system

Abstract

The system according to this embodiment aims to efficiently collect fresh ingredients, forecast demand, deliver them via the optimal route, and manage quality. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a delivery unit, a management unit, and a support unit. The collection unit provides a food catalog and an ordering system. The analysis unit performs demand forecasting based on the information collected by the collection unit. The delivery unit calculates the optimal route based on the results obtained by the analysis unit and performs delivery. The management unit manages the quality of the food delivered by the delivery unit. The support unit provides customer support based on the information managed by the management unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process of efficiently collecting fresh food ingredients, predicting demand, delivering them along an optimal route, and managing quality is complicated and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect fresh food ingredients, predict demand, deliver them along an optimal route, and manage quality.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a delivery unit, a management unit, and a support unit. The data collection unit provides a food catalog and an ordering system. The analysis unit performs demand forecasting based on the information collected by the data collection unit. The delivery unit calculates the optimal route based on the results obtained by the analysis unit and performs delivery. The management unit manages the quality of the food delivered by the delivery unit. The support unit provides customer support based on the information managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect fresh ingredients, forecast demand, deliver them via the optimal route, and manage quality. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The food supply system according to an embodiment of the present invention is a system that collects fresh food ingredients from local farmers, fishermen, and processors and delivers them to restaurants and homes in urban areas. This system provides a food ingredient catalog and ordering system through an online platform. Next, it uses AI to forecast demand and creates an optimal supply plan through data analysis. Furthermore, it calculates the optimal route and tracks it in real time to improve the efficiency of logistics and delivery. It strengthens collaboration with local producers and provides producer profile creation, training, and support. It also establishes a customer support system and collects feedback to improve the service. This mechanism realizes the provision of fresh, high-quality food ingredients, the revitalization of the local economy, and the reduction of food waste. For example, it provides a food ingredient catalog and ordering system through an online platform. Consumers can select and order fresh food ingredients provided by local farmers, fishermen, and processors. For example, seasonal vegetables, fresh seafood, and local specialties are listed in the catalog. Next, it uses AI to forecast demand. The AI ​​analyzes past order data, seasonal fluctuations, and consumer preferences to predict future demand. This makes it possible to optimize the supply plan and reduce unnecessary inventory. For example, it's possible to predict in advance which ingredients will be in high demand during specific seasons and secure the appropriate quantities. Furthermore, to improve the efficiency of logistics and delivery, it calculates optimal routes and tracks them in real time. AI optimizes delivery routes and enables efficient delivery. For example, it can calculate routes that efficiently visit multiple delivery destinations, reducing delivery times. It can also track delivery status in real time and provide consumers with the latest information. To strengthen collaboration with local producers, it provides producer profile creation, training, and support. Producers can register their profiles on the online platform and introduce their products and production methods to consumers. Training and support can also be used to improve producers' skills and quality control. Finally, a customer support system is in place to collect feedback. Consumer inquiries and complaints are addressed and used to improve services. For example, consumer feedback can be used to improve the quality of ingredients and the speed of delivery.This system ensures the provision of fresh, high-quality ingredients. Consumers can easily access fresh, locally produced ingredients and enjoy healthy and delicious meals. Furthermore, it stimulates the local economy and supports increased profits for local producers. In addition, it reduces food waste and minimizes the mismatch between local supply and demand. Thus, this ingredient supply system achieves the provision of fresh, high-quality ingredients, stimulates the local economy, and reduces food waste.

[0029] The food supply system according to the embodiment comprises a collection unit, an analysis unit, a delivery unit, a management unit, and a support unit. The collection unit provides a food catalog and an ordering system. The collection unit provides the food catalog and ordering system, for example, through an online platform. The collection unit enables consumers to easily order fresh local ingredients. The analysis unit performs demand forecasting based on the information collected by the collection unit. The analysis unit analyzes, for example, past order data, seasonal fluctuations, and consumer preferences to predict future demand. The analysis unit can improve the accuracy of demand forecasting and reduce unnecessary inventory. The delivery unit calculates the optimal route based on the results obtained by the analysis unit and performs delivery. The delivery unit calculates, for example, the optimal route and calculates a route that efficiently visits multiple delivery destinations. The delivery unit can improve delivery efficiency and shorten delivery time. The management unit manages the quality of the ingredients delivered by the delivery unit. The management unit manages the quality of the delivered ingredients and provides consumers with the latest information. The management unit provides consumers with high-quality ingredients and improves reliability. The support department provides customer support based on information managed by the management department. For example, the support department responds to inquiries and complaints from consumers and uses this information to improve services. The support department improves consumer satisfaction and service quality. As a result, the food supply system according to this embodiment can provide fresh, high-quality food ingredients, revitalize the local economy, and reduce food waste.

[0030] The Collection Department provides a food catalog and ordering system. For example, it provides the food catalog and ordering system through an online platform. Specifically, the Collection Department provides an intuitive and user-friendly interface to allow consumers to easily order fresh local ingredients. The online platform lists a wide variety of ingredients offered by local farmers and producers, and consumers can search these ingredients by category, popularity, price, etc. Furthermore, each ingredient includes detailed descriptions, nutritional information, storage instructions, and cooking methods, allowing consumers to obtain sufficient information before purchasing. The ordering system is designed to allow consumers to easily add selected ingredients to their cart and complete their order. A variety of payment methods are available, including credit cards, debit cards, e-money, and bank transfers, with multiple options for consumer convenience. In addition, the Collection Department provides a function to save consumers' order history and support reordering and creating favorites lists. This makes it easy for consumers to reorder previously purchased ingredients, improving convenience. The Collection Department also collects consumer feedback to help improve the food catalog and ordering system. For example, a feature could be provided that allows consumers to post ratings and reviews of specific food items, which other consumers can then use as a reference. This would enable the data collection department to provide services that meet consumer needs and improve satisfaction.

[0031] The analysis unit performs demand forecasting based on information collected by the data collection unit. For example, the analysis unit analyzes past order data, seasonal fluctuations, and consumer preferences to predict future demand. Specifically, the analysis unit uses AI to analyze large amounts of data and improve the accuracy of demand forecasting. The AI ​​learns demand patterns in specific seasons and events based on past order data to predict future demand. It can also analyze consumer preferences and purchase history to provide personalized suggestions to individual consumers. For example, it can suggest recommended ingredients for the next order based on ingredients frequently purchased by a particular consumer in the past or ingredients popular in a specific season. This allows the analysis unit to provide services that meet consumer needs and improve satisfaction. Furthermore, the analysis unit can optimize inventory management and procurement planning based on the demand forecasting results. For example, for ingredients predicted to have high demand, it can secure sufficient inventory in advance to reduce unnecessary stock. Conversely, for ingredients predicted to have low demand, it can reduce procurement to minimize food waste. This enables the analysis unit to achieve efficient inventory management and procurement planning, improving the overall operational efficiency of the system.

[0032] The delivery department calculates the optimal route based on the results obtained by the analysis department and carries out deliveries. For example, the delivery department calculates the optimal route and a route that efficiently visits multiple delivery destinations. Specifically, the delivery department uses AI to optimize delivery routes. The AI ​​considers traffic conditions, weather, and location information of delivery destinations to calculate the route that allows for the shortest and most efficient delivery. This allows the delivery department to improve delivery efficiency and shorten delivery times. Furthermore, the delivery department can monitor the delivery status in real time and recalculate or adjust routes as needed. For example, if an unexpected event such as a traffic jam or accident occurs, the AI ​​immediately calculates a new route and notifies the delivery driver. This allows the delivery department to always deliver using the optimal route and minimize delays. The delivery department also provides a function to notify consumers of the delivery status in real time. Consumers can check the stage of their order through an online platform or smartphone app. This allows consumers to wait for their order with peace of mind and improves customer satisfaction. In addition, the delivery department also provides functions to support safe driving by delivery drivers. For example, by providing warnings and suggesting breaks while driving, driver fatigue is reduced, ensuring safe deliveries. This allows the delivery department to conduct efficient and safe deliveries and provide high-quality service to consumers.

