system

Abstract

We provide the system. [Solution] A means of optimizing production plans by acquiring and analyzing climate data, A method for forecasting demand based on past consumption data, A means of managing inventory based on an optimized production plan, A means of creating a delivery plan based on demand forecast results and optimizing logistics routes, A means of collecting user feedback and dynamically adjusting production and delivery plans based on that feedback, A means of proposing the most suitable product for user equipment based on climate and market data, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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] Food surpluses and shortages occur simultaneously around the world. In particular, climate change - induced uncertainties in production and fluctuations in demand are factors causing food losses. Furthermore, due to insufficient efficiency in transportation and inventory management, there is a problem that sustainability has not been achieved in food supply. In such a situation, it is necessary to optimize the process from food production to distribution by utilizing climate data and consumption trends.

Means for Solving the Problems

[0005] This invention optimizes production planning by incorporating means for acquiring and analyzing climate data. Furthermore, it combines this with means for forecasting demand based on past consumption data, thereby streamlining production and inventory management. It also provides means for creating delivery plans based on demand forecast results and optimizing logistics routes. Moreover, it provides a system that reduces food waste and ensures that the necessary quantities are delivered to the necessary locations by collecting user feedback and dynamically adjusting production and delivery plans based on that feedback. This enables sustainable food supply.

[0006] "Climate data" refers to information about weather conditions, including elements such as temperature, precipitation, wind speed, and humidity, and is used to predict future weather patterns.

[0007] "Means of analysis" refers to the process or techniques for processing collected data and transforming it into meaningful information, and specifically includes software and algorithms used to identify trends and patterns in the data.

[0008] A "production plan" is a plan for determining the schedule and quantity of food and other goods produced, with the aim of optimizing the balance between supply and demand.

[0009] "Demand forecasting" refers to the process of predicting future consumption based on past data and market trends, and is a technique used to predict fluctuations in demand over a specific period.

[0010] "Inventory management" refers to the process of ensuring that goods and raw materials are managed appropriately and supplied at the right time, and includes tracking, replenishing, and organizing inventory.

[0011] A "delivery plan" refers to a plan for efficiently delivering goods to their destination, and includes optimizing delivery schedules and routes.

[0012] A "logistics route" refers to the path a product takes from the production site to the consumer, and is a route designed to ensure delivery in the shortest distance and most efficient time.

[0013] "User feedback" refers to information about consumption patterns and opinions provided by end consumers or system users, and is used to improve the system and enhance the accuracy of demand forecasting. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that optimizes food production and distribution using climate data and market data. The system consists of a server, terminals, and users, and operates as follows:

[0036] Collection and analysis of climate data

[0037] The server communicates with external weather information providers and periodically retrieves climate data via APIs. This data is analyzed using machine learning models and used to predict future weather patterns in specific areas. These predictions are a crucial factor in optimizing production plans.

[0038] Market data analysis and demand forecasting

[0039] The server retrieves historical consumption data from the database and uses statistical analysis methods and machine learning algorithms to forecast demand. This demand forecast is used to understand future consumption trends.

[0040] Production planning

[0041] The server develops optimal production plans for each production site based on climate and demand forecasts. This prevents overproduction and inventory shortages, ensuring efficient production activities. The production plans are updated in real time and adjusted as needed.

[0042] Inventory management and delivery planning

[0043] Based on the production plan, the server manages inventory data and optimizes inventory levels at distribution centers across the country. Furthermore, it calculates the optimal delivery route to improve logistics efficiency. Traffic and weather information are also taken into consideration to ensure deliveries are made at the best possible time.

[0044] User feedback and dynamic adjustments

[0045] Users input consumption information and additional demand data through their terminals. This information is sent to the server and stored in a feedback database. The server uses this data to re-evaluate demand forecasts and reflect them in production and delivery plans. This dynamic adjustment ensures that food supply is always optimized based on the latest information.

[0046] As a concrete example, consider a scenario where rising temperatures are predicted in a certain region, resulting in an expected increase in demand for a specific item (e.g., frozen desserts). In this system, the server uses this information to modify the production schedule and instructs increased production to meet the demand. Furthermore, the delivery routes are optimized to ensure that the item is supplied appropriately. In this way, efficient and flexible food supply is achieved.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server accesses APIs from weather information providers to periodically retrieve the latest climate data. The retrieved data, including temperature, precipitation, and wind speed, is stored in a database.

[0050] Step 2:

[0051] The server uses machine learning models to analyze stored climate data. This allows it to predict future weather patterns in specific regions, and the results are used as input for production planning.

[0052] Step 3:

[0053] The server extracts historical consumption data from a market database and performs demand forecasting by executing statistical analysis and machine learning algorithms. This process makes it possible to understand expected market trends and consumption patterns.

[0054] Step 4:

[0055] The server integrates analyzed climate data and demand forecasts to create an optimal production plan for each production site. The production plan is designed to avoid overproduction and inventory shortages, and resource allocation is adjusted as needed.

[0056] Step 5:

[0057] The server manages inventory levels based on production plans and monitors inventory status at each distribution center in real time. If an inventory shortage is predicted, the server immediately issues a replenishment order.

[0058] Step 6:

[0059] The server creates a delivery plan based on demand forecasts. This includes calculating the optimal delivery route and adjusting the delivery schedule to take into account traffic information and weather conditions.

[0060] Step 7:

[0061] Users input information about product consumption and new demands through their devices. The devices send this information to the server, which stores it in the database as feedback.

[0062] Step 8:

[0063] The server uses collected feedback data to update the demand forecasting model in real time. This enables dynamic adjustments to production and delivery in response to actual consumption trends on the ground.

[0064] (Example 1)

[0065] 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."

[0066] In the field of modern food production and supply, there has been a problem of supply shortages and surpluses because the impacts of climate change and market uncertainties cannot be accurately reflected. Furthermore, traditional methods have made it difficult to respond flexibly to dynamic changes in demand, making it difficult to achieve efficient inventory management and distribution planning. In addition, there has been a challenge in quickly reflecting consumer feedback, making it difficult to adjust supply to meet consumer needs.

[0067] 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.

[0068] In this invention, the server includes means for a processing device that collects and analyzes climate information from external organizations, means for a processing device that performs demand forecasting using statistical analysis methods based on past consumption information, and processing means that implements an optimization algorithm for integrating climate and market forecast data and formulating an optimized production plan. This enables efficient planning and execution from production to delivery. Furthermore, it allows for a quick and appropriate response to dynamically changing demand, suppresses supply surpluses and shortages, and realizes flexible supply tailored to consumer needs.

[0069] An "external organization" refers to a public or private organization that provides data such as weather information and traffic information.

[0070] "Climate information" refers to weather-related data such as temperature, precipitation, and wind speed, which is used for forecasting and analysis.

[0071] "Analysis" is the process of using specific methods to analyze acquired data and derive useful information.

[0072] A "processing device" refers to hardware or software used for collecting, processing, and analyzing data, and includes computers and servers.

[0073] "Statistical analysis methods" are mathematical techniques used to organize and analyze data and draw conclusions.

[0074] "Demand forecasting" is the process of analyzing and predicting future market demand.

[0075] An "optimization algorithm" refers to a computational procedure or method for obtaining the best possible result within given constraints.

[0076] A "production plan" is a plan for production aimed at efficiently utilizing resources over a certain period.

[0077] "Inventory management" is the process of properly managing inventory of products, materials, etc., to maintain a balance between supply and demand.

[0078] A "transportation route" refers to a travel path established to improve the efficiency of logistics and delivery.

[0079] "Feedback" refers to users' opinions and evaluations of a particular service or product, and is used for improvement and adjustments.

[0080] An "information management device" is a device or system used to manage the organization, storage, and access of data.

[0081] An "operating terminal" is a device used by a user to input information or obtain results, and includes computers and mobile devices.

[0082] This invention utilizes climate and market data to optimize the process from food production to distribution. The system consists of multiple elements, including servers, terminals, and users.

[0083] The server periodically collects climate information through API communication with external organizations. Specifically, it uses HTTP requests to obtain climate data such as temperature, precipitation, and wind speed, and stores it in a database. This is done using common API commands and request headers. Subsequently, the server uses the Python TENSORFLOW® library to run a time-series forecasting model and perform climate predictions. The prediction results are output as a numerical model.

[0084] Furthermore, the server uses a database management system to acquire historical consumption data and performs demand forecasting through statistical analysis methods. Software libraries such as R and scikit-learn are used here. Production plans that take into account the predicted fluctuations in demand are created using an optimization algorithm with the Python PuLP library to efficiently plan the activities of each production site.

[0085] Users operate the system from a terminal using a smartphone or computer, inputting consumption information and special demand conditions. This data is immediately transmitted to the server and stored in a database as feedback data. The server uses this feedback data to re-evaluate demand forecasts, and production and delivery plans are dynamically adjusted based on these evaluations.

[0086] Furthermore, the server uses the Google® Maps API to calculate the optimal logistics route and updates the delivery plan in real time. This process also takes into account actual traffic information and local weather conditions.

[0087] As a concrete example of its operation, when a rise in temperature is predicted in a certain region, it is predicted that the demand for frozen desserts will increase in that region. In this case, the server will use this information to change the production schedule and instruct production to meet the demand. Furthermore, it will calculate the optimal delivery route and implement a logistics plan to ensure that frozen desserts are supplied appropriately.

[0088] An example of a prompt for a generated AI model is: "Explain how to predict changes in demand for frozen desserts due to rising temperatures in a specific region and optimize production and delivery plans."

[0089] In this way, this invention enables efficient and flexible food supply.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The server collects climate information using APIs from external organizations. The input is weather data obtained through the API key network. This data is received in JSON format and stored in the server's database. Once data collection is complete, it is updated periodically.

[0093] Step 2:

[0094] The server inputs stored climate data into a TensorFlow time-series forecasting model to make future climate predictions. The input consists of time-series data such as past temperature, precipitation, and wind speed. This data is then fed into the model to predict weather patterns several days in advance, outputting numerical prediction results. This information influences production planning.

[0095] Step 3:

[0096] The server uses a database management system to retrieve historical consumption data. The input is consumption data for each region. This data is analyzed using R or scikit-learn to forecast demand. The output is forecast data regarding future demand, which is visualized as demand curves and statistical graphs.

[0097] Step 4:

[0098] The server integrates climate forecast data and demand forecast data and develops a production plan using an optimization algorithm with PuLP. The inputs are climate forecast and demand forecast data. This process calculates the optimal production volume and timing and outputs it as a plan. This schedule is then distributed to each production site.

[0099] Step 5:

[0100] The server uses the Google Maps API to calculate delivery routes for logistics optimization. Inputs include location data and traffic information between delivery points. Outputs navigation data representing efficient delivery routes. This process ensures optimal routes are set, reducing transportation time and costs.

[0101] Step 6:

[0102] Users provide feedback on market information and unique demand information using their terminals. The input is demand information entered by the user through the application. This data is sent to the server and stored in the feedback database. The server dynamically adjusts production and delivery plans based on this information. The output is reflected in the management system as a revised plan.

[0103] In each step, the generative AI model is used to query the accuracy of the computational method through prompts and to verify the reliability of the prediction results.

[0104] (Application Example 1)

[0105] 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."

[0106] The lack of efficiency in the food production and distribution process, as well as the inability to respond quickly to real-time environmental changes, leads to unnecessary inventory and failure to meet consumer demand. Furthermore, the lack of a system that can efficiently suggest suitable products to users is a challenge.

[0107] 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.

[0108] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for suggesting optimal products to user equipment based on climate and market data. This enables efficient production and inventory management, optimization of delivery plans, and appropriate product suggestions to users.

[0109] "Climate data" refers to data that records environmental conditions such as temperature, precipitation, humidity, and wind speed in a specific region.

[0110] "Analysis" is the process of collecting data and applying statistical methods and algorithms to extract meaning from it for a specific purpose.

[0111] A "production plan" is a plan that outlines the resource allocation and operating schedule necessary to improve the efficiency of production activities.

[0112] "Demand forecasting" is the process of estimating how much of a product will be needed in the future, based on past consumption data and market trends.

[0113] "Inventory management" is the administrative task of monitoring inventory levels to ensure that an appropriate amount of goods are held and to prevent excess inventory or stockouts.

[0114] A "delivery plan" is a plan that determines routes and priorities in order to efficiently deliver goods to designated locations.

[0115] "Optimizing logistics routes" is the process of determining the optimal transportation route to deliver goods over the shortest distance and in the shortest amount of time.

[0116] "User feedback" refers to information based on users' experiences and opinions, which is used to improve services and products.

[0117] "Dynamic adjustment" means instantly changing plans and actions in response to changing circumstances.

[0118] "Proposing the optimal product based on climate and market data" refers to the process of selecting and presenting products that are suitable for the user, taking into account current weather and market demand trends.

[0119] This invention comprises a server, terminals, and users to optimize a food supply system. The server periodically acquires climate data via APIs from external weather information providers. This data is analyzed using Python and the machine learning library Scikit-learn and used to predict future weather patterns in specific areas. The server also acquires historical consumption data from a database and performs demand forecasting using statistical analysis methods and machine learning algorithms. This allows for an understanding of consumption trends and the development of efficient production plans.

[0120] Users order groceries using a provided application on their smartphones or other devices. This application sends real-time notifications to users via Firebase Cloud Messaging, suggesting the most suitable products based on weather and demand. This allows for the efficient delivery of products tailored to consumers. For example, during the daytime in summer, the app can recommend cold beverages and ice cream.

[0121] The server collects order data from users as feedback and stores it in a feedback database. This helps to re-evaluate the demand forecasting model in real time. Based on this, the server dynamically adjusts delivery plans, taking into account traffic and weather information to suggest the optimal delivery route and timing.

