficient supply system.

[0556] "Means for recognizing and analyzing a user's emotional state" refers to a device or software that has the function of identifying and evaluating a user's current emotional state in real time by analyzing the user's facial expressions, voice, behavioral data, etc.

[0557] "Means for adjusting the food supply plan based on the user's emotional state" refers to a device or software that utilizes the results of the user's emotional analysis to dynamically adjust and modify the food supply plan to optimize it for that emotional state.

[0558] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0559] The server connects to an external weather database to obtain climate information, acquiring current and predicted weather data for each region in real time. In parallel, it acquires market information from various data sources, including market trends and consumer purchasing tendencies. Based on this information, it uses a consumer trend prediction algorithm to forecast future consumer behavior. The hardware or software used is typically a standard server computer connected to the internet; weather APIs are commonly used to acquire climate information, and machine learning models are used to predict consumer trends.

[0560] The user's device uses its camera and microphone to capture facial expressions and voice data, and recognizes their emotional state in real time via emotion recognition software. This emotional data is sent to a server, and the food supply plan is adjusted based on the user's emotional state. This process uses emotion recognition APIs such as Microsoft Azure Cognitive Services. The device then visualizes the adjusted supply plan and suggests the best food options and delivery plans for the user.

[0561] Furthermore, users can provide feedback on their emotional state through their device. This feedback is further analyzed by the emotion engine and used to generate future plans. For example, if a user expresses fatigue, the system can suggest foods or smoothies that will help restore their energy.

[0562] An example of a prompt message is: "If the user's emotion is identified as 'fatigue,' prioritize suggesting products that help with recovery."

[0563] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0564] Step 1:

[0565] The server uses a weather API to retrieve climate information from an external weather database. The input is the region's location, and based on this, it retrieves current and predicted weather data. The output is climate data for the relevant region. This data is used to predict future consumption trends.

[0566] Step 2:

[0567] The server uses a market trend API to acquire market information. The input is category information for the target product. This allows the server to collect market supply and demand balance and price fluctuation data. The output is a dataset showing market trends. This enables the consumer trend prediction algorithm to function.

[0568] Step 3:

[0569] The server runs a generative AI model that integrates climate and market information to predict consumption trends. The inputs are climate and market data from steps 1 and 2. The output is predictive data showing future consumption trends. This predictive data is used to optimize the food supply network.

[0570] Step 4:

[0571] The device uses the user's camera and microphone to acquire facial expressions and audio data. The input is the user's real-time image and audio. Based on this data, an emotion recognition API is used to analyze the user's emotions in real time. The output is the user's current emotional state.

[0572] Step 5:

[0573] The server performs a mechanism to adjust the food supply plan using the user's emotional state data. The inputs are the predicted consumption trend data obtained in step 3 and the emotional data obtained in step 4. The output is the adjusted food supply plan. This plan reflects the user's emotional state in the optimal delivery route and suggestions.

[0574] Step 6:

[0575] The user's device visually presents the adjusted food supply plan. The input is the adjusted plan from step 5. The output is the supply plan and options displayed on the user's screen. This allows the user to review the plan and provide feedback as needed.

[0576] 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.

[0577] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0578] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0579] [Fourth Embodiment]

[0580] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0581] 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.

[0582] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0583] 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.

[0584] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0585] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0586] 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.

[0587] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0588] 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.

[0589] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0590] The 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.

[0591] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0592] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0593] This invention is an AI system aimed at improving the efficiency of food production and distribution. The system includes a server, terminals, and a user interface. The server functions as the central processing unit, acquiring climate and market data from external sources. Using this data, the server performs data analysis and generates predictions of consumption trends. The analysis applies advanced algorithms, enabling detailed demand forecasts for each region.

[0594] Based on the analysis results, the server plans to create an optimized food supply network. This ensures proper inventory management and reduces food waste. The server develops and updates production plans in real time, including food production volumes and distribution routes.

[0595] The terminal plays the role of providing information from the server to the user. This includes a visually displayed production plan with an intuitive interface that the user can easily understand. The terminal also supports the user in modifying the plan.