[0033] The Management Department manages the quality of ingredients delivered by the Delivery Department. For example, the Management Department manages the quality of delivered ingredients and provides consumers with the latest information. Specifically, the Management Department checks the quality of delivered ingredients and provides quality assurance to consumers. For example, it checks the freshness and condition of ingredients and responds quickly if there are any problems. The Management Department also provides consumers with information on how to store and cook ingredients, supporting them in using the ingredients in the best possible condition. Furthermore, the Management Department has implemented a system to ensure the traceability of ingredients. This allows consumers to check how the ingredients they purchased were produced and how they were delivered. For example, they can view producer information, production processes, and delivery history of ingredients through online platforms and smartphone apps. This allows consumers to use ingredients with peace of mind and improves reliability. The Management Department also collects feedback from consumers and uses it to improve quality control. For example, it provides a function that allows consumers to post ratings and reviews on the quality of ingredients so that other consumers can use them as a reference. This allows the Management Department to implement quality control that meets consumer needs and improves satisfaction. Furthermore, the management department analyzes data on ingredient quality and continuously improves quality control processes. For example, based on quality data related to specific ingredients or producers, they review quality control standards and implement improvement measures. This allows the management department to consistently provide high-quality ingredients and gain consumer trust.

[0034] The Support Department provides customer support based on information managed by the Management Department. For example, the Support Department handles consumer inquiries and complaints, using this information to improve services. Specifically, the Support Department operates a customer support center to respond quickly and courteously to consumer inquiries. Consumers can contact the Support Department through multiple means, such as phone, email, and chat. The Support Department provides appropriate information and solutions depending on the nature of the consumer inquiry. For example, it handles a variety of inquiries, such as questions about orders, inquiries about delivery status, and complaints about the quality of ingredients. The Support Department also collects consumer feedback to improve services. For example, it analyzes ratings and reviews provided by consumers to identify areas for service enhancement and improvement. This allows the Support Department to provide services that meet consumer needs and improve satisfaction. Furthermore, it is important for the Support Department to provide proactive support to consumers. For example, it regularly notifies consumers of new services and campaigns to ensure they stay up-to-date. It also improves trust by implementing preventative measures for problems and complaints consumers have experienced in the past. This allows the Support Department to provide high-quality support to consumers and improve the overall quality of the service.

[0035] The collection unit can provide a food catalog and ordering system through an online platform. For example, the collection unit provides a food catalog and ordering system through an online platform. The collection unit makes it easy for consumers to order fresh local ingredients. The online platform includes specific functions and configurations such as a user interface, backend system, and security measures. This allows consumers to easily order fresh local ingredients.

[0036] The analysis unit can analyze past order data, seasonal fluctuations, and consumer preferences to predict future demand. For example, the analysis unit can analyze past order data, seasonal fluctuations, and consumer preferences to predict future demand. The analysis unit can improve the accuracy of demand forecasts and reduce unnecessary inventory. Past order data includes specific details and collection methods, such as order date and time, order details, and customer information. Seasonal fluctuations include specific impacts and considerations, such as seasonal changes in demand and seasonal supply conditions. Consumer preferences include specific methods of understanding and analyzing them, such as surveys, purchase history analysis, and social media data analysis. This improves the accuracy of demand forecasts and reduces unnecessary inventory.

[0037] The delivery department can calculate the optimal route and the most efficient route to visit multiple delivery destinations. For example, the delivery department can calculate the optimal route and the most efficient route to visit multiple delivery destinations. The delivery department can improve delivery efficiency and reduce delivery time. The optimal route calculation includes methods that consider factors such as distance, time, cost, and traffic conditions. This improves delivery efficiency and reduces delivery time.

[0038] The management department can control the quality of delivered ingredients and provide consumers with up-to-date information. For example, the management department can control the quality of delivered ingredients and provide consumers with up-to-date information. The management department can provide consumers with high-quality ingredients and improve reliability. Methods for controlling quality include specific methods and standards such as the frequency of quality checks, the equipment and techniques used, and the details of quality standards. This allows for the provision of high-quality ingredients to consumers and improves reliability.

[0039] The support department can handle customer inquiries and complaints and use this information to improve services. For example, the support department can handle customer inquiries and complaints and use this information to improve services. The support department can improve customer satisfaction and service quality. The methods for handling inquiries and complaints include specific procedures and standards, such as response times and the skills of the support staff. This can improve customer satisfaction and service quality.

[0040] The data collection unit can analyze a user's past order history and prioritize displaying the most suitable ingredients. For example, the data collection unit can automatically display ingredients that the user has frequently purchased in the past as suggestions. For example, the data collection unit can predict ingredients that a user will purchase in a particular season based on their past order history and prioritize displaying them. For example, the data collection unit can analyze a user's past order history and suggest relevant recipes and cooking methods. This allows the system to display the most suitable ingredients based on the user's past order history. Past order history includes specific details such as the date and time of order, order details, and customer information, as well as the method of collection.

[0041] The data collection unit can dynamically adjust the update frequency of the food catalog in accordance with seasonal and market fluctuations. For example, the data collection unit can automatically update the catalog content according to the availability of seasonal ingredients. For example, the data collection unit can update the prices and inventory status of ingredients in real time in accordance with market price fluctuations. For example, the data collection unit can dynamically change the catalog content to coincide with specific events or campaigns. This enables the updating of the food catalog in accordance with seasonal and market fluctuations. The update frequency includes specific criteria and adjustment methods such as the timing of updates, the content of updates, and the impact of updates.

[0042] The product collection department can attract consumer interest by adding stories and background information about local producers to the food catalog. For example, the department can add pages introducing producer profiles and production methods. For example, the department can post interview videos and photos of producers to create a sense of familiarity with consumers. For example, the department can provide information about the producers' region and history to pique consumer interest. In this way, adding stories and background information about local producers can attract consumer interest. The stories and background information about local producers can include specific details such as the producer's history, cultivation methods, and local specialties, as well as collection methods.

[0043] The collection unit can add a recipe suggestion function to the ingredient catalog, providing cooking ideas using purchased ingredients. For example, the collection unit can suggest easy-to-make recipes based on purchased ingredients. For example, the collection unit can offer special recipes using seasonal ingredients. For example, the collection unit can suggest a variety of recipes depending on the combination of ingredients. This will improve consumer satisfaction by providing cooking ideas using purchased ingredients. The recipe suggestion function will include specific details and implementation methods, such as the types of recipes suggested, the criteria for suggestion, and how the recipes are displayed.

[0044] The analysis unit can improve the accuracy of demand forecasts based on weather data. For example, the analysis unit forecasts demand under specific weather conditions based on weather data. For example, the analysis unit dynamically adjusts the demand forecasting model in response to weather fluctuations. For example, the analysis unit analyzes past weather data and demand data to clarify the relationship between weather and demand. This improves the accuracy of demand forecasts based on weather data. Weather data includes specific details such as temperature, precipitation, and wind speed, as well as methods of data collection.