[0122] As a concrete example, a prompt message for the generating AI model might be: "Today's forecast is for temperatures exceeding 30°C. Special deals on cold drinks and ice cream are available. Furthermore, the most efficient delivery time is 2 PM. Order now!" In this way, the invention achieves smooth and efficient food supply and appropriate product recommendations to consumers.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server retrieves climate data from a weather information provider's API. The input is climate data via the API, and the output is weather information for a specific region. The server uses Python and Scikit-learn to apply machine learning algorithms to the retrieved data to predict future weather patterns. This predicted data is then used in the next step.

[0126] Step 2:

[0127] The server retrieves historical consumption data from a database. The input is the consumption database, and the output is demand forecast data analyzed using statistical analysis and machine learning. The server runs a demand forecasting model on a generating AI model to predict future consumption trends.

[0128] Step 3:

[0129] The server integrates climate forecast data and demand forecast data to create a production plan. The input is climate forecast data and demand forecast data, and the output is an optimized production schedule. This production plan is used to prevent overproduction and inventory shortages. Specifically, it calculates production volume and timing.

[0130] Step 4:

[0131] Users using the terminal place grocery orders through the application. The input is the user's order information, and the output is order data. The data transmitted from the terminal is stored on the server. Based on this information, the server performs real-time inventory adjustments.

[0132] Step 5:

[0133] The server analyzes user order data and feedback databases to dynamically adjust delivery plans. Inputs are user feedback and order data, and output is an updated delivery schedule. It determines the optimal delivery route considering traffic and weather information.

[0134] Step 6:

[0135] The server uses a generative AI model to provide optimal product recommendations to the user. Inputs are the user profile and current weather information, and output is a customized list of recommended products. Specifically, Firebase Cloud Messaging is used to send product recommendation notifications to the user.

[0136] Step 7:

[0137] Users can place additional orders or cancellations based on notifications received on their devices. The input is the user's response, and the output is the updated order status. This prompts the server to update its demand forecasting model again and continue making appropriate real-time adjustments.

[0138] 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.

[0139] This invention is a system that combines conventional optimization methods using climate data and market data with an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users, and operates as follows:

[0140] Data collection and analysis

[0141] The server obtains climate data through APIs from weather data providers. This includes elements such as temperature, precipitation, and wind speed. The server uses this data to predict future weather. Furthermore, the server uses machine learning algorithms as a means of analyzing historical consumption data and predicting demand trends.

[0142] Utilizing the Emotion Engine

[0143] The emotion engine operates as users input feedback about a product through their device. This engine analyzes the user's verbal feedback and facial expressions to generate emotion data, which includes emotions such as satisfaction, anxiety, and anticipation. The server then analyzes this emotion data and integrates it into demand forecasting.

[0144] Optimization of production and logistics

[0145] The server integrates climate forecasts, demand forecasts, and sentiment data to create an optimal production plan. Based on this plan, production schedules for food and other products are adjusted. By more accurately understanding fluctuations in demand, it becomes possible to achieve efficient production and inventory management.

[0146] Delivery plans are created based on demand forecasts, and by further considering sentiment data, promotional activities and product suggestions are made that meet consumer expectations. The server considers road traffic information and weather in real time to ensure delivery via the shortest and most optimal route.

[0147] Using user feedback

[0148] Users use a terminal to input feedback about products. This feedback includes information about consumption patterns and new demand. The terminal sends this feedback to a server, which stores it in a feedback database. The server uses the feedback data to update demand forecasting models and dynamically adjust production and delivery plans.

[0149] For example, if many users express high levels of satisfaction with a particular product, the server predicts additional demand for that product and increases production. Conversely, if dissatisfaction data is received regarding another product, the delivery plan is adjusted to reduce delivery of that product and suggest alternatives. In this way, utilizing an emotion engine enables flexible food supply and marketing activities that take consumer emotions into account.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] The server accesses APIs from weather information providers to obtain climate data in real time. This data is stored in a database and used for subsequent climate forecasts.

[0153] Step 2:

[0154] The server analyzes stored climate data and uses machine learning models to make predictions. This generates future weather patterns, which are then used to formulate production plans.

[0155] Step 3:

[0156] The server retrieves historical consumption data from a market database and uses statistical analysis and machine learning techniques to forecast demand. This forecast is essential for predicting product consumption trends.

[0157] Step 4:

[0158] Users use a device to input feedback about their satisfaction level and any dissatisfaction with the product. The device collects this information and sends it to an emotion engine to analyze the user's emotions.

[0159] Step 5:

[0160] The emotion engine analyzes user feedback and generates emotional data based on language and facial expressions. This emotional data includes the user's satisfaction level and expectations.

[0161] Step 6:

[0162] The server incorporates the generated sentiment data into demand forecasts, improving their accuracy. This optimizes product production and supply to better meet consumer needs.

[0163] Step 7:

[0164] The server comprehensively analyzes climate forecasts, demand forecasts, and sentiment data to adjust production plans. Production schedules are designed to prevent overproduction and inventory shortages.

[0165] Step 8:

[0166] The server considers demand forecasts and sentiment data to create a delivery plan. This plan includes selecting the optimal delivery route and adjusting the timing to account for traffic conditions.

[0167] Step 9:

[0168] Each time a user provides new feedback, the server uses this information to update demand forecasting models and production / delivery plans in real time. This ensures a flexible supply chain that reflects consumer intuition and expectations.

[0169] (Example 2)

[0170] 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".

[0171] Traditional methods for optimizing production and logistics planning relied primarily on historical consumption data and climate data, making it difficult to predict user sentiment and expectations. As a result, there was insufficient flexibility to enhance consumer satisfaction, and improvements in inventory management and delivery efficiency were limited. Furthermore, updating demand forecasting models in real time was difficult, leading to delays in responding to rapid market changes.

[0172] 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.

[0173] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption information, and means for generating sentiment information based on feedback data and integrating it into the demand forecast. This enables demand forecasting based on consumer sentiment and allows for the development of flexible production and logistics plans.

[0174] "Climate data" refers to meteorological information such as temperature, precipitation, and wind speed, and is used for forecasting and planning.

[0175] "Consumer information" refers to data on past sales volume and purchasing trends of products, and is the basis for demand forecasting.

[0176] "Demand forecasting" is the process of predicting future consumer trends based on past consumption information and other related data.

[0177] "Feedback data" refers to data such as opinions and impressions about products collected from users, and is information that can be used to improve products and services.

[0178] "Emotional information" refers to information that represents the emotional state of users by analyzing user feedback data, and is used for demand forecasting and the formulation of marketing strategies.

[0179] "Generated prompts" are instructions or questions that are automatically generated to dynamically update the prediction model.

[0180] "Production planning" refers to a specific plan for optimizing the production volume and timing of a product.

[0181] "Logistics route" refers to a route plan used to determine the delivery route and schedule for goods.

[0182] This invention optimizes production and logistics planning by integrating climate data, consumption information, and sentiment information using a system centered on servers, terminals, and users. Specific embodiments are described below.

[0183] The server first accesses an API from an external weather data provider to obtain climate data. This data includes temperature, precipitation, wind speed, etc., and the server uses this to analyze weather patterns. The server can use machine learning software such as scikit-learn or TensorFlow for the analysis.

[0184] Next, the server retrieves past consumption information from a database and uses machine learning algorithms to forecast demand. Specific libraries that may be used include scikit-learn's Random Forest and TensorFlow's neural network models.

[0185] Through the terminal, users input feedback on products. This feedback data is converted into emotional information through text and image analysis. The terminal is equipped with an emotion engine that extracts emotions such as satisfaction, anxiety, and expectation from the user's linguistic expressions and facial expressions. The extracted emotional information is sent to a server and integrated into demand forecasting.

[0186] The server integrates this data and develops production plans that take demand accuracy into account. It utilizes prompts generated using a generative AI model to expedite production and logistics-related decision-making.

[0187] Furthermore, the server uses the Google Maps API to determine the optimal delivery route for logistics planning, and also develops personalized promotions based on emotional information.

[0188] For example, if temperatures rise and many users express high satisfaction with a particular beverage, the server will predict increased demand for that beverage and issue instructions to increase production. It will also generate prompts such as, "Analyze people's feelings towards this product and propose the next sales strategy," in order to refine the market strategy.

[0189] In this way, the system effectively utilizes data through the collaboration of servers, terminals, and users, thereby improving the accuracy of consumer trend predictions.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The server obtains climate data by sending API requests to external weather data providers. It uses an API key and necessary data parameters as input. The obtained weather information (temperature, precipitation, wind speed, etc.) is stored in a database on the server, and data processing is performed to convert it into an analyzable format. The output is data ready for analysis based on weather forecasts.

[0193] Step 2:

[0194] The server retrieves historical consumption information from the database. It uses consumption records and sales data as input. Machine learning algorithms (e.g., scikit-learn's Random Forest) are used to analyze this data and predict future demand. The predicted demand data is output, which forms the basis of production planning.

[0195] Step 3:

[0196] Users enter feedback about a product through their device. This feedback may include text and photos. The entered feedback is processed by an emotion engine on the device and converted into the user's emotional information (satisfaction, anxiety, expectations, etc.). Emotional data is output and this information is sent to the server.

[0197] Step 4:

[0198] The server integrates weather data, demand forecasts, and sentiment information to formulate a production plan. It uses various data obtained in previous steps as input. Utilizing a generative AI model, it generates prompt messages and automates the adjustment of the production schedule. The output is an optimized production plan. Based on these results, specific instructions are issued to the production department.

[0199] Step 5:

[0200] The server optimizes logistics plans based on demand forecasts and sentiment information. Inputs include generated production plans and real-time traffic data. It utilizes the Google Maps API to calculate the shortest route and determines logistics routes that take promotional activities into consideration. The final output is an efficient and optimized delivery plan.

[0201] Step 6:

[0202] Based on user feedback and improvements to demand forecast accuracy, the server dynamically updates the forecasting model using generated prompts. The input consists of user feedback and data from the existing model, while the output is the updated demand forecasting model. This model further enhances the accuracy of analysis in subsequent processes.

[0203] (Application Example 2)

[0204] 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".

[0205] While systems have traditionally used climate data and historical consumption data for production planning and demand forecasting, they have the drawback of failing to take into account demand fluctuations based on consumer sentiment. This makes it difficult to flexibly adjust production and logistics plans, potentially leading to decreased consumer satisfaction and lost opportunities. Therefore, there is a need to provide a system that can meet consumer expectations by integrating real-time consumer sentiment data, personalizing product recommendations, and optimizing production and logistics flexibly.

[0206] 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.

[0207] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for detecting consumer emotions in real time using sensors and making personalized product suggestions based on that data. This enables flexible adjustment of production and logistics plans based on consumer emotions.

[0208] "Climate data" is a collection of weather information such as temperature, precipitation, and wind speed, and is used to predict future weather conditions.

[0209] "Consumer data" refers to information about past purchase history and consumer behavior, and is useful for predicting future demand.

[0210] A "production plan" is a plan that determines the production volume and schedule of goods and services based on demand forecasts, and serves as a guideline for building an efficient supply chain.

[0211] "Inventory management" refers to methods for maintaining and adjusting the appropriate amount of inventory of goods, and plays an important role in preventing supply shortages and surpluses.

[0212] A "logistics route" refers to the path used for product delivery and is the basic unit for achieving efficient delivery planning.

[0213] "User feedback" refers to opinions and impressions provided by consumers regarding products and services. By analyzing this feedback, it is possible to incorporate it into demand forecasting and promotional activities.

[0214] A "sensor" is a device that detects the external environment and conditions and captures them as data, and is used to measure emotions and behavior in real time.

[0215] "Emotional data" refers to information that expresses consumers' emotional states as numerical values ​​or categories, and is used in marketing strategies and product recommendations.

[0216] "Personalized product recommendations" refer to suggestions for products and services that are customized to the needs and preferences of each individual consumer, and are a means of improving customer satisfaction.

[0217] The system that realizes this invention consists of a server, terminals, and users. The server collects information via APIs from weather data providers to acquire climate data and analyzes data such as temperature, precipitation, and wind speed. Furthermore, it performs demand forecasting using machine learning algorithms based on past consumption data. Based on this forecast data, it optimizes production planning and inventory management. In addition, the server develops a delivery plan to determine the shortest and most efficient logistics route based on the demand forecast.

[0218] The terminal plays a role in collecting user feedback, which includes information on consumption patterns and new demand. The terminal sends this data to a server to update the feedback database. This data is used to update the demand forecasting model.

[0219] Furthermore, the device is equipped with a camera and other sensors, enabling it to analyze consumers' facial expressions and generate emotional data in real time. This allows for personalized product recommendations tailored to each consumer's needs. For example, if a user shows a satisfied expression while browsing a particular product in a store, they will receive a notification on their smartphone about a promotion related to that product.

[0220] An example of a prompt message would be, "If a user displays a satisfied expression while viewing a specific product, notify them of an offer related to that product." This would then be sent to the generating AI model. This would enable the implementation of emotion-based marketing strategies, leading to improved consumer satisfaction.

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The server retrieves climate data from weather data providers' APIs. This data includes information such as temperature, precipitation, and wind speed. It analyzes this data and uses a climate prediction model to forecast future weather. It receives climate data as input and outputs the results of analyzing the data trends. Specifically, it performs time-series analysis to predict future weather patterns.

[0224] Step 2:

[0225] The server collects historical consumption data and applies machine learning algorithms to forecast demand. The input data is past consumption history, which is used to analyze demand patterns and derive future demand forecasts. The output is data predicting future consumption trends. Specifically, it performs regression analysis to predict consumption trends.

[0226] Step 3:

[0227] The device detects the user's facial expressions using sensors, and an emotion engine generates emotion data based on that data. The input is the user's facial expressions captured by the camera, and the output is the analyzed emotion category information. Specifically, it uses image recognition technology to convert the features of the facial expressions into an emotion score.

[0228] Step 4:

[0229] The server generates personalized product recommendations for users based on the collected emotional data. It uses emotional data and inventory information as input and generates appropriate product recommendations and promotional information as output. For example, it processes notifications of special offers for products in which the user has shown satisfaction.