[0596] Users can make concrete decisions based on the information presented on the terminal. For example, if a user wants to adjust production volume based on food demand, they can input this information into the terminal, and the server will automatically generate a new production plan.

[0597] For example, if the supply of rice becomes unstable due to weather in a certain region, the server updates its forecast using climate data and analyzes the demand for alternative food products from market data. The server then proposes an optimized delivery route, allowing the user to manage inventory appropriately and reduce food waste.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] The server retrieves real-time climate data from external weather data providers. This includes ingesting data such as the latest temperature, precipitation, and wind speed for a specific region.

[0601] Step 2:

[0602] The server collects market data from domestic and international markets. This data includes price fluctuations for goods, trends in demand, and the competitive landscape of the market.

[0603] Step 3:

[0604] The server analyzes this data and uses a generative AI model to predict consumption trends. This analysis predicts how consumer demand will evolve in each region.

[0605] Step 4:

[0606] Based on the prediction results, the server develops a plan to optimize the food supply network. This includes adjusting production volumes and optimizing logistics routes.

[0607] Step 5:

[0608] The server sends an optimized production plan to the terminal for user review. The terminal visually displays this information in a dashboard format.

[0609] Step 6:

[0610] The user checks the production plan displayed on the terminal and makes adjustments as needed. At this time, the user inputs the corrected data via the terminal and sends it to the server.

[0611] Step 7:

[0612] The server receives input from the user and, if necessary, sends the updated production plan back to the terminal in real time.

[0613] Step 8:

[0614] The user approves the final plan and begins its implementation. The terminal transmits operational instructions based on the approved plan to the relevant logistics systems and production departments.

[0615] (Example 1)

[0616] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0617] In recent years, the problems of resource waste and food waste have become increasingly serious in food production and distribution. Furthermore, the uncertainty caused by climate change is impacting production plans, necessitating the establishment of an efficient food supply system. Against this backdrop, achieving increased efficiency in food production and the creation of a sustainable supply system is an urgent priority.

[0618] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0619] In this invention, the server includes means for acquiring information on climate, means for acquiring information on the market, and means for predicting consumption trends. This enables the optimization of an efficient food supply system, reduces food waste, and allows for the creation of sustainable production plans.

[0620] "Means of obtaining climate-related information" refers to devices or programs that obtain meteorological data from external sources and analyze environmental conditions that affect food production.

[0621] "Means of acquiring information in the market" refers to devices or programs for collecting data on market trends and demand forecasts, and for analyzing consumer trends.

[0622] "Means for predicting consumption trends" refer to algorithms and devices that use acquired data to analyze consumer behavior patterns and estimate future demand.

[0623] "Means for optimizing the food supply system" refers to a device or program for formulating plans to streamline the entire production and distribution process, thereby reducing costs and stabilizing supply.

[0624] "Methods for performing data analysis using machine learning algorithms" refers to algorithms and methods for training models on datasets and performing predictions or pattern recognition.

[0625] "Means for formulating food supply plans" refer to processes and systems for creating efficient production and distribution plans based on collected and analyzed data.

[0626] "Means of inventory management" refer to methods and devices for monitoring the inflow and outflow of food products and maintaining the necessary quantities appropriately.

[0627] "Means for visually displaying the generated production plan and supporting user operation" refers to an interface or system that visualizes the analysis results, allowing users to easily understand and modify the plan.

[0628] This invention implements an AI system for improving the efficiency of food production and distribution. The system comprises a server, terminals, and a user interface.

[0629] The server functions as the central processing unit, first acquiring climate data from external sources. Specifically, it retrieves real-time weather data via APIs and stores it in a database. In addition, it collects market supply and demand-related data from online market information providers. These two datasets are integrated, and machine learning models are trained using software such as Python and TensorFlow to perform consumption trend forecasts. This enables detailed demand forecasts by region.

[0630] Based on the analyzed data, the server uses linear programming techniques to optimize the food supply network and formulate a production plan. This production plan includes optimal production volumes and distribution routes, which are constantly updated in real time. For example, if climate data indicates that rice production will be affected, the server uses that forecast data to analyze the demand for alternative food products and create an appropriate supply plan.