[0045] The analysis unit can analyze regional consumption trends and predict region-specific demand when forecasting demand. For example, the analysis unit collects regional consumption data and incorporates it into demand forecasting. For example, the analysis unit analyzes regional consumption trends and predicts demand in a specific region. For example, the analysis unit considers information such as regional events and festivals when forecasting demand. In this way, by analyzing regional consumption trends, it is possible to predict region-specific demand. Regional consumption trends include specific methods for understanding and analyzing regional purchasing data, region-specific events, and regional demographics.

[0046] The analysis unit can analyze social media trend data during demand forecasting to reflect consumer preferences. For example, the analysis unit collects social media trend data and incorporates it into demand forecasting. For example, the analysis unit analyzes social media trends to predict consumer preferences. For example, the analysis unit adjusts the demand forecasting model based on social media trend data. In this way, consumer preferences can be reflected by analyzing social media trend data. Social media trend data includes specific details and collection methods such as hashtag usage frequency, analysis of post content, and trend detection methods.

[0047] The analysis unit can predict demand for a specific event based on past event data during demand forecasting. For example, the analysis unit collects past event data and incorporates it into demand forecasting. For example, the analysis unit predicts demand for a specific event and develops a supply plan. For example, the analysis unit analyzes past event data and demand data to clarify the relationship between events and demand. This allows the analysis unit to predict demand for a specific event based on past event data. Past event data includes specific details such as the type of event, the number of participants, and the impact of the event, as well as the method of collection.

[0048] The delivery department can dynamically update the optimal route by reflecting real-time traffic data during delivery. For example, the delivery department can propose the optimal route based on real-time traffic congestion information. For example, the delivery department can propose a detour route considering real-time traffic accident information. For example, the delivery department can propose the optimal route considering the real-time operation status of public transportation. In this way, the optimal delivery route can be selected by reflecting real-time traffic data. Traffic data includes specific details and collection methods such as traffic congestion information, road construction information, and traffic accident information.

[0049] The delivery department can select routes that minimize environmental impact based on the fuel efficiency data of delivery vehicles during deliveries. For example, the delivery department can propose the optimal route based on the fuel efficiency data of delivery vehicles. For example, the delivery department can prioritize the selection of fuel-efficient routes. For example, the delivery department can propose routes that minimize environmental impact by considering fuel efficiency data. This allows for the selection of routes that minimize environmental impact based on the fuel efficiency data of delivery vehicles. Fuel efficiency data includes specific details and collection methods such as fuel efficiency data for each vehicle, mileage, and fuel consumption.

[0050] The delivery department can propose the optimal delivery time based on the consumer's availability to receive the delivery. For example, the delivery department proposes the optimal delivery time based on the consumer's availability. For example, the delivery department proposes flexible delivery times to suit the consumer's schedule. For example, the delivery department proposes efficient delivery times considering the consumer's availability. This allows the delivery department to propose the optimal delivery time based on the consumer's availability. Availability includes specific methods for determining and adjusting the user's preferred time, such as how to collect the user's preferred time and how to adjust the time slot.

[0051] The delivery department can apply algorithms to maximize the loading efficiency of delivery vehicles during delivery. For example, the delivery department proposes the optimal loading method to maximize the loading efficiency of delivery vehicles. For example, the delivery department proposes efficient routes considering the loading efficiency of delivery vehicles. For example, the delivery department applies algorithms to maximize the loading efficiency of delivery vehicles and creates a delivery plan. This enables efficient delivery by maximizing the loading efficiency of delivery vehicles. Loading efficiency includes specific calculation methods and criteria such as loadable weight, loadable volume, and loading arrangement method.

[0052] The management department can prevent quality deterioration by monitoring the storage conditions of ingredients in real time during quality control. For example, the management department can monitor the temperature and humidity of ingredients in real time to maintain an appropriate storage environment. For example, the management department can periodically check the storage conditions of ingredients to detect quality deterioration early. For example, if an abnormality occurs in the storage conditions of ingredients, the management department can immediately issue an alert and take action. In this way, quality deterioration can be prevented by monitoring the storage conditions of ingredients in real time. Storage conditions include specific monitoring methods and standards such as temperature, humidity, and storage period.

[0053] The management department can improve its quality control process by incorporating consumer feedback during quality control. For example, the management department can collect consumer feedback and incorporate it into the quality control process. For example, the management department can review and improve quality control standards based on consumer opinions. For example, the management department can analyze consumer feedback to identify quality control issues and take countermeasures. In this way, the quality control process can be improved by incorporating consumer feedback. Feedback includes specific collection and incorporation methods such as surveys, review collection, and feedback analysis methods.

[0054] The management department can provide transparency to consumers by displaying producer information for ingredients during quality control. For example, the management department can display producer information for ingredients and introduce consumers to the producer's background. For example, the management department can provide transparency to consumers by providing information on the production methods and production locations of ingredients. For example, the management department can provide trust to consumers by displaying ratings and reviews of ingredient producers. In this way, transparency can be provided to consumers by displaying producer information for ingredients. Producer information includes specific details and display methods such as the producer's name, location, cultivation methods, and certification information.

[0055] The management department can ensure the traceability of ingredients during quality control, providing consumers with peace of mind. For example, the management department can provide traceability information from the production to the delivery of ingredients. For example, the management department can update ingredient traceability information in real time to provide consumers with the latest information. For example, the management department can make the quality control process transparent based on ingredient traceability information. In this way, by ensuring the traceability of ingredients, consumers can be provided with peace of mind. Traceability includes specific methods and standards for ensuring traceability, such as tracking systems, data recording methods, and the scope of traceability.

[0056] The support department can provide prompt and accurate support based on past inquiry history. For example, the support department will respond quickly based on the user's past inquiry history. For example, the support department will refer to past inquiry content and provide accurate answers. For example, the support department will analyze past inquiry history and provide solutions to common problems. This enables prompt and accurate support based on past inquiry history. The inquiry history includes specific details such as the date and time of the inquiry, the content, and the status of the response, as well as the method of collection.

[0057] The support department can provide 24 / 7 support by utilizing an AI chatbot. For example, the support department can provide 24 / 7 support by utilizing an AI chatbot. For example, the support department can respond quickly to inquiries through the AI ​​chatbot. For example, the support department can provide automated responses to user questions by utilizing an AI chatbot. This makes 24 / 7 support possible by utilizing an AI chatbot. The AI ​​chatbot includes specific functions and implementation methods, such as the range of questions it can answer, its learning algorithm, and its user interface.

[0058] The support department can collect consumer feedback during support sessions and use it to improve services. For example, the support department can collect consumer feedback after providing support. For example, the support department can identify areas for service improvement based on consumer feedback. For example, the support department can analyze consumer feedback and use it to improve service quality. In this way, collecting consumer feedback can be used to improve services. Feedback includes specific collection methods and methods of implementation, such as surveys, review collection, and feedback analysis methods.

[0059] The support department can enhance the FAQ database and facilitate self-resolution during support. For example, the support department can refer to the FAQ database and provide answers quickly during support. For example, the support department can enhance the FAQ database to enable users to resolve issues themselves. For example, the support department can regularly update the FAQ database to provide the latest information. This enhances the FAQ database, enabling users to resolve issues themselves. The FAQ database includes specific content and methods of enhancement, such as the types of frequently asked questions, detailed answers, and how to update the database. Self-resolution includes specific methods and criteria for promoting self-resolution, such as providing user guides, instructions on how to use support tools, and methods for evaluating self-resolution.

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

[0061] The analysis unit can improve the accuracy of demand forecasts based on weather data. For example, it can predict demand under specific weather conditions based on weather data. It can also dynamically adjust the demand forecasting model in response to weather fluctuations. Furthermore, it can analyze past weather and demand data to clarify the relationship between weather and demand. This improves the accuracy of demand forecasts based on weather data.