[0230] Step 5:

[0231] Users provide product feedback via their devices, and the devices send this information to the server. The system receives user feedback as input and updates the feedback database as output. Specifically, it accepts text input through the user interface and transmits it to the server via the network.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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".

[0248] This invention is a system that optimizes food production and distribution using climate data and market data. The system consists of a server, terminals, and users, and operates as follows:

[0249] Collection and analysis of climate data

[0250] The server communicates with external weather information providers and periodically retrieves climate data via APIs. This data is analyzed using machine learning models and used to predict future weather patterns in specific areas. These predictions are a crucial factor in optimizing production plans.

[0251] Market data analysis and demand forecasting

[0252] The server retrieves historical consumption data from the database and uses statistical analysis methods and machine learning algorithms to forecast demand. This demand forecast is used to understand future consumption trends.

[0253] Production planning

[0254] The server develops optimal production plans for each production site based on climate and demand forecasts. This prevents overproduction and inventory shortages, ensuring efficient production activities. The production plans are updated in real time and adjusted as needed.

[0255] Inventory management and delivery planning

[0256] Based on the production plan, the server manages inventory data and optimizes inventory levels at distribution centers across the country. Furthermore, it calculates the optimal delivery route to improve logistics efficiency. Traffic and weather information are also taken into consideration to ensure deliveries are made at the best possible time.

[0257] User feedback and dynamic adjustments

[0258] Users input consumption information and additional demand data through their terminals. This information is sent to the server and stored in a feedback database. The server uses this data to re-evaluate demand forecasts and reflect them in production and delivery plans. This dynamic adjustment ensures that food supply is always optimized based on the latest information.

[0259] As a concrete example, consider a scenario where rising temperatures are predicted in a certain region, resulting in an expected increase in demand for a specific item (e.g., frozen desserts). In this system, the server uses this information to modify the production schedule and instructs increased production to meet the demand. Furthermore, the delivery routes are optimized to ensure that the item is supplied appropriately. In this way, efficient and flexible food supply is achieved.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] The server accesses APIs from weather information providers to periodically retrieve the latest climate data. The retrieved data, including temperature, precipitation, and wind speed, is stored in a database.

[0263] Step 2:

[0264] The server uses machine learning models to analyze stored climate data. This allows it to predict future weather patterns in specific regions, and the results are used as input for production planning.

[0265] Step 3:

[0266] The server extracts historical consumption data from a market database and performs demand forecasting by executing statistical analysis and machine learning algorithms. This process makes it possible to understand expected market trends and consumption patterns.

[0267] Step 4:

[0268] The server integrates analyzed climate data and demand forecasts to create an optimal production plan for each production site. The production plan is designed to avoid overproduction and inventory shortages, and resource allocation is adjusted as needed.

[0269] Step 5:

[0270] The server manages inventory levels based on production plans and monitors inventory status at each distribution center in real time. If an inventory shortage is predicted, the server immediately issues a replenishment order.

[0271] Step 6:

[0272] The server creates a delivery plan based on demand forecasts. This includes calculating the optimal delivery route and adjusting the delivery schedule to take into account traffic information and weather conditions.

[0273] Step 7:

[0274] Users input information about product consumption and new demands through their devices. The devices send this information to the server, which stores it in the database as feedback.

[0275] Step 8:

[0276] The server uses collected feedback data to update the demand forecasting model in real time. This enables dynamic adjustments to production and delivery in response to actual consumption trends on the ground.

[0277] (Example 1)

[0278] 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."

[0279] In the field of modern food production and supply, there has been a problem of supply shortages and surpluses because the impacts of climate change and market uncertainties cannot be accurately reflected. Furthermore, traditional methods have made it difficult to respond flexibly to dynamic changes in demand, making it difficult to achieve efficient inventory management and distribution planning. In addition, there has been a challenge in quickly reflecting consumer feedback, making it difficult to adjust supply to meet consumer needs.

[0280] 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.

[0281] In this invention, the server includes means comprising a processing device for collecting and analyzing climate information from external institutions, means comprising a processing device for performing demand forecasting using statistical analysis methods based on past consumption information, and processing means for integrating the predicted data of climate and market and implementing an optimization algorithm for formulating an optimized production plan. Thereby, it becomes possible to formulate and execute plans from efficient production to delivery. Also, it can respond quickly and appropriately to dynamically changing demands, suppress shortages and surpluses in supply, and achieve flexible supply that meets consumer needs.

[0282] The "external institution" refers to a public or private organization that provides data such as weather information and traffic information.

[0283] The "climate information" refers to data related to weather such as temperature, precipitation, and wind speed, which are used for prediction and analysis.

[0284] "Analysis" refers to the process of using specific methods to analyze the acquired data and derive useful information.

[0285] The "processing device" refers to hardware or software for collecting, processing, and analyzing data, and computers and servers fall under this category.

[0286] The "statistical analysis method" refers to a mathematical technique used to organize and analyze data and derive conclusions.

[0287] "Demand forecasting" refers to the process of analyzing and predicting future market demand.

[0288] The "optimization algorithm" refers to a computational procedure or method for obtaining the best result under constraint conditions.

[0289] The "production plan" refers to a production-related plan for efficiently utilizing resources over a certain period.

[0290] "Inventory management" is the process of properly managing inventory of products, materials, etc., to maintain a balance between supply and demand.

[0291] A "transportation route" refers to a travel path established to improve the efficiency of logistics and delivery.

[0292] "Feedback" refers to users' opinions and evaluations of a particular service or product, and is used for improvement and adjustments.

[0293] An "information management device" is a device or system used to manage the organization, storage, and access of data.

[0294] An "operating terminal" is a device used by a user to input information or obtain results, and includes computers and mobile devices.

[0295] This invention utilizes climate and market data to optimize the process from food production to distribution. The system consists of multiple elements, including servers, terminals, and users.

[0296] The server periodically collects climate information through API communication with external organizations. Specifically, it uses HTTP requests to obtain climate data such as temperature, precipitation, and wind speed, and stores it in a database. This is done using common API commands and request headers. Subsequently, the server uses the Python TensorFlow library to run a time-series forecasting model and perform climate predictions. The prediction results are output as a numerical model.

[0297] Furthermore, the server uses a database management system to acquire historical consumption data and performs demand forecasting through statistical analysis methods. Software libraries such as R and scikit-learn are used here. Production plans that take into account the predicted fluctuations in demand are created using an optimization algorithm with the Python PuLP library to efficiently plan the activities of each production site.

[0298] Users operate the system from a terminal using a smartphone or computer, inputting consumption information and special demand conditions. This data is immediately transmitted to the server and stored in a database as feedback data. The server uses this feedback data to re-evaluate demand forecasts, and production and delivery plans are dynamically adjusted based on these evaluations.

[0299] Furthermore, the server uses the Google Maps API to calculate the optimal logistics route and updates the delivery plan in real time. This process also takes into account actual traffic information and local weather conditions.

[0300] As a concrete example of its operation, when a rise in temperature is predicted in a certain region, it is predicted that the demand for frozen desserts will increase in that region. In this case, the server will use this information to change the production schedule and instruct production to meet the demand. Furthermore, it will calculate the optimal delivery route and implement a logistics plan to ensure that frozen desserts are supplied appropriately.

[0301] An example of a prompt for a generated AI model is: "Explain how to predict changes in demand for frozen desserts due to rising temperatures in a specific region and optimize production and delivery plans."

[0302] In this way, this invention enables efficient and flexible food supply.

[0303] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0304] Step 1:

[0305] The server collects climate information using APIs from external organizations. The input is weather data obtained through the API key network. This data is received in JSON format and stored in the server's database. Once data collection is complete, it is updated periodically.

[0306] Step 2:

[0307] The server inputs the stored climate data into the time series prediction model of TensorFlow to perform future climate predictions. The input is time series data such as past temperature, precipitation, wind speed, etc. This is applied to the model to predict the weather patterns several days ahead and output numerical prediction results. This information affects the production plan.

[0308] Step 3:

[0309] The server uses a database management system to obtain past consumption data. The input is the consumption performance data of each region. This data is analyzed using R or scikit - learn to perform demand forecasting. The output is prediction data regarding future demand, which is visualized as a demand curve or statistical graph.

[0310] Step 4:

[0311] The server integrates the climate prediction data and the demand prediction data and formulates a production plan using an optimization algorithm based on PuLP. The input is the data of climate prediction and demand prediction. In this process, the optimal production volume and timing are calculated and output as a plan document. Thereby, the schedule is distributed for each production base.

[0312] Step 5:

[0313] The server utilizes the Google Maps API to calculate the delivery route for optimizing logistics. The input is the location data and traffic information between the delivery bases. The output is to generate navigation data as an efficient delivery route. By this procedure, the optimal route is set, aiming to shorten the transportation time and reduce costs.

[0314] Step 6:

[0315] Users provide feedback on market information and unique demand information using their terminals. The input is demand information entered by the user through the application. This data is sent to the server and stored in the feedback database. The server dynamically adjusts production and delivery plans based on this information. The output is reflected in the management system as a revised plan.

[0316] In each step, the generative AI model is used to query the accuracy of the computational method through prompts and to verify the reliability of the prediction results.

[0317] (Application Example 1)

[0318] 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."

[0319] The lack of efficiency in the food production and distribution process, as well as the inability to respond quickly to real-time environmental changes, leads to unnecessary inventory and failure to meet consumer demand. Furthermore, the lack of a system that can efficiently suggest suitable products to users is a challenge.

[0320] 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.

[0321] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for suggesting optimal products to user equipment based on climate and market data. This enables efficient production and inventory management, optimization of delivery plans, and appropriate product suggestions to users.

[0322] "Climate data" refers to data that records environmental conditions such as temperature, precipitation, humidity, and wind speed in a specific region.

[0323] "Analysis" is the process of collecting data and applying statistical methods and algorithms to extract meaning from it for a specific purpose.

[0324] A "production plan" is a plan that outlines the resource allocation and operating schedule necessary to improve the efficiency of production activities.

[0325] "Demand forecasting" is the process of estimating how much of a product will be needed in the future, based on past consumption data and market trends.

[0326] "Inventory management" is the administrative task of monitoring inventory levels to ensure that an appropriate amount of goods are held and to prevent excess inventory or stockouts.

[0327] A "delivery plan" is a plan that determines routes and priorities in order to efficiently deliver goods to designated locations.

[0328] "Optimizing logistics routes" is the process of determining the optimal transportation route to deliver goods over the shortest distance and in the shortest amount of time.

[0329] "User feedback" refers to information based on users' experiences and opinions, which is used to improve services and products.

[0330] "Dynamic adjustment" means instantly changing plans and actions in response to changing circumstances.

[0331] "Proposing the optimal product based on climate and market data" refers to the process of selecting and presenting products that are suitable for the user, taking into account current weather and market demand trends.

[0332] This invention comprises a server, terminals, and users to optimize a food supply system. The server periodically acquires climate data via APIs from external weather information providers. This data is analyzed using Python and the machine learning library Scikit-learn and used to predict future weather patterns in specific areas. The server also acquires historical consumption data from a database and performs demand forecasting using statistical analysis methods and machine learning algorithms. This allows for an understanding of consumption trends and the development of efficient production plans.

[0333] Users order groceries using a provided application on their smartphones or other devices. This application sends real-time notifications to users via Firebase Cloud Messaging, suggesting the most suitable products based on weather and demand. This allows for the efficient delivery of products tailored to consumers. For example, during the daytime in summer, the app can recommend cold beverages and ice cream.

[0334] The server collects order data from users as feedback and stores it in a feedback database. This helps to re-evaluate the demand forecasting model in real time. Based on this, the server dynamically adjusts delivery plans, taking into account traffic and weather information to suggest the optimal delivery route and timing.

[0335] As a concrete example, a prompt message for the generating AI model might be: "Today's forecast is for temperatures exceeding 30°C. Special deals on cold drinks and ice cream are available. Furthermore, the most efficient delivery time is 2 PM. Order now!" In this way, the invention achieves smooth and efficient food supply and appropriate product recommendations to consumers.

[0336] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0337] Step 1:

[0338] The server retrieves climate data from a weather information provider's API. The input is climate data via the API, and the output is weather information for a specific region. The server uses Python and Scikit-learn to apply machine learning algorithms to the retrieved data to predict future weather patterns. This predicted data is then used in the next step.

[0339] Step 2:

[0340] The server retrieves historical consumption data from a database. The input is the consumption database, and the output is demand forecast data analyzed using statistical analysis and machine learning. The server runs a demand forecasting model on a generating AI model to predict future consumption trends.

[0341] Step 3:

[0342] The server integrates climate forecast data and demand forecast data to create a production plan. The input is climate forecast data and demand forecast data, and the output is an optimized production schedule. This production plan is used to prevent overproduction and inventory shortages. Specifically, it calculates production volume and timing.

[0343] Step 4:

[0344] Users using the terminal place grocery orders through the application. The input is the user's order information, and the output is order data. The data transmitted from the terminal is stored on the server. Based on this information, the server performs real-time inventory adjustments.

[0345] Step 5:

[0346] The server analyzes user order data and feedback databases to dynamically adjust delivery plans. Inputs are user feedback and order data, and output is an updated delivery schedule. It determines the optimal delivery route considering traffic and weather information.

[0347] Step 6:

[0348] The server uses a generative AI model to provide optimal product recommendations to the user. Inputs are the user profile and current weather information, and output is a customized list of recommended products. Specifically, Firebase Cloud Messaging is used to send product recommendation notifications to the user.

[0349] Step 7:

[0350] Users can place additional orders or cancellations based on notifications received on their devices. The input is the user's response, and the output is the updated order status. This prompts the server to update its demand forecasting model again and continue making appropriate real-time adjustments.

[0351] 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.