[0631] The terminal serves as an interface that visually displays the production plan generated from the server to the user. The software installed on the terminal graphically represents the data, allowing the user to intuitively understand the situation. Based on the information provided by the terminal, the user can make decisions and modify the plan as needed. Using the touchscreen or mouse, the user can adjust production volumes and set new delivery routes, and any resulting changes are immediately fed back to the server.

[0632] For example, if unstable rice production is predicted in a specific region due to weather conditions, the server analyzes market data and predicts the demand for alternative food products, such as pasta or bread. Based on this information, it proposes efficient delivery routes to the user and assists with inventory management.

[0633] An example of a prompt to input into the generating AI model is, "Please show the regional consumption forecast for next month." This will allow for a more detailed understanding of regional consumption trends and utilize this information in production and distribution planning.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The server uses the API of a weather information provider to retrieve real-time climate data from external sources. This input data includes temperature, precipitation, wind speed, and other parameters. The server stores this data in a database and converts it into a format usable for later analysis.

[0637] Step 2:

[0638] The server collects market data related to supply and demand from online market information providers. This data includes market prices, supply quantities, and consumer purchasing patterns. The input market data is retrieved in XML or JSON format and converted into a format suitable for analysis.

[0639] Step 3:

[0640] The server integrates climate and market data and uses machine learning algorithms to predict consumption trends. In this process, the server uses Python and TensorFlow to analyze the data and generate regional demand forecasting models. The output results are generated as predicted demand values.

[0641] Step 4:

[0642] The server uses linear programming techniques to develop an optimal food supply plan based on the generated demand forecast. Inputs include the forecasted demand and current supply chain information. The server determines the optimal production volume and distribution route while minimizing costs, and outputs the results.

[0643] Step 5:

[0644] The terminal visually presents production plan information to the user. The entered plan data is displayed in a graphical interface, making it easy for the user to understand. The terminal provides bar graphs, line graphs, and map views to help the user intuitively grasp the situation in each region.

[0645] Step 6:

[0646] Based on the information provided, users make modifications to production plans and delivery routes. Users input changes using the terminal's touch panel or keyboard. These modifications are sent to the server, where they are immediately re-analyzed and the plan is updated. The server generates a new production plan and outputs the result reflecting the changes.

[0647] (Application Example 1)

[0648] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] Modern food supply systems face problems such as frequent food surpluses and shortages due to their inability to respond quickly to climate change and market fluctuations. Furthermore, selecting efficient logistics routes is difficult, leading to increased transportation costs and times. Additionally, there is a lack of means to provide real-time, visual information and enable rapid decision-making.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0651] In this invention, the server includes means for acquiring climate data, means for acquiring market data, means for predicting consumption trends, means for optimizing the food supply network, means for suggesting the optimal delivery route, and means for providing information visually. This enables more efficient logistics and a rapid response to fluctuations in consumption.

[0652] "Climate data" refers to numerical information about the climate of a specific region, such as temperature, precipitation, and wind speed.

[0653] "Market data" refers to information related to economic activity in the market, such as consumer purchasing trends and product prices.

[0654] "Means of predicting consumption trends" refers to the process of analyzing past data to estimate future consumption patterns.

[0655] "Means of optimizing the food supply network" refers to methods for streamlining each stage of the supply chain, thereby reducing costs and shortening delivery times.

[0656] "Means of suggesting the optimal delivery route" refers to the process of calculating and presenting the most efficient delivery route based on geographical information and transportation conditions.

[0657] "Means of providing information visually" refers to methods of conveying information intuitively through a user interface via digital devices.

[0658] A system implementing this invention consists of a server for acquiring and analyzing climate data and market data, and a terminal for providing information from the server to the user.

[0659] The server obtains climate data from external weather information services and collects market data from databases and online market analysis tools. This data is analyzed using the Python programming language and the scikit-learn library to predict consumption trends. Specifically, it uses a linear regression model based on historical data to estimate future demand. Based on the results of this analysis, the server builds an optimal food supply network and calculates the most optimal delivery routes.