[0062] The management department can monitor the storage conditions of ingredients in real time during quality control, preventing quality deterioration. For example, it can monitor the temperature and humidity of ingredients in real time to maintain an appropriate storage environment. It can also regularly check the storage conditions of ingredients to detect quality deterioration early. Furthermore, if an abnormality occurs in the storage conditions of ingredients, an alert can be immediately issued, allowing for corrective action. In this way, quality deterioration can be prevented by monitoring the storage conditions of ingredients in real time.

[0063] The collection department can attract consumer interest by adding stories and background information about local producers to the food catalog. For example, they can add pages introducing producer profiles and production methods. They can also include interview videos and photos of producers to create a sense of familiarity with consumers. Furthermore, they can provide information about the producers' regions and history to pique consumer interest. In short, by adding stories and background information about local producers, they can attract consumer interest.

[0064] The delivery department can dynamically update the optimal route by reflecting real-time traffic data during delivery. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest detour routes considering real-time traffic accident information. Furthermore, it can suggest the optimal route considering the real-time operation status of public transportation. In this way, the optimal delivery route can be selected by reflecting real-time traffic data.

[0065] The support department can provide 24 / 7 support by utilizing an AI chatbot. For example, they can provide 24 / 7 support using an AI chatbot. They can also respond to inquiries quickly through the AI ​​chatbot. Furthermore, they can provide automated responses to user questions using the AI ​​chatbot. This makes 24 / 7 support possible by utilizing the AI ​​chatbot.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection department provides a food catalog and ordering system. The collection department provides a food catalog and ordering system, for example, through an online platform, making it easy for consumers to order fresh local ingredients. Step 2: The analysis unit performs demand forecasting based on the information collected by the data collection unit. The analysis unit analyzes, for example, past order data, seasonal fluctuations, and consumer preferences to predict future demand. This improves the accuracy of demand forecasting and reduces unnecessary inventory. Step 3: The delivery unit calculates the optimal route based on the results obtained by the analysis unit and carries out the delivery. For example, the delivery unit calculates the optimal route and a route that efficiently visits multiple delivery destinations. This improves the efficiency of delivery and reduces delivery time. Step 4: The management department manages the quality of ingredients delivered by the delivery department. For example, the management department manages the quality of delivered ingredients and provides consumers with up-to-date information. This ensures that consumers receive high-quality ingredients and improves trust. Step 5: The support department provides customer support based on information managed by the management department. For example, the support department responds to inquiries and complaints from consumers and uses this information to improve the service. This improves customer satisfaction and service quality.

[0068] (Example of form 2) The food supply system according to an embodiment of the present invention is a system that collects fresh food ingredients from local farmers, fishermen, and processors and delivers them to restaurants and homes in urban areas. This system provides a food ingredient catalog and ordering system through an online platform. Next, it uses AI to forecast demand and creates an optimal supply plan through data analysis. Furthermore, it calculates the optimal route and tracks it in real time to improve the efficiency of logistics and delivery. It strengthens collaboration with local producers and provides producer profile creation, training, and support. It also establishes a customer support system and collects feedback to improve the service. This mechanism realizes the provision of fresh, high-quality food ingredients, the revitalization of the local economy, and the reduction of food waste. For example, it provides a food ingredient catalog and ordering system through an online platform. Consumers can select and order fresh food ingredients provided by local farmers, fishermen, and processors. For example, seasonal vegetables, fresh seafood, and local specialties are listed in the catalog. Next, it uses AI to forecast demand. The AI ​​analyzes past order data, seasonal fluctuations, and consumer preferences to predict future demand. This makes it possible to optimize the supply plan and reduce unnecessary inventory. For example, it's possible to predict in advance which ingredients will be in high demand during specific seasons and secure the appropriate quantities. Furthermore, to improve the efficiency of logistics and delivery, it calculates optimal routes and tracks them in real time. AI optimizes delivery routes and enables efficient delivery. For example, it can calculate routes that efficiently visit multiple delivery destinations, reducing delivery times. It can also track delivery status in real time and provide consumers with the latest information. To strengthen collaboration with local producers, it provides producer profile creation, training, and support. Producers can register their profiles on the online platform and introduce their products and production methods to consumers. Training and support can also be used to improve producers' skills and quality control. Finally, a customer support system is in place to collect feedback. Consumer inquiries and complaints are addressed and used to improve services. For example, consumer feedback can be used to improve the quality of ingredients and the speed of delivery.This system ensures the provision of fresh, high-quality ingredients. Consumers can easily access fresh, locally produced ingredients and enjoy healthy and delicious meals. Furthermore, it stimulates the local economy and supports increased profits for local producers. In addition, it reduces food waste and minimizes the mismatch between local supply and demand. Thus, this ingredient supply system achieves the provision of fresh, high-quality ingredients, stimulates the local economy, and reduces food waste.

[0069] The food supply system according to the embodiment comprises a collection unit, an analysis unit, a delivery unit, a management unit, and a support unit. The collection unit provides a food catalog and an ordering system. The collection unit provides the food catalog and ordering system, for example, through an online platform. The collection unit enables consumers to easily order fresh local ingredients. The analysis unit performs demand forecasting based on the information collected by the collection unit. The analysis unit analyzes, for example, past order data, seasonal fluctuations, and consumer preferences to predict future demand. The analysis unit can improve the accuracy of demand forecasting and reduce unnecessary inventory. The delivery unit calculates the optimal route based on the results obtained by the analysis unit and performs delivery. The delivery unit calculates, for example, the optimal route and calculates a route that efficiently visits multiple delivery destinations. The delivery unit can improve delivery efficiency and shorten delivery time. The management unit manages the quality of the ingredients delivered by the delivery unit. The management unit manages the quality of the delivered ingredients and provides consumers with the latest information. The management unit provides consumers with high-quality ingredients and improves reliability. The support department provides customer support based on information managed by the management department. For example, the support department responds to inquiries and complaints from consumers and uses this information to improve services. The support department improves consumer satisfaction and service quality. As a result, the food supply system according to this embodiment can provide fresh, high-quality food ingredients, revitalize the local economy, and reduce food waste.

[0070] The Collection Department provides a food catalog and ordering system. For example, it provides the food catalog and ordering system through an online platform. Specifically, the Collection Department provides an intuitive and user-friendly interface to allow consumers to easily order fresh local ingredients. The online platform lists a wide variety of ingredients offered by local farmers and producers, and consumers can search these ingredients by category, popularity, price, etc. Furthermore, each ingredient includes detailed descriptions, nutritional information, storage instructions, and cooking methods, allowing consumers to obtain sufficient information before purchasing. The ordering system is designed to allow consumers to easily add selected ingredients to their cart and complete their order. A variety of payment methods are available, including credit cards, debit cards, e-money, and bank transfers, with multiple options for consumer convenience. In addition, the Collection Department provides a function to save consumers' order history and support reordering and creating favorites lists. This makes it easy for consumers to reorder previously purchased ingredients, improving convenience. The Collection Department also collects consumer feedback to help improve the food catalog and ordering system. For example, a feature could be provided that allows consumers to post ratings and reviews of specific food items, which other consumers can then use as a reference. This would enable the data collection department to provide services that meet consumer needs and improve satisfaction.

