system

Abstract

The system according to this embodiment aims to analyze food consumption data, optimize inventory, and propose new menu items. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an optimization unit, and a proposal unit. The collection unit collects food consumption data. The analysis unit analyzes the data collected by the collection unit. The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. The proposal unit proposes new menus based on the inventory optimized by the optimization 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, inventory optimization based on food consumption data and proposal of new menus have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze food consumption data, optimize inventory, and propose new menus.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an optimization unit, and a suggestion unit. The data collection unit collects food consumption data. The analysis unit analyzes the data collected by the data collection unit. The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. The suggestion unit proposes new menus based on the inventory optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze food consumption data, optimize inventory, and suggest new menu items. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 AI ​​Ingredient Smart Manager according to an embodiment of the present invention is a system that streamlines ingredient management and menu suggestion in the food and beverage industry. The AI ​​Ingredient Smart Manager utilizes generative AI to manage ingredient consumption data and optimize inventory and suggest new menu items. This reduces food waste and improves operational efficiency. For example, the AI ​​Ingredient Smart Manager automatically collects ingredient consumption data from stores, and the generative AI analyzes this data. Next, it optimizes inventory based on the analysis results and automatically orders necessary ingredients. The generative AI also suggests new menu items based on seasons and trends. This reduces the effort of manual management and supports efficient operation. For example, the generative AI suggests new menu items considering past consumption data, seasons, and trends. This allows stores to always offer menus that are in line with the latest trends, thereby increasing customer satisfaction. In addition, in inventory management, the generative AI maintains optimal inventory in real time, reducing food waste and lowering costs. Furthermore, by analyzing ingredient consumption data, the generative AI understands which ingredients are used and how frequently, preventing inventory shortages and excesses. This reduces food loss and supports sustainable business operations. This system targets small and medium-sized restaurants with 10 or more employees, as well as large franchise chains, aiming to improve efficiency and reduce environmental impact across the entire food service industry. For example, a company with multiple stores nationwide, primarily in urban areas, can improve operational efficiency through streamlined ingredient management and new menu suggestions. In this way, the AI ​​Ingredient Smart Manager can streamline ingredient management and menu suggestions in the food service industry, thereby improving operational efficiency.

[0029] The AI ​​Food Smart Manager according to this embodiment comprises a collection unit, an analysis unit, an optimization unit, and a suggestion unit. The collection unit collects food consumption data. The collection unit acquires data from, for example, a store's POS system. The collection unit can also collect manually entered data. Furthermore, the collection unit can collect data in real time using sensors. For example, the collection unit acquires sales data from a POS system to understand food consumption. Manually entered data is collected by, for example, digitizing data recorded by employees by hand. Data collection using sensors involves, for example, monitoring the temperature and humidity inside a refrigerator in real time to understand food consumption. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generative AI. The generative AI can predict future consumption based on past consumption data. Furthermore, the analysis unit can also analyze the data using statistical analysis. For example, the generative AI learns from past consumption data to predict future consumption. Statistical analysis involves, for example, using regression analysis to understand data trends. The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. For example, the optimization unit adjusts inventory levels. The optimization unit can also automatically determine order quantities. Furthermore, the optimization unit can optimize inventory turnover. For example, the optimization unit monitors inventory levels in real time and automatically orders necessary ingredients. Determining order quantities involves, for example, calculating the optimal order quantity based on past consumption data. Optimizing inventory turnover involves, for example, prioritizing the use of older inventory to increase turnover. The suggestion unit proposes new menus based on the inventory optimized by the optimization unit. The suggestion unit proposes new menus using, for example, generative AI. Generative AI can propose new menus considering seasons and trends. The suggestion unit can also propose new menus based on the popularity of past menus. For example, generative AI proposes new menus based on seasonal ingredient consumption data. Suggestions based on the popularity of past menus involve, for example, devising new menus by referring to popular menus from the past.As a result, the AI-powered food smart manager according to this embodiment can efficiently collect and analyze food consumption data, optimize inventory, and suggest new menu items.