[0660] The terminal provides information to the user visually. The terminal is a smart glasses or other wearable device that allows the user to check production and logistics plans in real time. Furthermore, picking instructions and logistics route information are displayed intuitively based on gaze and touch gestures.

[0661] For example, if the temperature changes rapidly in a certain region, the data is immediately analyzed on the server, and the results are reflected on the terminal in real time. This allows users to instantly check the optimal production volume and delivery route, enabling them to make quick decisions. An example of a prompt message to the generated AI model might be, "Based on the received climate and market data, predict the demand trends for food products and plan the optimal supply network."

[0662] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0663] Step 1:

[0664] The server acquires climate and market data. It obtains temperature and precipitation data from weather information services using APIs, and collects consumer purchasing pattern data from online market analysis tools. This data is stored in a database and used for subsequent analysis.

[0665] Step 2:

[0666] The server predicts consumption trends using acquired climate and market data. It applies a linear regression model from scikit-learn, generating a predictive model based on the data. Based on this, it calculates future demand numerically. The output is data representing the predicted consumption trends.

[0667] Step 3:

[0668] The server optimizes the food supply network based on predictive data. Using an optimization algorithm, it analyzes the efficiency of each stage of the supply chain and creates a plan to complete distribution at the lowest possible cost. The output is the optimized supply network plan.

[0669] Step 4:

[0670] The server then calculates the optimal delivery route. Using a Geographic Information System (GIS), it generates a route based on traffic conditions and distance information. This allows it to output the most efficient delivery route.

[0671] Step 5:

[0672] The terminal receives data transmitted from the server and presents the information to the user visually. In smart glasses, the display shows plans and route information, designed to be intuitively understandable to the user. The user reviews the information and adjusts picking and routes as needed.

[0673] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0674] This invention is an AI system that streamlines food production and distribution and also has the ability to recognize user emotions. The system includes a server, terminals, a user interface, and an emotion engine. The server acquires climate and market data from external sources and uses this to predict consumption trends. These predictions are used to optimize the food supply network and help in formulating production plans.

[0675] The emotion engine recognizes and analyzes user emotions in real time. Based on this emotion data, the server can adjust production plans and logistics strategies, making it possible to build an optimal food supply network that takes user stress and satisfaction into consideration.

[0676] The terminal not only provides users with visual information from the server, but also presents instructions that have been adjusted based on the analysis results of the emotion engine. This allows users to receive planning information that is best suited to their own emotional state.

[0677] Users review the plan displayed on their device and provide emotion-based feedback as needed. This feedback is then used as further data by the emotion engine, contributing to the overall improvement of the system.

[0678] For example, if a user reports high stress levels, the emotion engine instructs the server to prioritize simpler, less risky logistics plans. The server, upon receiving this instruction, generates a plan that includes risk mitigation measures while maintaining efficiency as much as possible within given constraints, and sends it to the terminal. This process enables a sustainable food supply system that incorporates emotional factors.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] The server retrieves climate data from external weather data providers. This involves collecting real-time updated weather information for each region.

[0682] Step 2:

[0683] The server collects market data from each market. This data includes product prices, demand trends, and sales data.

[0684] Step 3:

[0685] The server analyzes acquired climate and market data and uses a generative AI model to predict consumption trends. This prediction allows for an estimate of how future demand will change.

[0686] Step 4:

[0687] The server creates a plan to optimize the food supply network based on consumption trend forecasts. This plan includes adjusting production volumes and selecting transportation routes.

[0688] Step 5:

[0689] The server sends the plan details to the terminal for the user to review. The terminal then displays this information to the user in an easy-to-understand format.

[0690] Step 6:

[0691] The emotion engine collects and analyzes the user's real-time emotional state. This emotional data indicates the user's stress level, satisfaction level, and other factors.

[0692] Step 7:

[0693] Based on information obtained from the emotion engine, the server optimizes production operations. At this stage, adjustments to the plan may be made based on the user's emotions.

[0694] Step 8:

[0695] The device presents the user with a plan that has been adjusted based on sentiment analysis. The user can provide feedback on the presented plan.