[0352] This invention is a system that combines conventional optimization methods using climate data and market data with an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users, and operates as follows:

[0353] Data collection and analysis

[0354] The server obtains climate data through APIs from weather data providers. This includes elements such as temperature, precipitation, and wind speed. The server uses this data to predict future weather. Furthermore, the server uses machine learning algorithms as a means of analyzing historical consumption data and predicting demand trends.

[0355] Utilizing the Emotion Engine

[0356] The emotion engine operates as users input feedback about a product through their device. This engine analyzes the user's verbal feedback and facial expressions to generate emotion data, which includes emotions such as satisfaction, anxiety, and anticipation. The server then analyzes this emotion data and integrates it into demand forecasting.

[0357] Optimization of production and logistics

[0358] The server integrates climate forecasts, demand forecasts, and sentiment data to create an optimal production plan. Based on this plan, production schedules for food and other products are adjusted. By more accurately understanding fluctuations in demand, it becomes possible to achieve efficient production and inventory management.

[0359] Delivery plans are created based on demand forecasts, and by further considering sentiment data, promotional activities and product suggestions are made that meet consumer expectations. The server considers road traffic information and weather in real time to ensure delivery via the shortest and most optimal route.

[0360] Using user feedback

[0361] Users use a terminal to input feedback about products. This feedback includes information about consumption patterns and new demand. The terminal sends this feedback to a server, which stores it in a feedback database. The server uses the feedback data to update demand forecasting models and dynamically adjust production and delivery plans.

[0362] For example, if many users express high levels of satisfaction with a particular product, the server predicts additional demand for that product and increases production. Conversely, if dissatisfaction data is received regarding another product, the delivery plan is adjusted to reduce delivery of that product and suggest alternatives. In this way, utilizing an emotion engine enables flexible food supply and marketing activities that take consumer emotions into account.

[0363] The following describes the processing flow.

[0364] Step 1:

[0365] The server accesses APIs from weather information providers to obtain climate data in real time. This data is stored in a database and used for subsequent climate forecasts.

[0366] Step 2:

[0367] The server analyzes stored climate data and uses machine learning models to make predictions. This generates future weather patterns, which are then used to formulate production plans.

[0368] Step 3:

[0369] The server retrieves historical consumption data from a market database and uses statistical analysis and machine learning techniques to forecast demand. This forecast is essential for predicting product consumption trends.

[0370] Step 4:

[0371] Users use a device to input feedback about their satisfaction level and any dissatisfaction with the product. The device collects this information and sends it to an emotion engine to analyze the user's emotions.

[0372] Step 5:

[0373] The emotion engine analyzes user feedback and generates emotional data based on language and facial expressions. This emotional data includes the user's satisfaction level and expectations.

[0374] Step 6:

[0375] The server incorporates the generated sentiment data into demand forecasts, improving their accuracy. This optimizes product production and supply to better meet consumer needs.

[0376] Step 7:

[0377] The server comprehensively analyzes climate forecasts, demand forecasts, and sentiment data to adjust production plans. Production schedules are designed to prevent overproduction and inventory shortages.

[0378] Step 8:

[0379] The server considers demand forecasts and sentiment data to create a delivery plan. This plan includes selecting the optimal delivery route and adjusting the timing to account for traffic conditions.

[0380] Step 9:

[0381] Each time a user provides new feedback, the server uses this information to update demand forecasting models and production / delivery plans in real time. This ensures a flexible supply chain that reflects consumer intuition and expectations.

[0382] (Example 2)

[0383] 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".

[0384] Traditional methods for optimizing production and logistics planning relied primarily on historical consumption data and climate data, making it difficult to predict user sentiment and expectations. As a result, there was insufficient flexibility to enhance consumer satisfaction, and improvements in inventory management and delivery efficiency were limited. Furthermore, updating demand forecasting models in real time was difficult, leading to delays in responding to rapid market changes.

[0385] 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.

[0386] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption information, and means for generating sentiment information based on feedback data and integrating it into the demand forecast. This enables demand forecasting based on consumer sentiment and allows for the development of flexible production and logistics plans.

[0387] "Climate data" refers to meteorological information such as temperature, precipitation, and wind speed, and is used for forecasting and planning.

[0388] "Consumer information" refers to data on past sales volume and purchasing trends of products, and is the basis for demand forecasting.

[0389] "Demand forecasting" is the process of predicting future consumer trends based on past consumption information and other related data.

[0390] "Feedback data" refers to data such as opinions and impressions about products collected from users, and is information that can be used to improve products and services.

[0391] "Emotional information" refers to information that represents the emotional state of users by analyzing user feedback data, and is used for demand forecasting and the formulation of marketing strategies.

[0392] "Generated prompts" are instructions or questions that are automatically generated to dynamically update the prediction model.

[0393] "Production planning" refers to a specific plan for optimizing the production volume and timing of a product.

[0394] "Logistics route" refers to a route plan used to determine the delivery route and schedule for goods.

[0395] This invention optimizes production and logistics planning by integrating climate data, consumption information, and sentiment information using a system centered on servers, terminals, and users. Specific embodiments are described below.

[0396] The server first accesses an API from an external weather data provider to obtain climate data. This data includes temperature, precipitation, wind speed, etc., and the server uses this to analyze weather patterns. The server can use machine learning software such as scikit-learn or TensorFlow for the analysis.

[0397] Next, the server retrieves past consumption information from a database and uses machine learning algorithms to forecast demand. Specific libraries that may be used include scikit-learn's Random Forest and TensorFlow's neural network models.

[0398] Through the terminal, users input feedback on products. This feedback data is converted into emotional information through text and image analysis. The terminal is equipped with an emotion engine that extracts emotions such as satisfaction, anxiety, and expectation from the user's linguistic expressions and facial expressions. The extracted emotional information is sent to a server and integrated into demand forecasting.

[0399] The server integrates this data and develops production plans that take demand accuracy into account. It utilizes prompts generated using a generative AI model to expedite production and logistics-related decision-making.

[0400] Furthermore, the server uses the Google Maps API to determine the optimal delivery route for logistics planning, and also develops personalized promotions based on emotional information.

[0401] For example, if temperatures rise and many users express high satisfaction with a particular beverage, the server will predict increased demand for that beverage and issue instructions to increase production. It will also generate prompts such as, "Analyze people's feelings towards this product and propose the next sales strategy," in order to refine the market strategy.

[0402] In this way, the system effectively utilizes data through the collaboration of servers, terminals, and users, thereby improving the accuracy of consumer trend predictions.

[0403] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0404] Step 1:

[0405] The server obtains climate data by sending API requests to external weather data providers. It uses an API key and necessary data parameters as input. The obtained weather information (temperature, precipitation, wind speed, etc.) is stored in a database on the server, and data processing is performed to convert it into an analyzable format. The output is data ready for analysis based on weather forecasts.

[0406] Step 2:

[0407] The server retrieves historical consumption information from the database. It uses consumption records and sales data as input. Machine learning algorithms (e.g., scikit-learn's Random Forest) are used to analyze this data and predict future demand. The predicted demand data is output, which forms the basis of production planning.

[0408] Step 3:

[0409] Users enter feedback about a product through their device. This feedback may include text and photos. The entered feedback is processed by an emotion engine on the device and converted into the user's emotional information (satisfaction, anxiety, expectations, etc.). Emotional data is output and this information is sent to the server.

[0410] Step 4:

[0411] The server integrates weather data, demand forecasts, and sentiment information to formulate a production plan. It uses various data obtained in previous steps as input. Utilizing a generative AI model, it generates prompt messages and automates the adjustment of the production schedule. The output is an optimized production plan. Based on these results, specific instructions are issued to the production department.

[0412] Step 5:

[0413] The server optimizes logistics plans based on demand forecasts and sentiment information. Inputs include generated production plans and real-time traffic data. It utilizes the Google Maps API to calculate the shortest route and determines logistics routes that take promotional activities into consideration. The final output is an efficient and optimized delivery plan.

[0414] Step 6:

[0415] Based on user feedback and improvements to demand forecast accuracy, the server dynamically updates the forecasting model using generated prompts. The input consists of user feedback and data from the existing model, while the output is the updated demand forecasting model. This model further enhances the accuracy of analysis in subsequent processes.

[0416] (Application Example 2)

[0417] 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."

[0418] While systems have traditionally used climate data and historical consumption data for production planning and demand forecasting, they have the drawback of failing to take into account demand fluctuations based on consumer sentiment. This makes it difficult to flexibly adjust production and logistics plans, potentially leading to decreased consumer satisfaction and lost opportunities. Therefore, there is a need to provide a system that can meet consumer expectations by integrating real-time consumer sentiment data, personalizing product recommendations, and optimizing production and logistics flexibly.

[0419] 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.

[0420] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for detecting consumer emotions in real time using sensors and making personalized product suggestions based on that data. This enables flexible adjustment of production and logistics plans based on consumer emotions.

[0421] "Climate data" is a collection of weather information such as temperature, precipitation, and wind speed, and is used to predict future weather conditions.

[0422] "Consumer data" refers to information about past purchase history and consumer behavior, and is useful for predicting future demand.

[0423] A "production plan" is a plan that determines the production volume and schedule of goods and services based on demand forecasts, and serves as a guideline for building an efficient supply chain.

[0424] "Inventory management" refers to methods for maintaining and adjusting the appropriate amount of inventory of goods, and plays an important role in preventing supply shortages and surpluses.

[0425] A "logistics route" refers to the path used for product delivery and is the basic unit for achieving efficient delivery planning.

[0426] "User feedback" refers to opinions and impressions provided by consumers regarding products and services. By analyzing this feedback, it is possible to incorporate it into demand forecasting and promotional activities.

[0427] A "sensor" is a device that detects the external environment and conditions and captures them as data, and is used to measure emotions and behavior in real time.

[0428] "Emotional data" refers to information that expresses consumers' emotional states as numerical values ​​or categories, and is used in marketing strategies and product recommendations.

[0429] "Personalized product recommendations" refer to suggestions for products and services that are customized to the needs and preferences of each individual consumer, and are a means of improving customer satisfaction.

[0430] The system that realizes this invention consists of a server, terminals, and users. The server collects information via APIs from weather data providers to acquire climate data and analyzes data such as temperature, precipitation, and wind speed. Furthermore, it performs demand forecasting using machine learning algorithms based on past consumption data. Based on this forecast data, it optimizes production planning and inventory management. In addition, the server develops a delivery plan to determine the shortest and most efficient logistics route based on the demand forecast.

[0431] The terminal plays a role in collecting user feedback, which includes information on consumption patterns and new demand. The terminal sends this data to a server to update the feedback database. This data is used to update the demand forecasting model.

[0432] Furthermore, the device is equipped with a camera and other sensors, enabling it to analyze consumers' facial expressions and generate emotional data in real time. This allows for personalized product recommendations tailored to each consumer's needs. For example, if a user shows a satisfied expression while browsing a particular product in a store, they will receive a notification on their smartphone about a promotion related to that product.

[0433] An example of a prompt message would be, "If a user displays a satisfied expression while viewing a specific product, notify them of an offer related to that product." This would then be sent to the generating AI model. This would enable the implementation of emotion-based marketing strategies, leading to improved consumer satisfaction.

[0434] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0435] Step 1:

[0436] The server retrieves climate data from weather data providers' APIs. This data includes information such as temperature, precipitation, and wind speed. It analyzes this data and uses a climate prediction model to forecast future weather. It receives climate data as input and outputs the results of analyzing the data trends. Specifically, it performs time-series analysis to predict future weather patterns.

[0437] Step 2:

[0438] The server collects historical consumption data and applies machine learning algorithms to forecast demand. The input data is past consumption history, which is used to analyze demand patterns and derive future demand forecasts. The output is data predicting future consumption trends. Specifically, it performs regression analysis to predict consumption trends.

[0439] Step 3:

[0440] The device detects the user's facial expressions using sensors, and an emotion engine generates emotion data based on that data. The input is the user's facial expressions captured by the camera, and the output is the analyzed emotion category information. Specifically, it uses image recognition technology to convert the features of the facial expressions into an emotion score.

[0441] Step 4:

[0442] The server generates personalized product recommendations for users based on the collected emotional data. It uses emotional data and inventory information as input and generates appropriate product recommendations and promotional information as output. For example, it processes notifications of special offers for products in which the user has shown satisfaction.

[0443] Step 5:

[0444] Users provide product feedback via their devices, and the devices send this information to the server. The system receives user feedback as input and updates the feedback database as output. Specifically, it accepts text input through the user interface and transmits it to the server via the network.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] This invention is a system that optimizes food production and distribution using climate data and market data. The system consists of a server, terminals, and users, and operates as follows:

[0462] Collection and analysis of climate data

[0463] The server communicates with external weather information providers and periodically retrieves climate data via APIs. This data is analyzed using machine learning models and used to predict future weather patterns in specific areas. These predictions are a crucial factor in optimizing production plans.

[0464] Market data analysis and demand forecasting

[0465] The server retrieves historical consumption data from the database and uses statistical analysis methods and machine learning algorithms to forecast demand. This demand forecast is used to understand future consumption trends.

[0466] Production planning

[0467] The server develops optimal production plans for each production site based on climate and demand forecasts. This prevents overproduction and inventory shortages, ensuring efficient production activities. The production plans are updated in real time and adjusted as needed.

[0468] Inventory management and delivery planning

[0469] Based on the production plan, the server manages inventory data and optimizes inventory levels at distribution centers across the country. Furthermore, it calculates the optimal delivery route to improve logistics efficiency. Traffic and weather information are also taken into consideration to ensure deliveries are made at the best possible time.

[0470] User feedback and dynamic adjustments

[0471] Users input consumption information and additional demand data through their terminals. This information is sent to the server and stored in a feedback database. The server uses this data to re-evaluate demand forecasts and reflect them in production and delivery plans. This dynamic adjustment ensures that food supply is always optimized based on the latest information.