[0030] The data collection unit collects food consumption data. For example, it obtains data from a store's POS system. Specifically, the POS system records sales information for each product in real time, and by acquiring this data, the data collection unit can accurately understand which food items are being consumed and to what extent. The data collection unit can also collect manually entered data. For example, by digitizing data handwritten by employees and entering it into the system, data on special orders and events not recorded in the POS system can also be collected. Furthermore, the data collection unit can collect data in real time using sensors. For example, temperature and humidity sensors installed in refrigerators can be used to monitor the storage conditions of food items and understand their consumption status. This allows the data collection unit to integrate sales data from the POS system, manually entered data, and real-time data from sensors to collect comprehensive food consumption data. This data is stored in a central database and made accessible to the analysis and optimization units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a particular food item is being consumed rapidly, increasing the data collection frequency for that food item allows for quicker inventory assessment and appropriate action. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the data. Generative AI can predict future consumption based on past consumption data. Specifically, it learns from past consumption data and predicts future consumption by considering factors such as season, day of the week, and specific events. The analysis unit can also analyze data using statistical analysis. For example, it can use regression analysis to understand data trends and reveal consumption patterns. Generative AI can predict complex consumption patterns with high accuracy by utilizing neural networks and deep learning technologies. This allows the analysis unit to quickly and accurately analyze collected data and understand future consumption trends. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual consumption patterns and abnormal data, issuing early warnings. For example, if the consumption of a particular food item increases sharply, it can identify the cause and take appropriate action. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term consumption forecasting and anomaly detection, improving the overall reliability and efficiency of the system.

[0032] The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. For example, the optimization unit adjusts inventory levels. Specifically, it evaluates the current inventory level based on the consumption forecast data from the analysis unit and orders the necessary ingredients at the appropriate time. The optimization unit can also automatically determine order quantities. For example, it calculates the optimal order quantity considering past consumption data and the current inventory situation and places orders automatically. Furthermore, the optimization unit can optimize inventory turnover. For example, to increase inventory turnover, the system is configured to use older inventory first. This reduces food waste and lowers costs. The optimization unit monitors inventory levels in real time and can respond immediately as needed. For example, if a particular ingredient is being consumed rapidly, it can immediately check the inventory level of that ingredient and place an additional order. The optimization unit can also perform special inventory management according to seasons and events. For example, it can secure inventory of ingredients that are in high demand in advance to coincide with specific seasons or events. This allows the optimization unit to manage inventory efficiently and effectively, improving the overall system performance.

[0033] The Proposal Department proposes new menu items based on inventory optimized by the Optimization Department. The Proposal Department uses, for example, generative AI to propose new menu items. Generative AI can propose new menu items considering seasons and trends. Specifically, it analyzes seasonal food consumption trends based on past consumption data and current inventory levels to devise new menu items. The Proposal Department can also propose new menu items based on the popularity of past menu items. For example, it can devise new menu items by referencing popular menu items from the past. Generative AI can also automatically generate menu descriptions and recipes using natural language processing technology. This allows the Proposal Department to propose new menu items quickly and efficiently, increasing the diversity of the store's menu. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can adjust the content of future suggestions based on sales data and customer evaluations of new menu items. The Proposal Department can also provide more flexible suggestions by presenting multiple menu options and allowing users to choose. This allows the Proposal Department to offer attractive new menu items to users and contribute to increased store sales.

[0034] The proposal department can suggest new menus that take into account the seasons and trends. For example, the proposal department can suggest new menus based on seasonal food consumption data. For instance, they could suggest menus using spring vegetables in the spring and cold desserts in the summer. Furthermore, the proposal department can also suggest new menus that take trends into account. For example, they could analyze current food trends and suggest new menus based on those trends. This makes it possible to suggest new menus that are in line with the seasons and trends.

[0035] The optimization unit can manage inventory to prevent shortages and excesses. For example, the optimization unit can monitor inventory levels in real time and make adjustments to prevent shortages and excesses. For example, the optimization unit can automatically place orders when inventory falls below a certain level. The optimization unit can also adjust to prioritize the use of specific ingredients when inventory becomes excessive. This prevents shortages and excesses. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs inventory level monitoring and adjustments into an AI model, and the AI ​​model outputs the optimal inventory management method.