[0696] Step 9:

[0697] User feedback is evaluated by an emotion engine, and the results are fed back to the server. The server then uses the feedback information to further improve the plan.

[0698] (Example 2)

[0699] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] In recent years, as there has been a growing demand for increased efficiency in food production and distribution, the challenge has become building a flexible supply network that takes into account climate change, market uncertainties, and the emotions of individual consumers. Traditional systems have struggled to adequately consider consumer emotional factors in planning and adjustments, resulting in increased food waste and decreased customer satisfaction.

[0701] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0702] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for information processing for predicting consumption trends, means for planning for optimizing the supply network, means for analysis for recognizing emotional data, and means for adjusting the plan based on emotional data. This makes it possible to build a flexible and sustainable food supply network that takes consumer emotions into consideration.

[0703] "Climate information" refers to data on current or expected weather conditions in a specific region, such as temperature, precipitation, humidity, wind direction, and wind speed.

[0704] "Market information" refers to data on various conditions in a specific market, such as supply and demand, product prices, and consumer trends.

[0705] "Consumer trends" refer to predictive data that shows changes in consumer purchasing behavior and preferences over a certain period.

[0706] A "supply network" refers to the structure of a supply chain that shows how the production, distribution, and sale of a product are organized.

[0707] "Planning means" refers to a method or apparatus for formulating the procedures and schedules necessary to achieve a specific objective.

[0708] "Emotional data" refers to information that represents the emotional state of individual consumers, and includes data related to emotions such as joy, sadness, anger, and surprise.

[0709] "Analysis means" refers to a method or apparatus for analyzing acquired data and extracting useful information or insights.

[0710] "Means of adjusting a plan" refers to methods or devices for modifying or improving an existing plan based on changing conditions or new information.

[0711] This invention aims to streamline food production and distribution and build a supply network that takes user emotions into consideration. Therefore, the system includes a server, terminals, a user interface, and an emotion recognition engine.

[0712] The server obtains climate and market information from external APIs. Specifically, it uses a weather data acquisition API for climate information and various statistical data libraries for market information. Based on this data, the server utilizes generative AI models such as TensorFlow and PyTorch to predict consumption trends. Based on the consumption trends calculated by these AI models, the server formulates and optimizes production and logistics plans.

[0713] For emotion recognition, the system acquires the user's facial expressions and voice from the device's built-in camera and microphone, and analyzes this data using OpenCV and an emotion analysis API. The user's emotions are recognized in real time and sent to the server as emotion data. The server uses this emotion data to adjust plans, taking into account the user's stress level and satisfaction level. This adjustment process also includes risk management for logistics plans and optimization of delivery routes.

[0714] The terminal visually presents information from the server to the user. The user reviews the plan through the terminal and provides emotion-based feedback. This feedback is then used as further data by the emotion recognition engine, contributing to the overall improvement of the system.

[0715] For example, when a user provides emotional feedback to the system stating "I've been feeling stressed lately," the server automatically generates a plan prioritizing risk management and delivers it to the user via their device. This generated plan provides the most appropriate information for the user's emotional state.

[0716] An example of a prompt message might be: "The user has reported experiencing high levels of stress. Please propose an optimal food supply plan that takes this into consideration."

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] The server retrieves climate and market information via an external API. Input requires an API key and region specification, while output consists of climate data (e.g., temperature, precipitation) and market data (e.g., supply and demand indicators). This data is imported into the server in JSON format.

[0720] Step 2:

[0721] The server uses a generative AI model based on the acquired data to predict consumption trends. Specifically, it uses TensorFlow for prediction. The inputs required are climate information and market information acquired in step 1, and the output is a predicted value of future consumption trends. This predicted value will be used in the optimization process in the next step.

[0722] Step 3:

[0723] The server optimizes production planning and supply networks. It requires the predicted consumption trends obtained in step 2 as input. Linear programming is used to develop a plan that considers supply costs and production capacity. The output is an optimized production and logistics plan.

[0724] Step 4:

[0725] The device uses a camera and microphone to capture the user's real-time facial expressions and voice. The input is this sensor data, and the output is an analysis result indicating the user's emotions. This analysis is performed using OpenCV.