[0472] As a concrete example, consider a scenario where rising temperatures are predicted in a certain region, resulting in an expected increase in demand for a specific item (e.g., frozen desserts). In this system, the server uses this information to modify the production schedule and instructs increased production to meet the demand. Furthermore, the delivery routes are optimized to ensure that the item is supplied appropriately. In this way, efficient and flexible food supply is achieved.

[0473] The following describes the processing flow.

[0474] Step 1:

[0475] The server accesses APIs from weather information providers to periodically retrieve the latest climate data. The retrieved data, including temperature, precipitation, and wind speed, is stored in a database.

[0476] Step 2:

[0477] The server uses machine learning models to analyze stored climate data. This allows it to predict future weather patterns in specific regions, and the results are used as input for production planning.

[0478] Step 3:

[0479] The server extracts historical consumption data from a market database and performs demand forecasting by executing statistical analysis and machine learning algorithms. This process makes it possible to understand expected market trends and consumption patterns.

[0480] Step 4:

[0481] The server integrates analyzed climate data and demand forecasts to create an optimal production plan for each production site. The production plan is designed to avoid overproduction and inventory shortages, and resource allocation is adjusted as needed.

[0482] Step 5:

[0483] The server manages inventory levels based on production plans and monitors inventory status at each distribution center in real time. If an inventory shortage is predicted, the server immediately issues a replenishment order.

[0484] Step 6:

[0485] The server creates a delivery plan based on demand forecasts. This includes calculating the optimal delivery route and adjusting the delivery schedule to take into account traffic information and weather conditions.

[0486] Step 7:

[0487] Users input information about product consumption and new demands through their devices. The devices send this information to the server, which stores it in the database as feedback.

[0488] Step 8:

[0489] The server uses collected feedback data to update the demand forecasting model in real time. This enables dynamic adjustments to production and delivery in response to actual consumption trends on the ground.

[0490] (Example 1)

[0491] 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."

[0492] In the field of modern food production and supply, there has been a problem of supply shortages and surpluses because the impacts of climate change and market uncertainties cannot be accurately reflected. Furthermore, traditional methods have made it difficult to respond flexibly to dynamic changes in demand, making it difficult to achieve efficient inventory management and distribution planning. In addition, there has been a challenge in quickly reflecting consumer feedback, making it difficult to adjust supply to meet consumer needs.

[0493] 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.

[0494] In this invention, the server includes means for a processing device that collects and analyzes climate information from external organizations, means for a processing device that performs demand forecasting using statistical analysis methods based on past consumption information, and processing means that implements an optimization algorithm for integrating climate and market forecast data and formulating an optimized production plan. This enables efficient planning and execution from production to delivery. Furthermore, it allows for a quick and appropriate response to dynamically changing demand, suppresses supply surpluses and shortages, and realizes flexible supply tailored to consumer needs.

[0495] An "external organization" refers to a public or private organization that provides data such as weather information and traffic information.

[0496] "Climate information" refers to weather-related data such as temperature, precipitation, and wind speed, which is used for forecasting and analysis.

[0497] "Analysis" is the process of using specific methods to analyze acquired data and derive useful information.

[0498] A "processing device" refers to hardware or software used for collecting, processing, and analyzing data, and includes computers and servers.

[0499] "Statistical analysis methods" are mathematical techniques used to organize and analyze data and draw conclusions.

[0500] "Demand forecasting" is the process of analyzing and predicting future market demand.

[0501] An "optimization algorithm" refers to a computational procedure or method for obtaining the best possible result within given constraints.

[0502] A "production plan" is a plan for production aimed at efficiently utilizing resources over a certain period.

[0503] "Inventory management" is the process of properly managing inventory of products, materials, etc., to maintain a balance between supply and demand.

[0504] A "transportation route" refers to a travel path established to improve the efficiency of logistics and delivery.

[0505] "Feedback" refers to users' opinions and evaluations of a particular service or product, and is used for improvement and adjustments.

[0506] An "information management device" is a device or system used to manage the organization, storage, and access of data.

[0507] An "operating terminal" is a device used by a user to input information or obtain results, and includes computers and mobile devices.

[0508] This invention utilizes climate and market data to optimize the process from food production to distribution. The system consists of multiple elements, including servers, terminals, and users.

[0509] The server periodically collects climate information through API communication with external organizations. Specifically, it uses HTTP requests to obtain climate data such as temperature, precipitation, and wind speed, and stores it in a database. This is done using common API commands and request headers. Subsequently, the server uses the Python TensorFlow library to run a time-series forecasting model and perform climate predictions. The prediction results are output as a numerical model.

[0510] Furthermore, the server uses a database management system to acquire historical consumption data and performs demand forecasting through statistical analysis methods. Software libraries such as R and scikit-learn are used here. Production plans that take into account the predicted fluctuations in demand are created using an optimization algorithm with the Python PuLP library to efficiently plan the activities of each production site.

[0511] Users operate the system from a terminal using a smartphone or computer, inputting consumption information and special demand conditions. This data is immediately transmitted to the server and stored in a database as feedback data. The server uses this feedback data to re-evaluate demand forecasts, and production and delivery plans are dynamically adjusted based on these evaluations.

[0512] Furthermore, the server uses the Google Maps API to calculate the optimal logistics route and updates the delivery plan in real time. This process also takes into account actual traffic information and local weather conditions.

[0513] As a concrete example of its operation, when a rise in temperature is predicted in a certain region, it is predicted that the demand for frozen desserts will increase in that region. In this case, the server will use this information to change the production schedule and instruct production to meet the demand. Furthermore, it will calculate the optimal delivery route and implement a logistics plan to ensure that frozen desserts are supplied appropriately.

[0514] An example of a prompt for a generated AI model is: "Explain how to predict changes in demand for frozen desserts due to rising temperatures in a specific region and optimize production and delivery plans."

[0515] In this way, this invention enables efficient and flexible food supply.

[0516] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0517] Step 1:

[0518] The server collects climate information using APIs from external organizations. The input is weather data obtained through the API key network. This data is received in JSON format and stored in the server's database. Once data collection is complete, it is updated periodically.

[0519] Step 2:

[0520] The server inputs stored climate data into a TensorFlow time-series forecasting model to make future climate predictions. The input consists of time-series data such as past temperature, precipitation, and wind speed. This data is then fed into the model to predict weather patterns several days in advance, outputting numerical prediction results. This information influences production planning.

[0521] Step 3:

[0522] The server uses a database management system to retrieve historical consumption data. The input is consumption data for each region. This data is analyzed using R or scikit-learn to forecast demand. The output is forecast data regarding future demand, which is visualized as demand curves and statistical graphs.

[0523] Step 4:

[0524] The server integrates climate forecast data and demand forecast data and develops a production plan using an optimization algorithm with PuLP. The inputs are climate forecast and demand forecast data. This process calculates the optimal production volume and timing and outputs it as a plan. This schedule is then distributed to each production site.

[0525] Step 5:

[0526] The server uses the Google Maps API to calculate delivery routes for logistics optimization. Inputs include location data and traffic information between delivery points. Outputs navigation data representing efficient delivery routes. This process ensures optimal routes are set, reducing transportation time and costs.

[0527] Step 6:

[0528] Users provide feedback on market information and unique demand information using their terminals. The input is demand information entered by the user through the application. This data is sent to the server and stored in the feedback database. The server dynamically adjusts production and delivery plans based on this information. The output is reflected in the management system as a revised plan.

[0529] In each step, the generative AI model is used to query the accuracy of the computational method through prompts and to verify the reliability of the prediction results.

[0530] (Application Example 1)

[0531] 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."

[0532] The lack of efficiency in the food production and distribution process, as well as the inability to respond quickly to real-time environmental changes, leads to unnecessary inventory and failure to meet consumer demand. Furthermore, the lack of a system that can efficiently suggest suitable products to users is a challenge.

[0533] 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.

[0534] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for suggesting optimal products to user equipment based on climate and market data. This enables efficient production and inventory management, optimization of delivery plans, and appropriate product suggestions to users.

[0535] "Climate data" refers to data that records environmental conditions such as temperature, precipitation, humidity, and wind speed in a specific region.

[0536] "Analysis" is the process of collecting data and applying statistical methods and algorithms to extract meaning from it for a specific purpose.

[0537] A "production plan" is a plan that outlines the resource allocation and operating schedule necessary to improve the efficiency of production activities.

[0538] "Demand forecasting" is the process of estimating how much of a product will be needed in the future, based on past consumption data and market trends.

[0539] "Inventory management" is the administrative task of monitoring inventory levels to ensure that an appropriate amount of goods are held and to prevent excess inventory or stockouts.

[0540] A "delivery plan" is a plan that determines routes and priorities in order to efficiently deliver goods to designated locations.

[0541] "Optimizing logistics routes" is the process of determining the optimal transportation route to deliver goods over the shortest distance and in the shortest amount of time.

[0542] "User feedback" refers to information based on users' experiences and opinions, which is used to improve services and products.

[0543] "Dynamic adjustment" means instantly changing plans and actions in response to changing circumstances.

[0544] "Proposing the optimal product based on climate and market data" refers to the process of selecting and presenting products that are suitable for the user, taking into account current weather and market demand trends.

[0545] This invention comprises a server, terminals, and users to optimize a food supply system. The server periodically acquires climate data via APIs from external weather information providers. This data is analyzed using Python and the machine learning library Scikit-learn and used to predict future weather patterns in specific areas. The server also acquires historical consumption data from a database and performs demand forecasting using statistical analysis methods and machine learning algorithms. This allows for an understanding of consumption trends and the development of efficient production plans.

[0546] Users order groceries using a provided application on their smartphones or other devices. This application sends real-time notifications to users via Firebase Cloud Messaging, suggesting the most suitable products based on weather and demand. This allows for the efficient delivery of products tailored to consumers. For example, during the daytime in summer, the app can recommend cold beverages and ice cream.

[0547] The server collects order data from users as feedback and stores it in a feedback database. This helps to re-evaluate the demand forecasting model in real time. Based on this, the server dynamically adjusts delivery plans, taking into account traffic and weather information to suggest the optimal delivery route and timing.

[0548] As a concrete example, a prompt message for the generating AI model might be: "Today's forecast is for temperatures exceeding 30°C. Special deals on cold drinks and ice cream are available. Furthermore, the most efficient delivery time is 2 PM. Order now!" In this way, the invention achieves smooth and efficient food supply and appropriate product recommendations to consumers.

[0549] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0550] Step 1:

[0551] The server retrieves climate data from a weather information provider's API. The input is climate data via the API, and the output is weather information for a specific region. The server uses Python and Scikit-learn to apply machine learning algorithms to the retrieved data to predict future weather patterns. This predicted data is then used in the next step.

[0552] Step 2:

[0553] The server retrieves historical consumption data from a database. The input is the consumption database, and the output is demand forecast data analyzed using statistical analysis and machine learning. The server runs a demand forecasting model on a generating AI model to predict future consumption trends.

[0554] Step 3:

[0555] The server integrates climate forecast data and demand forecast data to create a production plan. The input is climate forecast data and demand forecast data, and the output is an optimized production schedule. This production plan is used to prevent overproduction and inventory shortages. Specifically, it calculates production volume and timing.

[0556] Step 4:

[0557] Users using the terminal place grocery orders through the application. The input is the user's order information, and the output is order data. The data transmitted from the terminal is stored on the server. Based on this information, the server performs real-time inventory adjustments.

[0558] Step 5:

[0559] The server analyzes user order data and feedback databases to dynamically adjust delivery plans. Inputs are user feedback and order data, and output is an updated delivery schedule. It determines the optimal delivery route considering traffic and weather information.

[0560] Step 6:

[0561] The server uses a generative AI model to provide optimal product recommendations to the user. Inputs are the user profile and current weather information, and output is a customized list of recommended products. Specifically, Firebase Cloud Messaging is used to send product recommendation notifications to the user.

[0562] Step 7:

[0563] Users can place additional orders or cancellations based on notifications received on their devices. The input is the user's response, and the output is the updated order status. This prompts the server to update its demand forecasting model again and continue making appropriate real-time adjustments.

[0564] 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.

[0565] This invention is a system that combines conventional optimization methods using climate data and market data with an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users, and operates as follows:

[0566] Data collection and analysis

[0567] The server obtains climate data through APIs from weather data providers. This includes elements such as temperature, precipitation, and wind speed. The server uses this data to predict future weather. Furthermore, the server uses machine learning algorithms as a means of analyzing historical consumption data and predicting demand trends.

[0568] Utilizing the Emotion Engine

[0569] The emotion engine operates as users input feedback about a product through their device. This engine analyzes the user's verbal feedback and facial expressions to generate emotion data, which includes emotions such as satisfaction, anxiety, and anticipation. The server then analyzes this emotion data and integrates it into demand forecasting.

[0570] Optimization of production and logistics

[0571] The server integrates climate forecasts, demand forecasts, and sentiment data to create an optimal production plan. Based on this plan, production schedules for food and other products are adjusted. By more accurately understanding fluctuations in demand, it becomes possible to achieve efficient production and inventory management.

[0572] Delivery plans are created based on demand forecasts, and by further considering sentiment data, promotional activities and product suggestions are made that meet consumer expectations. The server considers road traffic information and weather in real time to ensure delivery via the shortest and most optimal route.

[0573] Using user feedback

[0574] Users use a terminal to input feedback about products. This feedback includes information about consumption patterns and new demand. The terminal sends this feedback to a server, which stores it in a feedback database. The server uses the feedback data to update demand forecasting models and dynamically adjust production and delivery plans.

[0575] For example, if many users express high levels of satisfaction with a particular product, the server predicts additional demand for that product and increases production. Conversely, if dissatisfaction data is received regarding another product, the delivery plan is adjusted to reduce delivery of that product and suggest alternatives. In this way, utilizing an emotion engine enables flexible food supply and marketing activities that take consumer emotions into account.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] The server accesses APIs from weather information providers to obtain climate data in real time. This data is stored in a database and used for subsequent climate forecasts.