[0071] The analysis unit performs demand forecasting based on information collected by the data collection unit. For example, the analysis unit analyzes past order data, seasonal fluctuations, and consumer preferences to predict future demand. Specifically, the analysis unit uses AI to analyze large amounts of data and improve the accuracy of demand forecasting. The AI ​​learns demand patterns in specific seasons and events based on past order data to predict future demand. It can also analyze consumer preferences and purchase history to provide personalized suggestions to individual consumers. For example, it can suggest recommended ingredients for the next order based on ingredients frequently purchased by a particular consumer in the past or ingredients popular in a specific season. This allows the analysis unit to provide services that meet consumer needs and improve satisfaction. Furthermore, the analysis unit can optimize inventory management and procurement planning based on the demand forecasting results. For example, for ingredients predicted to have high demand, it can secure sufficient inventory in advance to reduce unnecessary stock. Conversely, for ingredients predicted to have low demand, it can reduce procurement to minimize food waste. This enables the analysis unit to achieve efficient inventory management and procurement planning, improving the overall operational efficiency of the system.

[0072] The delivery department calculates the optimal route based on the results obtained by the analysis department and carries out deliveries. For example, the delivery department calculates the optimal route and a route that efficiently visits multiple delivery destinations. Specifically, the delivery department uses AI to optimize delivery routes. The AI ​​considers traffic conditions, weather, and location information of delivery destinations to calculate the route that allows for the shortest and most efficient delivery. This allows the delivery department to improve delivery efficiency and shorten delivery times. Furthermore, the delivery department can monitor the delivery status in real time and recalculate or adjust routes as needed. For example, if an unexpected event such as a traffic jam or accident occurs, the AI ​​immediately calculates a new route and notifies the delivery driver. This allows the delivery department to always deliver using the optimal route and minimize delays. The delivery department also provides a function to notify consumers of the delivery status in real time. Consumers can check the stage of their order through an online platform or smartphone app. This allows consumers to wait for their order with peace of mind and improves customer satisfaction. In addition, the delivery department also provides functions to support safe driving by delivery drivers. For example, by providing warnings and suggesting breaks while driving, driver fatigue is reduced, ensuring safe deliveries. This allows the delivery department to conduct efficient and safe deliveries and provide high-quality service to consumers.

[0073] The Management Department manages the quality of ingredients delivered by the Delivery Department. For example, the Management Department manages the quality of delivered ingredients and provides consumers with the latest information. Specifically, the Management Department checks the quality of delivered ingredients and provides quality assurance to consumers. For example, it checks the freshness and condition of ingredients and responds quickly if there are any problems. The Management Department also provides consumers with information on how to store and cook ingredients, supporting them in using the ingredients in the best possible condition. Furthermore, the Management Department has implemented a system to ensure the traceability of ingredients. This allows consumers to check how the ingredients they purchased were produced and how they were delivered. For example, they can view producer information, production processes, and delivery history of ingredients through online platforms and smartphone apps. This allows consumers to use ingredients with peace of mind and improves reliability. The Management Department also collects feedback from consumers and uses it to improve quality control. For example, it provides a function that allows consumers to post ratings and reviews on the quality of ingredients so that other consumers can use them as a reference. This allows the Management Department to implement quality control that meets consumer needs and improves satisfaction. Furthermore, the management department analyzes data on ingredient quality and continuously improves quality control processes. For example, based on quality data related to specific ingredients or producers, they review quality control standards and implement improvement measures. This allows the management department to consistently provide high-quality ingredients and gain consumer trust.

[0074] The Support Department provides customer support based on information managed by the Management Department. For example, the Support Department handles consumer inquiries and complaints, using this information to improve services. Specifically, the Support Department operates a customer support center to respond quickly and courteously to consumer inquiries. Consumers can contact the Support Department through multiple means, such as phone, email, and chat. The Support Department provides appropriate information and solutions depending on the nature of the consumer inquiry. For example, it handles a variety of inquiries, such as questions about orders, inquiries about delivery status, and complaints about the quality of ingredients. The Support Department also collects consumer feedback to improve services. For example, it analyzes ratings and reviews provided by consumers to identify areas for service enhancement and improvement. This allows the Support Department to provide services that meet consumer needs and improve satisfaction. Furthermore, it is important for the Support Department to provide proactive support to consumers. For example, it regularly notifies consumers of new services and campaigns to ensure they stay up-to-date. It also improves trust by implementing preventative measures for problems and complaints consumers have experienced in the past. This allows the Support Department to provide high-quality support to consumers and improve the overall quality of the service.

[0075] The collection unit can provide a food catalog and ordering system through an online platform. For example, the collection unit provides a food catalog and ordering system through an online platform. The collection unit makes it easy for consumers to order fresh local ingredients. The online platform includes specific functions and configurations such as a user interface, backend system, and security measures. This allows consumers to easily order fresh local ingredients.

[0076] The analysis unit can analyze past order data, seasonal fluctuations, and consumer preferences to predict future demand. For example, the analysis unit can analyze past order data, seasonal fluctuations, and consumer preferences to predict future demand. The analysis unit can improve the accuracy of demand forecasts and reduce unnecessary inventory. Past order data includes specific details and collection methods, such as order date and time, order details, and customer information. Seasonal fluctuations include specific impacts and considerations, such as seasonal changes in demand and seasonal supply conditions. Consumer preferences include specific methods of understanding and analyzing them, such as surveys, purchase history analysis, and social media data analysis. This improves the accuracy of demand forecasts and reduces unnecessary inventory.

[0077] The delivery department can calculate the optimal route and the most efficient route to visit multiple delivery destinations. For example, the delivery department can calculate the optimal route and the most efficient route to visit multiple delivery destinations. The delivery department can improve delivery efficiency and reduce delivery time. The optimal route calculation includes methods that consider factors such as distance, time, cost, and traffic conditions. This improves delivery efficiency and reduces delivery time.

[0078] The management department can control the quality of delivered ingredients and provide consumers with up-to-date information. For example, the management department can control the quality of delivered ingredients and provide consumers with up-to-date information. The management department can provide consumers with high-quality ingredients and improve reliability. Methods for controlling quality include specific methods and standards such as the frequency of quality checks, the equipment and techniques used, and the details of quality standards. This allows for the provision of high-quality ingredients to consumers and improves reliability.

[0079] The support department can handle customer inquiries and complaints and use this information to improve services. For example, the support department can handle customer inquiries and complaints and use this information to improve services. The support department can improve customer satisfaction and service quality. The methods for handling inquiries and complaints include specific procedures and standards, such as response times and the skills of the support staff. This can improve customer satisfaction and service quality.

[0080] The data collection unit can estimate the user's emotions and adjust the display order of the ingredient catalog based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize displaying ingredients with relaxing effects. For example, if the user is having fun, the data collection unit will display seasonal specialties and new products in a prominent position. For example, if the user is in a hurry, the data collection unit will prioritize displaying ingredients that can be cooked immediately. This makes it possible to display an ingredient catalog that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can analyze a user's past order history and prioritize displaying the most suitable ingredients. For example, the data collection unit can automatically display ingredients that the user has frequently purchased in the past as suggestions. For example, the data collection unit can predict ingredients that a user will purchase in a particular season based on their past order history and prioritize displaying them. For example, the data collection unit can analyze a user's past order history and suggest relevant recipes and cooking methods. This allows the system to display the most suitable ingredients based on the user's past order history. Past order history includes specific details such as the date and time of order, order details, and customer information, as well as the method of collection.

[0082] The data collection unit can dynamically adjust the update frequency of the food catalog in accordance with seasonal and market fluctuations. For example, the data collection unit can automatically update the catalog content according to the availability of seasonal ingredients. For example, the data collection unit can update the prices and inventory status of ingredients in real time in accordance with market price fluctuations. For example, the data collection unit can dynamically change the catalog content to coincide with specific events or campaigns. This enables the updating of the food catalog in accordance with seasonal and market fluctuations. The update frequency includes specific criteria and adjustment methods such as the timing of updates, the content of updates, and the impact of updates.