[0726] Step 5:

[0727] The server analyzes the emotional data sent from the terminal to determine the user's emotional state. The input is the emotional analysis results from step 4, and the output is numerical data indicating the user's stress level and satisfaction level. This data is used by the server to readjust the plan.

[0728] Step 6:

[0729] The server takes emotional data into account and readjusts production plans and supply chains. Inputs include the optimized plan from step 3 and the emotional data from step 5. The output is a new plan that takes into account the user's emotional state and logistics risks. Specifically, it involves changing delivery routes and re-evaluating supplier options.

[0730] Step 7:

[0731] The terminal presents the user with the adjusted plan. The input is the plan from step 6, and the output is visualized information through a graphical user interface. The user then provides emotion-based feedback based on this information.

[0732] Step 8:

[0733] Users provide feedback based on the information displayed on their devices. The input is the information displayed on the device, and the output is data sent to the server as feedback. This feedback is used to improve the overall system adaptability.

[0734] (Application Example 2)

[0735] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In modern society, the processes of food production and distribution are plagued by problems such as decreased efficiency and increased food waste. Furthermore, one-sided food supply, without considering the emotional state of consumers, can lead to decreased consumer satisfaction. It is necessary to address these challenges and realize a sustainable food supply system.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0738] In this invention, the server includes means for acquiring climate information, means for acquiring market information, means for predicting consumption trends, means for optimizing the food supply network, means for recognizing and analyzing the user's emotional state, and means for adjusting the food supply plan based on the user's emotional state. This enables an efficient and sustainable food supply system that takes consumer emotions into consideration.

[0739] "Means for acquiring climate information" refers to devices or software that have the function of acquiring information on current and future weather conditions from external weather databases or sensors.

[0740] "Means for acquiring market information" refers to devices or software for collecting data such as market trends, prices, and consumer purchasing tendencies from various data sources.

[0741] "Means for predicting consumption trends" refers to devices or software that have the function of predicting future consumption behavior trends using mathematical models or analytical methods based on acquired market information and climate information.

[0742] "Means for optimizing food supply networks" refers to devices or software that have the function of optimizing food production, distribution, and delivery based on predicted consumption trends, and for building an efficient supply system.

[0743] "Means for recognizing and analyzing a user's emotional state" refers to a device or software that has the function of identifying and evaluating a user's current emotional state in real time by analyzing the user's facial expressions, voice, behavioral data, etc.

[0744] "Means for adjusting the food supply plan based on the user's emotional state" refers to a device or software that utilizes the results of the user's emotional analysis to dynamically adjust and modify the food supply plan to optimize it for that emotional state.

[0745] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0746] The server connects to an external weather database to obtain climate information, acquiring current and predicted weather data for each region in real time. In parallel, it acquires market information from various data sources, including market trends and consumer purchasing tendencies. Based on this information, it uses a consumer trend prediction algorithm to forecast future consumer behavior. The hardware or software used is typically a standard server computer connected to the internet; weather APIs are commonly used to acquire climate information, and machine learning models are used to predict consumer trends.

[0747] The user's device uses its camera and microphone to capture facial expressions and voice data, and recognizes their emotional state in real time via emotion recognition software. This emotional data is sent to a server, and the food supply plan is adjusted based on the user's emotional state. This process uses emotion recognition APIs such as Microsoft Azure Cognitive Services. The device then visualizes the adjusted supply plan and suggests the best food options and delivery plans for the user.

[0748] Furthermore, users can provide feedback on their emotional state through their device. This feedback is further analyzed by the emotion engine and used to generate future plans. For example, if a user expresses fatigue, the system can suggest foods or smoothies that will help restore their energy.

[0749] An example of a prompt message is: "If the user's emotion is identified as 'fatigue,' prioritize suggesting products that help with recovery."

[0750] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0751] Step 1:

[0752] The server uses a weather API to retrieve climate information from an external weather database. The input is the region's location, and based on this, it retrieves current and predicted weather data. The output is climate data for the relevant region. This data is used to predict future consumption trends.