[0579] Step 2:

[0580] The server analyzes stored climate data and uses machine learning models to make predictions. This generates future weather patterns, which are then used to formulate production plans.

[0581] Step 3:

[0582] The server retrieves historical consumption data from a market database and uses statistical analysis and machine learning techniques to forecast demand. This forecast is essential for predicting product consumption trends.

[0583] Step 4:

[0584] Users use a device to input feedback about their satisfaction level and any dissatisfaction with the product. The device collects this information and sends it to an emotion engine to analyze the user's emotions.

[0585] Step 5:

[0586] The emotion engine analyzes user feedback and generates emotional data based on language and facial expressions. This emotional data includes the user's satisfaction level and expectations.

[0587] Step 6:

[0588] The server incorporates the generated sentiment data into demand forecasts, improving their accuracy. This optimizes product production and supply to better meet consumer needs.

[0589] Step 7:

[0590] The server comprehensively analyzes climate forecasts, demand forecasts, and sentiment data to adjust production plans. Production schedules are designed to prevent overproduction and inventory shortages.

[0591] Step 8:

[0592] The server considers demand forecasts and sentiment data to create a delivery plan. This plan includes selecting the optimal delivery route and adjusting the timing to account for traffic conditions.

[0593] Step 9:

[0594] Each time a user provides new feedback, the server uses this information to update demand forecasting models and production / delivery plans in real time. This ensures a flexible supply chain that reflects consumer intuition and expectations.

[0595] (Example 2)

[0596] 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."

[0597] Traditional methods for optimizing production and logistics planning relied primarily on historical consumption data and climate data, making it difficult to predict user sentiment and expectations. As a result, there was insufficient flexibility to enhance consumer satisfaction, and improvements in inventory management and delivery efficiency were limited. Furthermore, updating demand forecasting models in real time was difficult, leading to delays in responding to rapid market changes.

[0598] 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.

[0599] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption information, and means for generating sentiment information based on feedback data and integrating it into the demand forecast. This enables demand forecasting based on consumer sentiment and allows for the development of flexible production and logistics plans.

[0600] "Climate data" refers to meteorological information such as temperature, precipitation, and wind speed, and is used for forecasting and planning.

[0601] "Consumer information" refers to data on past sales volume and purchasing trends of products, and is the basis for demand forecasting.

[0602] "Demand forecasting" is the process of predicting future consumer trends based on past consumption information and other related data.

[0603] "Feedback data" refers to data such as opinions and impressions about products collected from users, and is information that can be used to improve products and services.

[0604] "Emotional information" refers to information that represents the emotional state of users by analyzing user feedback data, and is used for demand forecasting and the formulation of marketing strategies.

[0605] "Generated prompts" are instructions or questions that are automatically generated to dynamically update the prediction model.

[0606] "Production planning" refers to a specific plan for optimizing the production volume and timing of a product.

[0607] "Logistics route" refers to a route plan used to determine the delivery route and schedule for goods.

[0608] This invention optimizes production and logistics planning by integrating climate data, consumption information, and sentiment information using a system centered on servers, terminals, and users. Specific embodiments are described below.

[0609] The server first accesses an API from an external weather data provider to obtain climate data. This data includes temperature, precipitation, wind speed, etc., and the server uses this to analyze weather patterns. The server can use machine learning software such as scikit-learn or TensorFlow for the analysis.

[0610] Next, the server retrieves past consumption information from a database and uses machine learning algorithms to forecast demand. Specific libraries that may be used include scikit-learn's Random Forest and TensorFlow's neural network models.

[0611] Through the terminal, users input feedback on products. This feedback data is converted into emotional information through text and image analysis. The terminal is equipped with an emotion engine that extracts emotions such as satisfaction, anxiety, and expectation from the user's linguistic expressions and facial expressions. The extracted emotional information is sent to a server and integrated into demand forecasting.

[0612] The server integrates this data and develops production plans that take demand accuracy into account. It utilizes prompts generated using a generative AI model to expedite production and logistics-related decision-making.

[0613] Furthermore, the server uses the Google Maps API to determine the optimal delivery route for logistics planning, and also develops personalized promotions based on emotional information.

[0614] For example, if temperatures rise and many users express high satisfaction with a particular beverage, the server will predict increased demand for that beverage and issue instructions to increase production. It will also generate prompts such as, "Analyze people's feelings towards this product and propose the next sales strategy," in order to refine the market strategy.

[0615] In this way, the system effectively utilizes data through the collaboration of servers, terminals, and users, thereby improving the accuracy of consumer trend predictions.

[0616] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0617] Step 1:

[0618] The server obtains climate data by sending API requests to external weather data providers. It uses an API key and necessary data parameters as input. The obtained weather information (temperature, precipitation, wind speed, etc.) is stored in a database on the server, and data processing is performed to convert it into an analyzable format. The output is data ready for analysis based on weather forecasts.

[0619] Step 2:

[0620] The server retrieves historical consumption information from the database. It uses consumption records and sales data as input. Machine learning algorithms (e.g., scikit-learn's Random Forest) are used to analyze this data and predict future demand. The predicted demand data is output, which forms the basis of production planning.

[0621] Step 3:

[0622] Users enter feedback about a product through their device. This feedback may include text and photos. The entered feedback is processed by an emotion engine on the device and converted into the user's emotional information (satisfaction, anxiety, expectations, etc.). Emotional data is output and this information is sent to the server.

[0623] Step 4:

[0624] The server integrates weather data, demand forecasts, and sentiment information to formulate a production plan. It uses various data obtained in previous steps as input. Utilizing a generative AI model, it generates prompt messages and automates the adjustment of the production schedule. The output is an optimized production plan. Based on these results, specific instructions are issued to the production department.

[0625] Step 5:

[0626] The server optimizes logistics plans based on demand forecasts and sentiment information. Inputs include generated production plans and real-time traffic data. It utilizes the Google Maps API to calculate the shortest route and determines logistics routes that take promotional activities into consideration. The final output is an efficient and optimized delivery plan.

[0627] Step 6:

[0628] Based on user feedback and improvements to demand forecast accuracy, the server dynamically updates the forecasting model using generated prompts. The input consists of user feedback and data from the existing model, while the output is the updated demand forecasting model. This model further enhances the accuracy of analysis in subsequent processes.

[0629] (Application Example 2)

[0630] 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."

[0631] While systems have traditionally used climate data and historical consumption data for production planning and demand forecasting, they have the drawback of failing to take into account demand fluctuations based on consumer sentiment. This makes it difficult to flexibly adjust production and logistics plans, potentially leading to decreased consumer satisfaction and lost opportunities. Therefore, there is a need to provide a system that can meet consumer expectations by integrating real-time consumer sentiment data, personalizing product recommendations, and optimizing production and logistics flexibly.

[0632] 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.

[0633] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for detecting consumer emotions in real time using sensors and making personalized product suggestions based on that data. This enables flexible adjustment of production and logistics plans based on consumer emotions.

[0634] "Climate data" is a collection of weather information such as temperature, precipitation, and wind speed, and is used to predict future weather conditions.

[0635] "Consumer data" refers to information about past purchase history and consumer behavior, and is useful for predicting future demand.

[0636] A "production plan" is a plan that determines the production volume and schedule of goods and services based on demand forecasts, and serves as a guideline for building an efficient supply chain.

[0637] "Inventory management" refers to methods for maintaining and adjusting the appropriate amount of inventory of goods, and plays an important role in preventing supply shortages and surpluses.

[0638] A "logistics route" refers to the path used for product delivery and is the basic unit for achieving efficient delivery planning.

[0639] "User feedback" refers to opinions and impressions provided by consumers regarding products and services. By analyzing this feedback, it is possible to incorporate it into demand forecasting and promotional activities.

[0640] A "sensor" is a device that detects the external environment and conditions and captures them as data, and is used to measure emotions and behavior in real time.

[0641] "Emotional data" refers to information that expresses consumers' emotional states as numerical values ​​or categories, and is used in marketing strategies and product recommendations.

[0642] "Personalized product recommendations" refer to suggestions for products and services that are customized to the needs and preferences of each individual consumer, and are a means of improving customer satisfaction.

[0643] The system that realizes this invention consists of a server, terminals, and users. The server collects information via APIs from weather data providers to acquire climate data and analyzes data such as temperature, precipitation, and wind speed. Furthermore, it performs demand forecasting using machine learning algorithms based on past consumption data. Based on this forecast data, it optimizes production planning and inventory management. In addition, the server develops a delivery plan to determine the shortest and most efficient logistics route based on the demand forecast.

[0644] The terminal plays a role in collecting user feedback, which includes information on consumption patterns and new demand. The terminal sends this data to a server to update the feedback database. This data is used to update the demand forecasting model.

[0645] Furthermore, the device is equipped with a camera and other sensors, enabling it to analyze consumers' facial expressions and generate emotional data in real time. This allows for personalized product recommendations tailored to each consumer's needs. For example, if a user shows a satisfied expression while browsing a particular product in a store, they will receive a notification on their smartphone about a promotion related to that product.

[0646] An example of a prompt message would be, "If a user displays a satisfied expression while viewing a specific product, notify them of an offer related to that product." This would then be sent to the generating AI model. This would enable the implementation of emotion-based marketing strategies, leading to improved consumer satisfaction.

[0647] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0648] Step 1:

[0649] The server retrieves climate data from weather data providers' APIs. This data includes information such as temperature, precipitation, and wind speed. It analyzes this data and uses a climate prediction model to forecast future weather. It receives climate data as input and outputs the results of analyzing the data trends. Specifically, it performs time-series analysis to predict future weather patterns.

[0650] Step 2:

[0651] The server collects historical consumption data and applies machine learning algorithms to forecast demand. The input data is past consumption history, which is used to analyze demand patterns and derive future demand forecasts. The output is data predicting future consumption trends. Specifically, it performs regression analysis to predict consumption trends.

[0652] Step 3:

[0653] The device detects the user's facial expressions using sensors, and an emotion engine generates emotion data based on that data. The input is the user's facial expressions captured by the camera, and the output is the analyzed emotion category information. Specifically, it uses image recognition technology to convert the features of the facial expressions into an emotion score.

[0654] Step 4:

[0655] The server generates personalized product recommendations for users based on the collected emotional data. It uses emotional data and inventory information as input and generates appropriate product recommendations and promotional information as output. For example, it processes notifications of special offers for products in which the user has shown satisfaction.

[0656] Step 5:

[0657] Users provide product feedback via their devices, and the devices send this information to the server. The system receives user feedback as input and updates the feedback database as output. Specifically, it accepts text input through the user interface and transmits it to the server via the network.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] [Fourth Embodiment]

[0662] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0663] 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.

[0664] 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).

[0665] 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.

[0666] 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.

[0667] 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).

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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".

[0675] This invention is a system that optimizes food production and distribution using climate data and market data. The system consists of a server, terminals, and users, and operates as follows:

[0676] Collection and analysis of climate data

[0677] The server communicates with external weather information providers and periodically retrieves climate data via APIs. This data is analyzed using machine learning models and used to predict future weather patterns in specific areas. These predictions are a crucial factor in optimizing production plans.

[0678] Market data analysis and demand forecasting

[0679] The server retrieves historical consumption data from the database and uses statistical analysis methods and machine learning algorithms to forecast demand. This demand forecast is used to understand future consumption trends.

[0680] Production planning

[0681] The server develops optimal production plans for each production site based on climate and demand forecasts. This prevents overproduction and inventory shortages, ensuring efficient production activities. The production plans are updated in real time and adjusted as needed.

[0682] Inventory management and delivery planning

[0683] Based on the production plan, the server manages inventory data and optimizes inventory levels at distribution centers across the country. Furthermore, it calculates the optimal delivery route to improve logistics efficiency. Traffic and weather information are also taken into consideration to ensure deliveries are made at the best possible time.

[0684] User feedback and dynamic adjustments

[0685] Users input consumption information and additional demand data through their terminals. This information is sent to the server and stored in a feedback database. The server uses this data to re-evaluate demand forecasts and reflect them in production and delivery plans. This dynamic adjustment ensures that food supply is always optimized based on the latest information.

[0686] As a concrete example, consider a scenario where rising temperatures are predicted in a certain region, resulting in an expected increase in demand for a specific item (e.g., frozen desserts). In this system, the server uses this information to modify the production schedule and instructs increased production to meet the demand. Furthermore, the delivery routes are optimized to ensure that the item is supplied appropriately. In this way, efficient and flexible food supply is achieved.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The server accesses APIs from weather information providers to periodically retrieve the latest climate data. The retrieved data, including temperature, precipitation, and wind speed, is stored in a database.

[0690] Step 2:

[0691] The server uses machine learning models to analyze stored climate data. This allows it to predict future weather patterns in specific regions, and the results are used as input for production planning.

[0692] Step 3:

[0693] The server extracts historical consumption data from a market database and performs demand forecasting by executing statistical analysis and machine learning algorithms. This process makes it possible to understand expected market trends and consumption patterns.

[0694] Step 4:

[0695] The server integrates analyzed climate data and demand forecasts to create an optimal production plan for each production site. The production plan is designed to avoid overproduction and inventory shortages, and resource allocation is adjusted as needed.

[0696] Step 5:

[0697] The server manages inventory levels based on production plans and monitors inventory status at each distribution center in real time. If an inventory shortage is predicted, the server immediately issues a replenishment order.

[0698] Step 6:

[0699] The server creates a delivery plan based on demand forecasts. This includes calculating the optimal delivery route and adjusting the delivery schedule to take into account traffic information and weather conditions.

[0700] Step 7:

[0701] Users input information about product consumption and new demands through their devices. The devices send this information to the server, which stores it in the database as feedback.

[0702] Step 8:

[0703] The server uses collected feedback data to update the demand forecasting model in real time. This enables dynamic adjustments to production and delivery in response to actual consumption trends on the ground.

[0704] (Example 1)

[0705] 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".