[0083] The data collection unit can estimate the user's emotions and customize the ordering system interface based on the estimated emotions. For example, if the user is tense, the data collection unit provides a simple and highly visible interface. For example, if the user is relaxed, the data collection unit provides an interface with detailed information. For example, if the user is in a hurry, the data collection unit provides an interface that allows for quick ordering. This provides an ordering system interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The product collection department can attract consumer interest by adding stories and background information about local producers to the food catalog. For example, the department can add pages introducing producer profiles and production methods. For example, the department can post interview videos and photos of producers to create a sense of familiarity with consumers. For example, the department can provide information about the producers' region and history to pique consumer interest. In this way, adding stories and background information about local producers can attract consumer interest. The stories and background information about local producers can include specific details such as the producer's history, cultivation methods, and local specialties, as well as collection methods.

[0085] The collection unit can add a recipe suggestion function to the ingredient catalog, providing cooking ideas using purchased ingredients. For example, the collection unit can suggest easy-to-make recipes based on purchased ingredients. For example, the collection unit can offer special recipes using seasonal ingredients. For example, the collection unit can suggest a variety of recipes depending on the combination of ingredients. This will improve consumer satisfaction by providing cooking ideas using purchased ingredients. The recipe suggestion function will include specific details and implementation methods, such as the types of recipes suggested, the criteria for suggestion, and how the recipes are displayed.

[0086] The analysis unit can estimate the user's emotions and adjust the accuracy of demand forecasts based on those emotions. For example, if the user is stressed, the analysis unit uses detailed data to improve the accuracy of demand forecasts. For example, if the user is relaxed, the analysis unit adjusts the accuracy of demand forecasts and creates a flexible supply plan. For example, if the user is in a hurry, the analysis unit can quickly forecast demand to enable immediate response. This makes it possible to adjust the accuracy of demand forecasts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can improve the accuracy of demand forecasts based on weather data. For example, the analysis unit forecasts demand under specific weather conditions based on weather data. For example, the analysis unit dynamically adjusts the demand forecasting model in response to weather fluctuations. For example, the analysis unit analyzes past weather data and demand data to clarify the relationship between weather and demand. This improves the accuracy of demand forecasts based on weather data. Weather data includes specific details such as temperature, precipitation, and wind speed, as well as methods of data collection.

[0088] The analysis unit can analyze regional consumption trends and predict region-specific demand when forecasting demand. For example, the analysis unit collects regional consumption data and incorporates it into demand forecasting. For example, the analysis unit analyzes regional consumption trends and predicts demand in a specific region. For example, the analysis unit considers information such as regional events and festivals when forecasting demand. In this way, by analyzing regional consumption trends, it is possible to predict region-specific demand. Regional consumption trends include specific methods for understanding and analyzing regional purchasing data, region-specific events, and regional demographics.

[0089] The analysis unit can estimate the user's emotions and adjust the method of displaying the demand forecast results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a concise display method. This makes it possible to display demand forecast results that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis unit can analyze social media trend data during demand forecasting to reflect consumer preferences. For example, the analysis unit collects social media trend data and incorporates it into demand forecasting. For example, the analysis unit analyzes social media trends to predict consumer preferences. For example, the analysis unit adjusts the demand forecasting model based on social media trend data. In this way, consumer preferences can be reflected by analyzing social media trend data. Social media trend data includes specific details and collection methods such as hashtag usage frequency, analysis of post content, and trend detection methods.

[0091] The analysis unit can predict demand for a specific event based on past event data during demand forecasting. For example, the analysis unit collects past event data and incorporates it into demand forecasting. For example, the analysis unit predicts demand for a specific event and develops a supply plan. For example, the analysis unit analyzes past event data and demand data to clarify the relationship between events and demand. This allows the analysis unit to predict demand for a specific event based on past event data. Past event data includes specific details such as the type of event, the number of participants, and the impact of the event, as well as the method of collection.

[0092] The delivery system can estimate the user's emotions and adjust the priority of delivery routes based on those emotions. For example, if the user is stressed, the system will prioritize routes that allow for quick delivery. If the user is relaxed, the system will prioritize efficient routes. If the user is in a hurry, the system will prioritize the shortest route. This allows for the prioritization of delivery routes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The delivery department can dynamically update the optimal route by reflecting real-time traffic data during delivery. For example, the delivery department can propose the optimal route based on real-time traffic congestion information. For example, the delivery department can propose a detour route considering real-time traffic accident information. For example, the delivery department can propose the optimal route considering the real-time operation status of public transportation. In this way, the optimal delivery route can be selected by reflecting real-time traffic data. Traffic data includes specific details and collection methods such as traffic congestion information, road construction information, and traffic accident information.

[0094] The delivery department can select routes that minimize environmental impact based on the fuel efficiency data of delivery vehicles during deliveries. For example, the delivery department can propose the optimal route based on the fuel efficiency data of delivery vehicles. For example, the delivery department can prioritize the selection of fuel-efficient routes. For example, the delivery department can propose routes that minimize environmental impact by considering fuel efficiency data. This allows for the selection of routes that minimize environmental impact based on the fuel efficiency data of delivery vehicles. Fuel efficiency data includes specific details and collection methods such as fuel efficiency data for each vehicle, mileage, and fuel consumption.

[0095] The delivery unit can estimate the user's emotions and adjust the timing of delivery notifications based on those emotions. For example, if the user is stressed, the delivery unit will send a delivery notification earlier. If the user is relaxed, the delivery unit will send a delivery notification at an appropriate time. If the user is in a hurry, the delivery unit will send a delivery notification quickly. This makes it possible to adjust the timing of delivery notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The delivery department can propose the optimal delivery time based on the consumer's availability to receive the delivery. For example, the delivery department proposes the optimal delivery time based on the consumer's availability. For example, the delivery department proposes flexible delivery times to suit the consumer's schedule. For example, the delivery department proposes efficient delivery times considering the consumer's availability. This allows the delivery department to propose the optimal delivery time based on the consumer's availability. Availability includes specific methods for determining and adjusting the user's preferred time, such as how to collect the user's preferred time and how to adjust the time slot.

[0097] The delivery department can apply algorithms to maximize the loading efficiency of delivery vehicles during delivery. For example, the delivery department proposes the optimal loading method to maximize the loading efficiency of delivery vehicles. For example, the delivery department proposes efficient routes considering the loading efficiency of delivery vehicles. For example, the delivery department applies algorithms to maximize the loading efficiency of delivery vehicles and creates a delivery plan. This enables efficient delivery by maximizing the loading efficiency of delivery vehicles. Loading efficiency includes specific calculation methods and criteria such as loadable weight, loadable volume, and loading arrangement method.

[0098] The management department can estimate the user's emotions and adjust quality control standards based on those estimated emotions. For example, if the user is stressed, the management department will apply strict quality control standards. For example, if the user is relaxed, the management department will apply flexible quality control standards. For example, if the user is in a hurry, the management department will perform rapid quality control. This makes it possible to adjust quality control standards according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The management department can prevent quality deterioration by monitoring the storage conditions of ingredients in real time during quality control. For example, the management department can monitor the temperature and humidity of ingredients in real time to maintain an appropriate storage environment. For example, the management department can periodically check the storage conditions of ingredients to detect quality deterioration early. For example, if an abnormality occurs in the storage conditions of ingredients, the management department can immediately issue an alert and take action. In this way, quality deterioration can be prevented by monitoring the storage conditions of ingredients in real time. Storage conditions include specific monitoring methods and standards such as temperature, humidity, and storage period.