[0036] The data collection unit can collect food consumption data in real time. The data collection unit collects data in real time, for example, using sensors. The data collection unit monitors the temperature and humidity inside the refrigerator in real time to understand the status of food consumption. The data collection unit can also acquire sales data in real time from a POS system. This enables real-time data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs data acquired from sensors into an AI model, and the AI ​​model analyzes the data in real time.

[0037] The analysis unit can analyze past consumption data and provide data to maintain optimal inventory levels. For example, the analysis unit uses a generative AI to analyze past consumption data. The generative AI can make future consumption predictions based on past consumption data. For example, the analysis unit can analyze consumption data for the past year to calculate the optimal inventory level. The analysis unit can also analyze consumption data for a specific period to understand seasonal consumption patterns. This allows for maintaining optimal inventory levels based on past consumption data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit inputs past consumption data into a generative AI, and the generative AI outputs the optimal inventory level.

[0038] The suggestion unit can automatically propose new menus using a generation AI. For example, the suggestion unit proposes new menus using the generation AI. The generation AI can propose new menus considering past consumption data, seasons, and trends. For example, the suggestion unit has the generation AI propose new menus based on past consumption data. The generation AI can also propose new menus based on seasonal food consumption data. This enables the generation AI to automatically propose new menus. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs past consumption data into the generation AI, and the generation AI outputs new menus.

[0039] The data collection unit can optimize its collection method when collecting food consumption data, taking into account the store's operating status and event information. For example, if a store is in a busy period, the data collection unit can increase the collection frequency to improve data accuracy. If a store is holding an event, the data collection unit can focus on collecting consumption data for specific food items. The data collection unit can also temporarily suspend collection when a store is closed to avoid collecting unnecessary data. This enables optimal data collection tailored to the store's situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store operating status and event information into an AI model, and the AI ​​model can output the optimal collection method.

[0040] The data collection unit can improve the accuracy of data collection based on the type and quality of the food ingredients when collecting consumption data. For example, for expensive food ingredients, the data collection unit can collect detailed consumption data to strictly manage inventory. For example, for food ingredients with short shelf lives, the data collection unit can collect consumption data frequently to prevent waste. In addition, if the quality of food ingredients is prone to fluctuations, the data collection unit can also collect and manage quality data. This enables highly accurate data collection tailored to the type and quality of the food ingredients. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs food ingredient type and quality data into an AI model, and the AI ​​model outputs the optimal data collection method.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of stores when collecting food consumption data. For example, in urban stores, the data collection unit increases the frequency of data collection to grasp trends. In suburban stores, for example, the data collection unit considers region-specific consumption patterns when collecting data. Furthermore, in tourist areas, the data collection unit can prioritize the collection of consumption data corresponding to the season or events. This enables the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the geographical location information of stores into an AI model, and the AI ​​model outputs the optimal collection method.

[0042] The data collection unit can analyze a store's social media activity and collect relevant data when collecting food consumption data. For example, the data collection unit can analyze a store's social media posts and collect consumption data for a specific menu item. For example, the data collection unit can collect consumption data for popular ingredients by referring to online reviews of the store. The data collection unit can also determine the priority of data collection by considering the store's follower count and engagement rate. This enables data collection based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the store's social media data into an AI model, and the AI ​​model can output the optimal collection method.

[0043] The analysis unit can optimize its analysis algorithm based on the consumption patterns of ingredients during analysis. For example, the analysis unit uses a generative AI to analyze the consumption patterns of ingredients and select the optimal analysis algorithm. For example, the analysis unit performs a detailed analysis of ingredients that are consumed frequently to understand their consumption patterns. For example, the analysis unit performs seasonal analysis for ingredients whose consumption patterns change seasonally. The analysis unit can also perform event-specific analysis for ingredients whose consumption increases during specific events or campaigns. This enables optimal analysis based on consumption patterns. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal analysis algorithm.

[0044] The analysis unit can apply different analysis methods depending on the frequency of consumption and usage of ingredients during analysis. For example, the analysis unit uses a generative AI to analyze the frequency of consumption and usage of ingredients and selects the optimal analysis method. For example, the analysis unit applies a detailed analysis method to ingredients that are consumed frequently. For example, the analysis unit applies a simpler analysis method to ingredients that are consumed infrequently. Furthermore, the analysis unit can also apply a dynamic analysis method to ingredients whose usage is prone to fluctuations. This allows for the application of analysis methods that are appropriate to the frequency of consumption and usage. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal analysis method.