[0706] In the field of modern food production and supply, there has been a problem of supply shortages and surpluses because the impacts of climate change and market uncertainties cannot be accurately reflected. Furthermore, traditional methods have made it difficult to respond flexibly to dynamic changes in demand, making it difficult to achieve efficient inventory management and distribution planning. In addition, there has been a challenge in quickly reflecting consumer feedback, making it difficult to adjust supply to meet consumer needs.

[0707] 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.

[0708] In this invention, the server includes means for a processing device that collects and analyzes climate information from external organizations, means for a processing device that performs demand forecasting using statistical analysis methods based on past consumption information, and processing means that implements an optimization algorithm for integrating climate and market forecast data and formulating an optimized production plan. This enables efficient planning and execution from production to delivery. Furthermore, it allows for a quick and appropriate response to dynamically changing demand, suppresses supply surpluses and shortages, and realizes flexible supply tailored to consumer needs.

[0709] An "external organization" refers to a public or private organization that provides data such as weather information and traffic information.

[0710] "Climate information" refers to weather-related data such as temperature, precipitation, and wind speed, which is used for forecasting and analysis.

[0711] "Analysis" is the process of using specific methods to analyze acquired data and derive useful information.

[0712] A "processing device" refers to hardware or software used for collecting, processing, and analyzing data, and includes computers and servers.

[0713] "Statistical analysis methods" are mathematical techniques used to organize and analyze data and draw conclusions.

[0714] "Demand forecasting" is the process of analyzing and predicting future market demand.

[0715] An "optimization algorithm" refers to a computational procedure or method for obtaining the best possible result within given constraints.

[0716] A "production plan" is a plan for production aimed at efficiently utilizing resources over a certain period.

[0717] "Inventory management" is the process of properly managing inventory of products, materials, etc., to maintain a balance between supply and demand.

[0718] A "transportation route" refers to a travel path established to improve the efficiency of logistics and delivery.

[0719] "Feedback" refers to users' opinions and evaluations of a particular service or product, and is used for improvement and adjustments.

[0720] An "information management device" is a device or system used to manage the organization, storage, and access of data.

[0721] An "operating terminal" is a device used by a user to input information or obtain results, and includes computers and mobile devices.

[0722] This invention utilizes climate and market data to optimize the process from food production to distribution. The system consists of multiple elements, including servers, terminals, and users.

[0723] The server periodically collects climate information through API communication with external organizations. Specifically, it uses HTTP requests to obtain climate data such as temperature, precipitation, and wind speed, and stores it in a database. This is done using common API commands and request headers. Subsequently, the server uses the Python TensorFlow library to run a time-series forecasting model and perform climate predictions. The prediction results are output as a numerical model.

[0724] Furthermore, the server uses a database management system to acquire historical consumption data and performs demand forecasting through statistical analysis methods. Software libraries such as R and scikit-learn are used here. Production plans that take into account the predicted fluctuations in demand are created using an optimization algorithm with the Python PuLP library to efficiently plan the activities of each production site.

[0725] Users operate the system from a terminal using a smartphone or computer, inputting consumption information and special demand conditions. This data is immediately transmitted to the server and stored in a database as feedback data. The server uses this feedback data to re-evaluate demand forecasts, and production and delivery plans are dynamically adjusted based on these evaluations.

[0726] Furthermore, the server uses the Google Maps API to calculate the optimal logistics route and updates the delivery plan in real time. This process also takes into account actual traffic information and local weather conditions.

[0727] As a concrete example of its operation, when a rise in temperature is predicted in a certain region, it is predicted that the demand for frozen desserts will increase in that region. In this case, the server will use this information to change the production schedule and instruct production to meet the demand. Furthermore, it will calculate the optimal delivery route and implement a logistics plan to ensure that frozen desserts are supplied appropriately.

[0728] An example of a prompt for a generated AI model is: "Explain how to predict changes in demand for frozen desserts due to rising temperatures in a specific region and optimize production and delivery plans."

[0729] In this way, this invention enables efficient and flexible food supply.

[0730] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0731] Step 1:

[0732] The server collects climate information using APIs from external organizations. The input is weather data obtained through the API key network. This data is received in JSON format and stored in the server's database. Once data collection is complete, it is updated periodically.

[0733] Step 2:

[0734] The server inputs stored climate data into a TensorFlow time-series forecasting model to make future climate predictions. The input consists of time-series data such as past temperature, precipitation, and wind speed. This data is then fed into the model to predict weather patterns several days in advance, outputting numerical prediction results. This information influences production planning.

[0735] Step 3:

[0736] The server uses a database management system to retrieve historical consumption data. The input is consumption data for each region. This data is analyzed using R or scikit-learn to forecast demand. The output is forecast data regarding future demand, which is visualized as demand curves and statistical graphs.

[0737] Step 4:

[0738] The server integrates climate forecast data and demand forecast data and develops a production plan using an optimization algorithm with PuLP. The inputs are climate forecast and demand forecast data. This process calculates the optimal production volume and timing and outputs it as a plan. This schedule is then distributed to each production site.

[0739] Step 5:

[0740] The server uses the Google Maps API to calculate delivery routes for logistics optimization. Inputs include location data and traffic information between delivery points. Outputs navigation data representing efficient delivery routes. This process ensures optimal routes are set, reducing transportation time and costs.

[0741] Step 6:

[0742] Users provide feedback on market information and unique demand information using their terminals. The input is demand information entered by the user through the application. This data is sent to the server and stored in the feedback database. The server dynamically adjusts production and delivery plans based on this information. The output is reflected in the management system as a revised plan.

[0743] In each step, the generative AI model is used to query the accuracy of the computational method through prompts and to verify the reliability of the prediction results.

[0744] (Application Example 1)

[0745] 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".

[0746] The lack of efficiency in the food production and distribution process, as well as the inability to respond quickly to real-time environmental changes, leads to unnecessary inventory and failure to meet consumer demand. Furthermore, the lack of a system that can efficiently suggest suitable products to users is a challenge.

[0747] 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.

[0748] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for suggesting optimal products to user equipment based on climate and market data. This enables efficient production and inventory management, optimization of delivery plans, and appropriate product suggestions to users.

[0749] "Climate data" refers to data that records environmental conditions such as temperature, precipitation, humidity, and wind speed in a specific region.

[0750] "Analysis" is the process of collecting data and applying statistical methods and algorithms to extract meaning from it for a specific purpose.

[0751] A "production plan" is a plan that outlines the resource allocation and operating schedule necessary to improve the efficiency of production activities.

[0752] "Demand forecasting" is the process of estimating how much of a product will be needed in the future, based on past consumption data and market trends.

[0753] "Inventory management" is the administrative task of monitoring inventory levels to ensure that an appropriate amount of goods are held and to prevent excess inventory or stockouts.

[0754] A "delivery plan" is a plan that determines routes and priorities in order to efficiently deliver goods to designated locations.

[0755] "Optimizing logistics routes" is the process of determining the optimal transportation route to deliver goods over the shortest distance and in the shortest amount of time.

[0756] "User feedback" refers to information based on users' experiences and opinions, which is used to improve services and products.

[0757] "Dynamic adjustment" means instantly changing plans and actions in response to changing circumstances.

[0758] "Proposing the optimal product based on climate and market data" refers to the process of selecting and presenting products that are suitable for the user, taking into account current weather and market demand trends.

[0759] This invention comprises a server, terminals, and users to optimize a food supply system. The server periodically acquires climate data via APIs from external weather information providers. This data is analyzed using Python and the machine learning library Scikit-learn and used to predict future weather patterns in specific areas. The server also acquires historical consumption data from a database and performs demand forecasting using statistical analysis methods and machine learning algorithms. This allows for an understanding of consumption trends and the development of efficient production plans.

[0760] Users order groceries using a provided application on their smartphones or other devices. This application sends real-time notifications to users via Firebase Cloud Messaging, suggesting the most suitable products based on weather and demand. This allows for the efficient delivery of products tailored to consumers. For example, during the daytime in summer, the app can recommend cold beverages and ice cream.

[0761] The server collects order data from users as feedback and stores it in a feedback database. This helps to re-evaluate the demand forecasting model in real time. Based on this, the server dynamically adjusts delivery plans, taking into account traffic and weather information to suggest the optimal delivery route and timing.

[0762] As a concrete example, a prompt message for the generating AI model might be: "Today's forecast is for temperatures exceeding 30°C. Special deals on cold drinks and ice cream are available. Furthermore, the most efficient delivery time is 2 PM. Order now!" In this way, the invention achieves smooth and efficient food supply and appropriate product recommendations to consumers.

[0763] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0764] Step 1:

[0765] The server retrieves climate data from a weather information provider's API. The input is climate data via the API, and the output is weather information for a specific region. The server uses Python and Scikit-learn to apply machine learning algorithms to the retrieved data to predict future weather patterns. This predicted data is then used in the next step.

[0766] Step 2:

[0767] The server retrieves historical consumption data from a database. The input is the consumption database, and the output is demand forecast data analyzed using statistical analysis and machine learning. The server runs a demand forecasting model on a generating AI model to predict future consumption trends.

[0768] Step 3:

[0769] The server integrates climate forecast data and demand forecast data to create a production plan. The input is climate forecast data and demand forecast data, and the output is an optimized production schedule. This production plan is used to prevent overproduction and inventory shortages. Specifically, it calculates production volume and timing.

[0770] Step 4:

[0771] Users using the terminal place grocery orders through the application. The input is the user's order information, and the output is order data. The data transmitted from the terminal is stored on the server. Based on this information, the server performs real-time inventory adjustments.

[0772] Step 5:

[0773] The server analyzes user order data and feedback databases to dynamically adjust delivery plans. Inputs are user feedback and order data, and output is an updated delivery schedule. It determines the optimal delivery route considering traffic and weather information.

[0774] Step 6:

[0775] The server uses a generative AI model to provide optimal product recommendations to the user. Inputs are the user profile and current weather information, and output is a customized list of recommended products. Specifically, Firebase Cloud Messaging is used to send product recommendation notifications to the user.

[0776] Step 7:

[0777] Users can place additional orders or cancellations based on notifications received on their devices. The input is the user's response, and the output is the updated order status. This prompts the server to update its demand forecasting model again and continue making appropriate real-time adjustments.

[0778] 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.

[0779] This invention is a system that combines conventional optimization methods using climate data and market data with an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users, and operates as follows:

[0780] Data collection and analysis

[0781] The server obtains climate data through APIs from weather data providers. This includes elements such as temperature, precipitation, and wind speed. The server uses this data to predict future weather. Furthermore, the server uses machine learning algorithms as a means of analyzing historical consumption data and predicting demand trends.

[0782] Utilizing the Emotion Engine

[0783] The emotion engine operates as users input feedback about a product through their device. This engine analyzes the user's verbal feedback and facial expressions to generate emotion data, which includes emotions such as satisfaction, anxiety, and anticipation. The server then analyzes this emotion data and integrates it into demand forecasting.

[0784] Optimization of production and logistics

[0785] The server integrates climate forecasts, demand forecasts, and sentiment data to create an optimal production plan. Based on this plan, production schedules for food and other products are adjusted. By more accurately understanding fluctuations in demand, it becomes possible to achieve efficient production and inventory management.

[0786] Delivery plans are created based on demand forecasts, and by further considering sentiment data, promotional activities and product suggestions are made that meet consumer expectations. The server considers road traffic information and weather in real time to ensure delivery via the shortest and most optimal route.

[0787] Using user feedback

[0788] Users use a terminal to input feedback about products. This feedback includes information about consumption patterns and new demand. The terminal sends this feedback to a server, which stores it in a feedback database. The server uses the feedback data to update demand forecasting models and dynamically adjust production and delivery plans.

[0789] For example, if many users express high levels of satisfaction with a particular product, the server predicts additional demand for that product and increases production. Conversely, if dissatisfaction data is received regarding another product, the delivery plan is adjusted to reduce delivery of that product and suggest alternatives. In this way, utilizing an emotion engine enables flexible food supply and marketing activities that take consumer emotions into account.

[0790] The following describes the processing flow.

[0791] Step 1:

[0792] The server accesses APIs from weather information providers to obtain climate data in real time. This data is stored in a database and used for subsequent climate forecasts.

[0793] Step 2:

[0794] The server analyzes stored climate data and uses machine learning models to make predictions. This generates future weather patterns, which are then used to formulate production plans.

[0795] Step 3:

[0796] The server retrieves historical consumption data from a market database and uses statistical analysis and machine learning techniques to forecast demand. This forecast is essential for predicting product consumption trends.

[0797] Step 4:

[0798] Users use a device to input feedback about their satisfaction level and any dissatisfaction with the product. The device collects this information and sends it to an emotion engine to analyze the user's emotions.

[0799] Step 5:

[0800] The emotion engine analyzes user feedback and generates emotional data based on language and facial expressions. This emotional data includes the user's satisfaction level and expectations.

[0801] Step 6:

[0802] The server incorporates the generated sentiment data into demand forecasts, improving their accuracy. This optimizes product production and supply to better meet consumer needs.

[0803] Step 7:

[0804] The server comprehensively analyzes climate forecasts, demand forecasts, and sentiment data to adjust production plans. Production schedules are designed to prevent overproduction and inventory shortages.

[0805] Step 8:

[0806] The server considers demand forecasts and sentiment data to create a delivery plan. This plan includes selecting the optimal delivery route and adjusting the timing to account for traffic conditions.

[0807] Step 9:

[0808] Each time a user provides new feedback, the server uses this information to update demand forecasting models and production / delivery plans in real time. This ensures a flexible supply chain that reflects consumer intuition and expectations.

[0809] (Example 2)

[0810] 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".

[0811] Traditional methods for optimizing production and logistics planning relied primarily on historical consumption data and climate data, making it difficult to predict user sentiment and expectations. As a result, there was insufficient flexibility to enhance consumer satisfaction, and improvements in inventory management and delivery efficiency were limited. Furthermore, updating demand forecasting models in real time was difficult, leading to delays in responding to rapid market changes.