[0100] The management department can improve its quality control process by incorporating consumer feedback during quality control. For example, the management department can collect consumer feedback and incorporate it into the quality control process. For example, the management department can review and improve quality control standards based on consumer opinions. For example, the management department can analyze consumer feedback to identify quality control issues and take countermeasures. In this way, the quality control process can be improved by incorporating consumer feedback. Feedback includes specific collection and incorporation methods such as surveys, review collection, and feedback analysis methods.

[0101] The management department can estimate the user's emotions and adjust the quality control reporting method based on the estimated emotions. For example, if the user is stressed, the management department can provide a simple and highly visible reporting method. For example, if the user is relaxed, the management department can provide a reporting method that includes detailed information. For example, if the user is in a hurry, the management department can provide a reporting method that gets straight to the point. This makes it possible to adjust the quality control reporting method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The management department can provide transparency to consumers by displaying producer information for ingredients during quality control. For example, the management department can display producer information for ingredients and introduce consumers to the producer's background. For example, the management department can provide transparency to consumers by providing information on the production methods and production locations of ingredients. For example, the management department can provide trust to consumers by displaying ratings and reviews of ingredient producers. In this way, transparency can be provided to consumers by displaying producer information for ingredients. Producer information includes specific details and display methods such as the producer's name, location, cultivation methods, and certification information.

[0103] The management department can ensure the traceability of ingredients during quality control, providing consumers with peace of mind. For example, the management department can provide traceability information from the production to the delivery of ingredients. For example, the management department can update ingredient traceability information in real time to provide consumers with the latest information. For example, the management department can make the quality control process transparent based on ingredient traceability information. In this way, by ensuring the traceability of ingredients, consumers can be provided with peace of mind. Traceability includes specific methods and standards for ensuring traceability, such as tracking systems, data recording methods, and the scope of traceability.

[0104] The support unit can estimate the user's emotions and adjust the priority of support responses based on the estimated emotions. For example, if the user is stressed, the support unit will provide support quickly. For example, if the user is relaxed, the support unit will provide support at the appropriate time. For example, if the user is in a hurry, the support unit will adjust the priority of support responses to ensure immediate assistance. This makes it possible to adjust the priority of support responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The support department can provide prompt and accurate support based on past inquiry history. For example, the support department will respond quickly based on the user's past inquiry history. For example, the support department will refer to past inquiry content and provide accurate answers. For example, the support department will analyze past inquiry history and provide solutions to common problems. This enables prompt and accurate support based on past inquiry history. The inquiry history includes specific details such as the date and time of the inquiry, the content, and the status of the response, as well as the method of collection.

[0106] The support department can provide 24 / 7 support by utilizing an AI chatbot. For example, the support department can provide 24 / 7 support by utilizing an AI chatbot. For example, the support department can respond quickly to inquiries through the AI ​​chatbot. For example, the support department can provide automated responses to user questions by utilizing an AI chatbot. This makes 24 / 7 support possible by utilizing an AI chatbot. The AI ​​chatbot includes specific functions and implementation methods, such as the range of questions it can answer, its learning algorithm, and its user interface.

[0107] The support unit can estimate the user's emotions and adjust its support response based on those emotions. For example, if the user is nervous, the support unit will respond calmly. If the user is relaxed, the support unit will respond in a friendly manner. If the user is in a hurry, the support unit will respond quickly and concisely. This makes it possible to adjust the support response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The support department can collect consumer feedback during support sessions and use it to improve services. For example, the support department can collect consumer feedback after providing support. For example, the support department can identify areas for service improvement based on consumer feedback. For example, the support department can analyze consumer feedback and use it to improve service quality. In this way, collecting consumer feedback can be used to improve services. Feedback includes specific collection methods and methods of implementation, such as surveys, review collection, and feedback analysis methods.

[0109] The support department can enhance the FAQ database and facilitate self-resolution during support. For example, the support department can refer to the FAQ database and provide answers quickly during support. For example, the support department can enhance the FAQ database to enable users to resolve issues themselves. For example, the support department can regularly update the FAQ database to provide the latest information. This enhances the FAQ database, enabling users to resolve issues themselves. The FAQ database includes specific content and methods of enhancement, such as the types of frequently asked questions, detailed answers, and how to update the database. Self-resolution includes specific methods and criteria for promoting self-resolution, such as providing user guides, instructions on how to use support tools, and methods for evaluating self-resolution.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the user's emotions and adjust the display order of the ingredient catalog based on those emotions. For example, if the user is stressed, ingredients with relaxing effects can be displayed preferentially. If the user is enjoying themselves, seasonal specialties and new products can be displayed in a prominent position. Furthermore, if the user is in a hurry, ingredients that can be cooked immediately can be displayed preferentially. This makes it possible to display an ingredient catalog that responds to the user's emotions.

[0112] The analysis unit can improve the accuracy of demand forecasts based on weather data. For example, it can predict demand under specific weather conditions based on weather data. It can also dynamically adjust the demand forecasting model in response to weather fluctuations. Furthermore, it can analyze past weather and demand data to clarify the relationship between weather and demand. This improves the accuracy of demand forecasts based on weather data.

[0113] The delivery department can estimate the user's emotions and adjust the priority of delivery routes based on those emotions. For example, if the user is stressed, it can prioritize routes that allow for quick delivery. If the user is relaxed, it can prioritize efficient routes. Furthermore, if the user is in a hurry, it can prioritize the shortest route. This makes it possible to adjust the priority of delivery routes according to the user's emotions.

[0114] The management department can monitor the storage conditions of ingredients in real time during quality control, preventing quality deterioration. For example, it can monitor the temperature and humidity of ingredients in real time to maintain an appropriate storage environment. It can also regularly check the storage conditions of ingredients to detect quality deterioration early. Furthermore, if an abnormality occurs in the storage conditions of ingredients, an alert can be immediately issued, allowing for corrective action. In this way, quality deterioration can be prevented by monitoring the storage conditions of ingredients in real time.

[0115] The support department can estimate the user's emotions and adjust the priority of support responses based on those estimates. For example, if the user is stressed, support can be provided quickly. If the user is relaxed, support can be provided at the appropriate time. Furthermore, if the user is in a hurry, the priority of support responses can be adjusted to allow for immediate response. This makes it possible to adjust the priority of support responses according to the user's emotions.

[0116] The collection department can attract consumer interest by adding stories and background information about local producers to the food catalog. For example, they can add pages introducing producer profiles and production methods. They can also include interview videos and photos of producers to create a sense of familiarity with consumers. Furthermore, they can provide information about the producers' regions and history to pique consumer interest. In short, by adding stories and background information about local producers, they can attract consumer interest.

[0117] The analysis unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on those emotions. For example, if the user is stressed, detailed data can be used to improve the accuracy of the demand forecast. If the user is relaxed, the accuracy of the demand forecast can be adjusted to create a more flexible supply plan. Furthermore, if the user is in a hurry, the system can quickly forecast demand to enable immediate response. This makes it possible to adjust the accuracy of the demand forecast according to the user's emotions.

[0118] The delivery department can dynamically update the optimal route by reflecting real-time traffic data during delivery. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest detour routes considering real-time traffic accident information. Furthermore, it can suggest the optimal route considering the real-time operation status of public transportation. In this way, the optimal delivery route can be selected by reflecting real-time traffic data.