[0045] The analysis unit can adjust the priority of analysis based on the consumption period of ingredients during the analysis process. For example, the analysis unit uses a generative AI to analyze the consumption period of ingredients and determine the optimal analysis priority. For example, for ingredients whose consumption increases seasonally, the analysis unit prioritizes seasonal analysis. For example, for ingredients whose consumption increases during specific events or campaigns, the analysis unit prioritizes event-specific analysis. In addition, for ingredients with irregular consumption periods, the analysis unit can perform analysis considering fluctuations in consumption data. This allows for adjustment of the analysis priority based on the consumption period. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal priority.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on food ingredients during the analysis process. For example, the analysis unit uses a generation AI to analyze relevant literature on food ingredients and select the optimal analysis method. For example, the analysis unit refers to literature on the nutritional value of food ingredients and reflects it in the analysis results. For example, the analysis unit refers to literature on food ingredient preservation methods and reflects it in the inventory management analysis. The analysis unit can also refer to literature on food ingredient cooking methods and reflect it in the analysis of new menu suggestions. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs relevant literature into the generation AI, and the generation AI outputs the optimal analysis method.

[0047] The optimization unit can optimize its optimization algorithm by referring to past inventory data when optimizing inventory. For example, the optimization unit can determine the optimal ordering timing based on past inventory data. For example, the optimization unit can calculate the appropriate amount of inventory based on past inventory data. The optimization unit can also optimize the inventory turnover rate based on past inventory data. This enables optimal inventory management based on past inventory data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit inputs past inventory data into an AI model, and the AI ​​model outputs the optimal optimization algorithm.

[0048] The optimization unit can optimize inventory by considering the expiration dates and quality information of ingredients. For example, the optimization unit can optimize to prioritize the use of ingredients with approaching expiration dates. For example, the optimization unit can optimize to use ingredients that spoil easily as soon as possible. The optimization unit can also adjust the appropriate inventory quantity for ingredients with long expiration dates. This enables optimal inventory management based on expiration dates and quality information. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit inputs expiration dates and quality information into an AI model, and the AI ​​model outputs the optimal optimization method.

[0049] The optimization unit can select the optimal inventory management method by considering the geographical location information of stores when optimizing inventory. For example, in urban stores, the optimization unit increases the frequency of collecting consumption data to grasp trends. For example, in suburban stores, the optimization unit collects data considering consumption patterns specific to the region. Furthermore, in stores in tourist areas, the optimization unit can prioritize the collection of consumption data according to the season and events. This enables optimal inventory management based on geographical location information. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit inputs the geographical location information of stores into an AI model, and the AI ​​model outputs the optimal inventory management method.

[0050] The optimization unit can improve the accuracy of inventory optimization by referring to relevant literature on ingredients. For example, the optimization unit can refer to literature on the nutritional value of ingredients and reflect it in inventory management. For example, the optimization unit can refer to literature on how to preserve ingredients and reflect it in inventory management. The optimization unit can also refer to literature on how to cook ingredients and reflect it in inventory management. In this way, the accuracy of inventory management is improved by referring to relevant literature. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit inputs relevant literature into an AI model, and the AI ​​model outputs the optimal optimization method.

[0051] The proposal unit can optimize its proposal algorithm when proposing new menu items, taking into account the consumption patterns and trends of ingredients. For example, the proposal unit can use generative AI to analyze the consumption patterns and trends of ingredients and select the optimal proposal algorithm. For example, the proposal unit can consider seasonal consumption patterns and propose new menu items that are appropriate for the season. For example, the proposal unit can propose new menu items using popular ingredients based on trends. Furthermore, the proposal unit can also propose new menu items that are timed to coincide with periods of increased consumption, based on past consumption data. This makes it possible to propose optimal new menu items based on consumption patterns and trends. Some or all of the above processes in the proposal unit are performed using generative AI. For example, the proposal unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal proposal algorithm.