[0812] 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.

[0813] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption information, and means for generating sentiment information based on feedback data and integrating it into the demand forecast. This enables demand forecasting based on consumer sentiment and allows for the development of flexible production and logistics plans.

[0814] "Climate data" refers to meteorological information such as temperature, precipitation, and wind speed, and is used for forecasting and planning.

[0815] "Consumer information" refers to data on past sales volume and purchasing trends of products, and is the basis for demand forecasting.

[0816] "Demand forecasting" is the process of predicting future consumer trends based on past consumption information and other related data.

[0817] "Feedback data" refers to data such as opinions and impressions about products collected from users, and is information that can be used to improve products and services.

[0818] "Emotional information" refers to information that represents the emotional state of users by analyzing user feedback data, and is used for demand forecasting and the formulation of marketing strategies.

[0819] "Generated prompts" are instructions or questions that are automatically generated to dynamically update the prediction model.

[0820] "Production planning" refers to a specific plan for optimizing the production volume and timing of a product.

[0821] "Logistics route" refers to a route plan used to determine the delivery route and schedule for goods.

[0822] This invention optimizes production and logistics planning by integrating climate data, consumption information, and sentiment information using a system centered on servers, terminals, and users. Specific embodiments are described below.

[0823] The server first accesses an API from an external weather data provider to obtain climate data. This data includes temperature, precipitation, wind speed, etc., and the server uses this to analyze weather patterns. The server can use machine learning software such as scikit-learn or TensorFlow for the analysis.

[0824] Next, the server retrieves past consumption information from a database and uses machine learning algorithms to forecast demand. Specific libraries that may be used include scikit-learn's Random Forest and TensorFlow's neural network models.

[0825] Through the terminal, users input feedback on products. This feedback data is converted into emotional information through text and image analysis. The terminal is equipped with an emotion engine that extracts emotions such as satisfaction, anxiety, and expectation from the user's linguistic expressions and facial expressions. The extracted emotional information is sent to a server and integrated into demand forecasting.

[0826] The server integrates this data and develops production plans that take demand accuracy into account. It utilizes prompts generated using a generative AI model to expedite production and logistics-related decision-making.

[0827] Furthermore, the server uses the Google Maps API to determine the optimal delivery route for logistics planning, and also develops personalized promotions based on emotional information.

[0828] For example, if temperatures rise and many users express high satisfaction with a particular beverage, the server will predict increased demand for that beverage and issue instructions to increase production. It will also generate prompts such as, "Analyze people's feelings towards this product and propose the next sales strategy," in order to refine the market strategy.

[0829] In this way, the system effectively utilizes data through the collaboration of servers, terminals, and users, thereby improving the accuracy of consumer trend predictions.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] The server obtains climate data by sending API requests to external weather data providers. It uses an API key and necessary data parameters as input. The obtained weather information (temperature, precipitation, wind speed, etc.) is stored in a database on the server, and data processing is performed to convert it into an analyzable format. The output is data ready for analysis based on weather forecasts.

[0833] Step 2:

[0834] The server retrieves historical consumption information from the database. It uses consumption records and sales data as input. Machine learning algorithms (e.g., scikit-learn's Random Forest) are used to analyze this data and predict future demand. The predicted demand data is output, which forms the basis of production planning.

[0835] Step 3:

[0836] Users enter feedback about a product through their device. This feedback may include text and photos. The entered feedback is processed by an emotion engine on the device and converted into the user's emotional information (satisfaction, anxiety, expectations, etc.). Emotional data is output and this information is sent to the server.

[0837] Step 4:

[0838] The server integrates weather data, demand forecasts, and sentiment information to formulate a production plan. It uses various data obtained in previous steps as input. Utilizing a generative AI model, it generates prompt messages and automates the adjustment of the production schedule. The output is an optimized production plan. Based on these results, specific instructions are issued to the production department.

[0839] Step 5:

[0840] The server optimizes logistics plans based on demand forecasts and sentiment information. Inputs include generated production plans and real-time traffic data. It utilizes the Google Maps API to calculate the shortest route and determines logistics routes that take promotional activities into consideration. The final output is an efficient and optimized delivery plan.

[0841] Step 6:

[0842] Based on user feedback and improvements to demand forecast accuracy, the server dynamically updates the forecasting model using generated prompts. The input consists of user feedback and data from the existing model, while the output is the updated demand forecasting model. This model further enhances the accuracy of analysis in subsequent processes.

[0843] (Application Example 2)

[0844] 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".

[0845] While systems have traditionally used climate data and historical consumption data for production planning and demand forecasting, they have the drawback of failing to take into account demand fluctuations based on consumer sentiment. This makes it difficult to flexibly adjust production and logistics plans, potentially leading to decreased consumer satisfaction and lost opportunities. Therefore, there is a need to provide a system that can meet consumer expectations by integrating real-time consumer sentiment data, personalizing product recommendations, and optimizing production and logistics flexibly.

[0846] 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.

[0847] In this invention, the server includes means for optimizing production plans by acquiring and analyzing climate data, means for forecasting demand based on past consumption data, and means for detecting consumer emotions in real time using sensors and making personalized product suggestions based on that data. This enables flexible adjustment of production and logistics plans based on consumer emotions.

[0848] "Climate data" is a collection of weather information such as temperature, precipitation, and wind speed, and is used to predict future weather conditions.

[0849] "Consumer data" refers to information about past purchase history and consumer behavior, and is useful for predicting future demand.

[0850] A "production plan" is a plan that determines the production volume and schedule of goods and services based on demand forecasts, and serves as a guideline for building an efficient supply chain.

[0851] "Inventory management" refers to methods for maintaining and adjusting the appropriate amount of inventory of goods, and plays an important role in preventing supply shortages and surpluses.

[0852] A "logistics route" refers to the path used for product delivery and is the basic unit for achieving efficient delivery planning.

[0853] "User feedback" refers to opinions and impressions provided by consumers regarding products and services. By analyzing this feedback, it is possible to incorporate it into demand forecasting and promotional activities.

[0854] A "sensor" is a device that detects the external environment and conditions and captures them as data, and is used to measure emotions and behavior in real time.

[0855] "Emotional data" refers to information that expresses consumers' emotional states as numerical values ​​or categories, and is used in marketing strategies and product recommendations.

[0856] "Personalized product recommendations" refer to suggestions for products and services that are customized to the needs and preferences of each individual consumer, and are a means of improving customer satisfaction.

[0857] The system that realizes this invention consists of a server, terminals, and users. The server collects information via APIs from weather data providers to acquire climate data and analyzes data such as temperature, precipitation, and wind speed. Furthermore, it performs demand forecasting using machine learning algorithms based on past consumption data. Based on this forecast data, it optimizes production planning and inventory management. In addition, the server develops a delivery plan to determine the shortest and most efficient logistics route based on the demand forecast.

[0858] The terminal plays a role in collecting user feedback, which includes information on consumption patterns and new demand. The terminal sends this data to a server to update the feedback database. This data is used to update the demand forecasting model.

[0859] Furthermore, the device is equipped with a camera and other sensors, enabling it to analyze consumers' facial expressions and generate emotional data in real time. This allows for personalized product recommendations tailored to each consumer's needs. For example, if a user shows a satisfied expression while browsing a particular product in a store, they will receive a notification on their smartphone about a promotion related to that product.

[0860] An example of a prompt message would be, "If a user displays a satisfied expression while viewing a specific product, notify them of an offer related to that product." This would then be sent to the generating AI model. This would enable the implementation of emotion-based marketing strategies, leading to improved consumer satisfaction.

[0861] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0862] Step 1:

[0863] The server retrieves climate data from weather data providers' APIs. This data includes information such as temperature, precipitation, and wind speed. It analyzes this data and uses a climate prediction model to forecast future weather. It receives climate data as input and outputs the results of analyzing the data trends. Specifically, it performs time-series analysis to predict future weather patterns.

[0864] Step 2:

[0865] The server collects historical consumption data and applies machine learning algorithms to forecast demand. The input data is past consumption history, which is used to analyze demand patterns and derive future demand forecasts. The output is data predicting future consumption trends. Specifically, it performs regression analysis to predict consumption trends.

[0866] Step 3:

[0867] The device detects the user's facial expressions using sensors, and an emotion engine generates emotion data based on that data. The input is the user's facial expressions captured by the camera, and the output is the analyzed emotion category information. Specifically, it uses image recognition technology to convert the features of the facial expressions into an emotion score.

[0868] Step 4:

[0869] The server generates personalized product recommendations for users based on the collected emotional data. It uses emotional data and inventory information as input and generates appropriate product recommendations and promotional information as output. For example, it processes notifications of special offers for products in which the user has shown satisfaction.

[0870] Step 5:

[0871] Users provide product feedback via their devices, and the devices send this information to the server. The system receives user feedback as input and updates the feedback database as output. Specifically, it accepts text input through the user interface and transmits it to the server via the network.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] The inside of the Emotion Map 400 represents what's in your mind, while the outside represents what you're doing. Therefore, the further you go out the 400-coordinate scale, the more visible your emotions become (the more they manifest in your actions).

[0879] 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.

[0880] 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."

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0893] The following is further disclosed regarding the embodiments described above.

[0894] (Claim 1)

[0895] A means of optimizing production plans by acquiring and analyzing climate data,

[0896] A method for forecasting demand based on past consumption data,

[0897] A means of managing inventory based on an optimized production plan,

[0898] A means of creating a delivery plan based on demand forecast results and optimizing logistics routes,

[0899] A means of collecting user feedback and dynamically adjusting production and delivery plans based on that feedback,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, further comprising means for dynamically changing delivery routes and priorities in consideration of climate information and traffic information.

[0903] (Claim 3)

[0904] The system according to claim 1, comprising a feedback database for re-evaluating a demand forecasting model in real time and a data processing means therefor.

[0905] "Example 1"

[0906] (Claim 1)

[0907] A means equipped with a processing device for optimizing production plans by collecting and analyzing climate information from external organizations,

[0908] A means for providing a processing device that performs demand forecasting using statistical analysis methods based on past consumption information,

[0909] A processing device that implements an optimization algorithm for integrating climate and market forecast data and formulating an optimized production plan,

[0910] A means equipped with an information management device for managing inventory data based on an optimized production plan,

[0911] A means comprising a computing device for creating a logistics plan based on demand forecast results and optimizing transportation routes,

[0912] A processing means for collecting user feedback via an operating terminal and dynamically adjusting production and logistics plans based on that feedback,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, further comprising processing means for dynamically changing transport routes and priorities, taking into account climate information and movement information.

[0916] (Claim 3)

[0917] The system according to claim 1, comprising a feedback data storage and data processing means for re-evaluating a demand forecasting model in real time.

[0918] "Application Example 1"

[0919] (Claim 1)

[0920] A means of optimizing production plans by acquiring and analyzing climate data,

[0921] A method for forecasting demand based on past consumption data,

[0922] A means of managing inventory based on an optimized production plan,

[0923] A means of creating a delivery plan based on demand forecast results and optimizing logistics routes,

[0924] A means of collecting user feedback and dynamically adjusting production and delivery plans based on that feedback,

[0925] A means of proposing the most suitable product for user equipment based on climate and market data,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, further comprising means for dynamically changing delivery routes and priorities in consideration of climate information and traffic information.

[0929] (Claim 3)

[0930] The system according to claim 1, comprising a feedback database for re-evaluating a demand forecasting model in real time and a data processing means therefor.

[0931] "Example 2 of combining an emotion engine"

[0932] (Claim 1)

[0933] A means of optimizing production plans by acquiring and analyzing climate data,

[0934] A method for forecasting demand based on past consumption data,

[0935] A means of managing inventory based on an optimized production plan,

[0936] A means of creating a delivery plan based on demand forecast results and optimizing logistics routes,

[0937] A means of collecting user feedback and dynamically adjusting production and delivery plans based on that feedback,

[0938] A means of generating sentiment information based on feedback data and integrating it into demand forecasting,

[0939] A means of incorporating emotional information into production and logistics planning to improve accuracy,

[0940] A means for dynamically updating the prediction model using the generated prompt statement,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, further comprising means for dynamically changing delivery routes and priorities in consideration of climate information and traffic information.

[0944] (Claim 3)

[0945] The system according to claim 1, comprising a feedback database for re-evaluating a demand forecasting model in real time and a data processing means therefor, and performing information processing utilizing sentiment information.

[0946] "Application example 2 when combining with an emotional engine"

[0947] (Claim 1)

[0948] A means of optimizing production plans by acquiring and analyzing climate data,

[0949] A method for forecasting demand based on past consumption data,

[0950] A means of managing inventory based on an optimized production plan,

[0951] A means of creating a delivery plan based on demand forecast results and optimizing logistics routes,

[0952] A means of collecting user feedback and dynamically adjusting production and delivery plans based on that feedback,

[0953] A means of detecting consumer emotions in real time using sensors and making personalized product recommendations based on that data,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, further comprising means for dynamically changing delivery routes and priorities in consideration of climate information and traffic information.

[0957] (Claim 3)

[0958] The system according to claim 1, comprising a feedback database for re-evaluating a demand forecasting model in real time and a data processing means therefor. [Explanation of symbols]

[0959] 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. A means of optimizing production plans by acquiring and analyzing climate data, A method for forecasting demand based on past consumption data, A means of managing inventory based on an optimized production plan, A means of creating a delivery plan based on demand forecast results and optimizing logistics routes, A means of collecting user feedback and dynamically adjusting production and delivery plans based on that feedback, A means of proposing the most suitable product for user equipment based on climate and market data, A system that includes this.

2. The system according to claim 1, further comprising means for dynamically changing delivery routes and priorities in consideration of climate information and traffic information.

3. The system according to claim 1, comprising a feedback database for re-evaluating a demand forecasting model in real time and a data processing means therefor.

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