[0119] The management department can estimate the user's emotions and adjust quality control standards based on those estimates. For example, if the user is stressed, strict quality control standards can be applied. Conversely, if the user is relaxed, flexible quality control standards can be applied. Furthermore, if the user is in a hurry, quality control can be performed quickly. This makes it possible to adjust quality control standards in accordance with the user's emotions.

[0120] The support department can provide 24 / 7 support by utilizing an AI chatbot. For example, they can provide 24 / 7 support using an AI chatbot. They can also respond to inquiries quickly through the AI ​​chatbot. Furthermore, they can provide automated responses to user questions using the AI ​​chatbot. This makes 24 / 7 support possible by utilizing the AI ​​chatbot.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection department provides a food catalog and ordering system. The collection department provides a food catalog and ordering system, for example, through an online platform, making it easy for consumers to order fresh local ingredients. Step 2: The analysis unit performs demand forecasting based on the information collected by the data collection unit. The analysis unit analyzes, for example, past order data, seasonal fluctuations, and consumer preferences to predict future demand. This improves the accuracy of demand forecasting and reduces unnecessary inventory. Step 3: The delivery unit calculates the optimal route based on the results obtained by the analysis unit and carries out the delivery. For example, the delivery unit calculates the optimal route and a route that efficiently visits multiple delivery destinations. This improves the efficiency of delivery and reduces delivery time. Step 4: The management department manages the quality of ingredients delivered by the delivery department. For example, the management department manages the quality of delivered ingredients and provides consumers with up-to-date information. This ensures that consumers receive high-quality ingredients and improves trust. Step 5: The support department provides customer support based on information managed by the management department. For example, the support department responds to inquiries and complaints from consumers and uses this information to improve the service. This improves customer satisfaction and service quality.

[0123] 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, delivery unit, management unit, and support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and provides a food catalog and ordering system through an online platform. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past order data, seasonal fluctuations, consumer preferences, etc., to predict future demand. The delivery unit is implemented by, for example, the control unit 46A of the smart device 14 and calculates the optimal route and a route that efficiently visits multiple delivery destinations. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the quality of delivered food ingredients and provides consumers with the latest information. The support unit is implemented by, for example, the control unit 46A of the smart device 14 and responds to inquiries and complaints from consumers and helps improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, delivery unit, management unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and provides a food catalog and ordering system through an online platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past order data, seasonal fluctuations, consumer preferences, etc., to predict future demand. The delivery unit is implemented by the control unit 46A of the smart glasses 214 and calculates the optimal route and a route that efficiently visits multiple delivery destinations. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the quality of delivered food ingredients and provides consumers with the latest information. The support unit is implemented by the control unit 46A of the smart glasses 214 and responds to inquiries and complaints from consumers and helps improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, delivery unit, management unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and provides a food catalog and ordering system through an online platform. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past order data, seasonal fluctuations, consumer preferences, etc., to predict future demand. The delivery unit is implemented by, for example, the control unit 46A of the headset terminal 314 and calculates the optimal route and a route that efficiently visits multiple delivery destinations. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the quality of delivered food ingredients and provides consumers with the latest information. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and responds to inquiries and complaints from consumers and helps improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, delivery unit, management unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and provides a food catalog and ordering system through an online platform. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past order data, seasonal fluctuations, consumer preferences, etc., to predict future demand. The delivery unit is implemented by, for example, the control unit 46A of the robot 414 and calculates the optimal route and a route that efficiently visits multiple delivery destinations. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the quality of delivered food ingredients and provides consumers with the latest information. The support unit is implemented by, for example, the control unit 46A of the robot 414 and responds to inquiries and complaints from consumers and helps improve the service. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] 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.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] 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.

[0194] (Note 1) A collection department that provides a food catalog and ordering system, An analysis unit performs demand forecasting based on the information collected by the aforementioned collection unit, Based on the results obtained by the analysis unit, the delivery unit calculates the optimal route and carries out the delivery. A management department that manages the quality of food ingredients delivered by the aforementioned delivery department, The system includes a support unit that provides customer support based on information managed by the aforementioned management unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We provide a food catalog and ordering system through our online platform. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze past order data, seasonal fluctuations, and consumer preferences to predict future demand. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned delivery department, Calculate the optimal route and determine a route that efficiently visits multiple delivery destinations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, We manage the quality of delivered ingredients and provide consumers with the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We will respond to customer inquiries and complaints and use them to improve our services. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the display order of the ingredient catalog based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system analyzes the user's past order history and prioritizes displaying the most suitable ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The frequency of updating the ingredient catalog is dynamically adjusted according to seasonal and market fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user emotions and customizes the ordering system interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Adding stories and background information about local producers to the food catalog will attract consumer interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We've added a recipe suggestion feature to our ingredient catalog, providing ideas for dishes using the ingredients you've purchased. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates user sentiment and adjusts the accuracy of demand forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Improve the accuracy of demand forecasts by using weather data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When forecasting demand, analyze consumption trends in each region and predict region-specific demand. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Adjust the way we estimate user sentiment and display demand forecast results based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When forecasting demand, analyze social media trend data to reflect consumer preferences. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When forecasting demand, we predict demand for a specific event based on past event data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned delivery department, The system estimates the user's emotions and adjusts the priority of delivery routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned delivery department, During delivery, traffic data is reflected in real time, and the optimal route is dynamically updated. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned delivery department, During delivery, routes that minimize environmental impact are selected based on the fuel efficiency data of the delivery vehicles. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned delivery department, The system estimates the user's emotions and adjusts the timing of delivery notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned delivery department, During delivery, we will suggest the optimal delivery time based on the consumer's availability to receive the delivery. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned delivery department, During delivery, an algorithm is applied to maximize the loading efficiency of the delivery vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, We estimate user sentiment and adjust quality control standards based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During quality control, the storage conditions of ingredients are monitored in real time to prevent quality deterioration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During quality control, we incorporate consumer feedback to improve the quality control process. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, We estimate user sentiment and adjust quality control reporting methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During quality control, we display information about the producers of the ingredients to provide transparency to consumers. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, During quality control, we ensure the traceability of ingredients and provide consumers with peace of mind. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is The system estimates the user's emotions and adjusts the priority of support responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is During support, we will provide prompt and accurate responses based on past inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is We utilize AI chatbots to provide 24 / 7 support. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is The system estimates the user's emotions and adjusts its support response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is During support, we collect consumer feedback to help improve our services. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit is During support, we will enhance our FAQ database and promote self-service. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that provides a food catalog and ordering system, An analysis unit performs demand forecasting based on the information collected by the aforementioned collection unit, Based on the results obtained by the analysis unit, the delivery unit calculates the optimal route and carries out the delivery. A management department that manages the quality of food ingredients delivered by the aforementioned delivery department, The system includes a support unit that provides customer support based on information managed by the aforementioned management unit. A system characterized by the following features.

2. The aforementioned collection unit is We provide a food catalog and ordering system through our online platform. The system according to feature 1.

3. The aforementioned analysis unit, We analyze past order data, seasonal fluctuations, and consumer preferences to predict future demand. The system according to feature 1.

4. The aforementioned delivery department, Calculate the optimal route and determine a route that efficiently visits multiple delivery destinations. The system according to feature 1.

5. The aforementioned management department, We manage the quality of delivered ingredients and provide consumers with the latest information. The system according to feature 1.

6. The aforementioned support unit is We will respond to customer inquiries and complaints and use them to improve our services. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the display order of the ingredient catalog based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is The system analyzes the user's past order history and prioritizes displaying the most suitable ingredients. The system according to feature 1.

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