[0753] Step 2:

[0754] The server uses a market trend API to acquire market information. The input is category information for the target product. This allows the server to collect market supply and demand balance and price fluctuation data. The output is a dataset showing market trends. This enables the consumer trend prediction algorithm to function.

[0755] Step 3:

[0756] The server runs a generative AI model that integrates climate and market information to predict consumption trends. The inputs are climate and market data from steps 1 and 2. The output is predictive data showing future consumption trends. This predictive data is used to optimize the food supply network.

[0757] Step 4:

[0758] The device uses the user's camera and microphone to acquire facial expressions and audio data. The input is the user's real-time image and audio. Based on this data, an emotion recognition API is used to analyze the user's emotions in real time. The output is the user's current emotional state.

[0759] Step 5:

[0760] The server performs a mechanism to adjust the food supply plan using the user's emotional state data. The inputs are the predicted consumption trend data obtained in step 3 and the emotional data obtained in step 4. The output is the adjusted food supply plan. This plan reflects the user's emotional state in the optimal delivery route and suggestions.

[0761] Step 6:

[0762] The user's device visually presents the adjusted food supply plan. The input is the adjusted plan from step 5. The output is the supply plan and options displayed on the user's screen. This allows the user to review the plan and provide feedback as needed.

[0763] 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.

[0764] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0765] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0766] 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.

[0767] Figure 9 shows an 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.

[0768] 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.

[0769] 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.

[0770] 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, motorcycles, etc., 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, for example, based 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.

[0771] 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."

[0772] 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.

[0773] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0774] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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 the like 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.

[0783] 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 as being incorporated by reference.

[0784] The following is further disclosed regarding the embodiments described above.

[0785] (Claim 1)

[0786] Means of acquiring climate data,

[0787] Means of obtaining market data,

[0788] Means for predicting consumer trends,

[0789] Means for optimizing the food supply network,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for managing inventory to reduce food waste.

[0793] (Claim 3)

[0794] The system according to claim 1, further comprising means for displaying the generated production plan and providing instructions to the user.

[0795] "Example 1"

[0796] (Claim 1)

[0797] Means of obtaining climate information,

[0798] Means of obtaining information in the market,

[0799] Means for predicting trends related to consumption,

[0800] Means for optimizing the food supply system,

[0801] Methods for performing data analysis using machine learning algorithms,

[0802] Means for formulating a food supply plan,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for inventory management aimed at reducing food waste.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising means for visually displaying the generated production plan and assisting the user's operation.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] Means of acquiring climate data,

[0811] Means of obtaining market data,

[0812] Means for predicting consumer trends,

[0813] Means for optimizing the food supply network,

[0814] A means of suggesting the optimal delivery route,

[0815] Means of providing information visually,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for managing inventory to reduce food waste.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising means for displaying the generated production plan and providing instructions to the user.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] Means of obtaining climate information,

[0824] Means of obtaining market information,

[0825] Information processing means for predicting consumption trends,

[0826] A planning tool for optimizing the supply network,

[0827] An analytical method for recognizing emotional data,

[0828] A means of adjusting plans based on emotional data,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising means for managing resources to reduce food waste.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising means for displaying a generated plan and providing instructions to a user.

[0834] "Application example 2 when combining with an emotional engine"

[0835] (Claim 1)

[0836] Means of obtaining climate information,

[0837] Means of obtaining market information,

[0838] Means for predicting consumer trends,

[0839] Means for optimizing the food supply network,

[0840] A means of recognizing and analyzing the emotional state of users,

[0841] A means of adjusting the food supply plan based on the user's emotional state,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for managing inventory to reduce food waste.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for displaying the generated production plan and providing instructions to the user.

[0847] (Claim 4)

[0848] The system according to claim 1, further comprising means for presenting suggestions according to the user's emotional state. [Explanation of Symbols]

[0849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring climate data, Means of obtaining market data, Means for predicting consumer trends, Means for optimizing the food supply network, A system that includes this.

2. The system according to claim 1, further comprising means for managing inventory to reduce food waste.

3. The system according to claim 1, further comprising means for displaying the generated production plan and providing instructions to the user.

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