[0052] The proposal unit can apply different proposal methods depending on the category and quality of the ingredients when proposing new menu items. For example, the proposal unit uses generative AI to analyze the category and quality of ingredients and select the optimal proposal method. For example, for new menu items using high-quality ingredients, the proposal unit will provide a proposal that includes a detailed explanation. For new menu items using common ingredients, the proposal unit will provide a concise proposal. Furthermore, for new menu items using ingredients from a specific category, the proposal unit can apply a category-specific proposal method. This makes it possible to propose the optimal new menu item according to the category and quality of the ingredients. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs ingredient category and quality data into the generative AI, and the generative AI outputs the optimal proposal method.

[0053] The proposal department can adjust the priority of new menu suggestions based on the consumption period of ingredients. For example, the proposal department can use generative AI to analyze the consumption period of ingredients and determine the optimal priority of suggestions. For example, the proposal department can consider seasonal consumption patterns and prioritize suggesting new menus that are appropriate for the season. For example, the proposal department can prioritize suggesting new menus that use ingredients whose consumption increases during specific events or campaigns. Furthermore, for new menus using ingredients with irregular consumption periods, the proposal department can also make suggestions considering fluctuations in consumption data. This allows for adjustment of the priority of new menu suggestions based on consumption period. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal priority.

[0054] The proposal unit can improve the accuracy of its new menu suggestions by referring to relevant literature on ingredients. For example, the proposal unit uses generative AI to analyze relevant literature on ingredients and select the optimal suggestion method. For example, the proposal unit refers to literature on the nutritional value of ingredients and incorporates it into the new menu suggestions. For example, the proposal unit refers to literature on food preservation methods and incorporates it into the new menu suggestions. The proposal unit can also refer to literature on cooking methods and incorporate it into the new menu suggestions. In this way, the accuracy of new menu suggestions is improved by referring to relevant literature. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs relevant literature into the generative AI, and the generative AI outputs the optimal suggestion method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The AI ​​Food Smart Manager can further optimize its data collection methods by considering store operating conditions and event information when collecting food consumption data. For example, the collection unit can increase the collection frequency to improve data accuracy when a store is in a busy period. It can also focus on collecting consumption data for specific ingredients when a store is holding an event. Furthermore, it can temporarily suspend data collection when a store is closed to avoid collecting unnecessary data. This enables optimal data collection tailored to the store's situation. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs store operating conditions and event information into an AI model, and the AI ​​model outputs the optimal collection method.

[0057] The AI ​​Food Smart Manager can further improve the accuracy of food consumption data collection based on the type and quality of the food. For example, the collection unit can collect detailed consumption data for expensive food items to enable strict inventory management. For food items with short shelf lives, consumption data can be collected frequently to prevent waste. Furthermore, if the quality of the food item is prone to fluctuations, quality data can also be collected and managed. This enables highly accurate data collection tailored to the type and quality of the food item. Some or all of the above-described processes in the collection unit may be performed using AI, or they may not. For example, the collection unit inputs food type and quality data into an AI model, and the AI ​​model outputs the optimal collection method.

[0058] The AI ​​Food Smart Manager can further prioritize the collection of highly relevant data by considering the geographical location of stores when collecting food consumption data. For example, the collection unit can increase the frequency of data collection to understand trends in urban stores. In suburban stores, it can collect data considering region-specific consumption patterns. Furthermore, in stores in tourist areas, it can prioritize the collection of consumption data corresponding to seasons and events. This enables the collection of highly relevant data based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the geographical location information of stores into an AI model, and the AI ​​model outputs the optimal collection method.

[0059] The AI ​​Ingredient Smart Manager can further analyze a store's social media activity and collect relevant data when gathering ingredient consumption data. For example, the collection unit can analyze a store's social media posts and collect consumption data for specific menu items. It can also collect consumption data for popular ingredients by referring to online reviews of the store. Furthermore, it can determine the priority of data collection by considering the store's follower count and engagement rate. This enables data collection based on social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the store's social media data into an AI model, and the AI ​​model outputs the optimal collection method.

[0060] The AI ​​Food Smart Manager can further optimize its optimization algorithm by referencing past inventory data when collecting food consumption data. For example, the optimization unit can determine the optimal ordering timing based on past inventory data. It can also calculate the appropriate amount of inventory based on past inventory data. Furthermore, it can optimize the inventory turnover rate based on past inventory data. This enables optimal inventory management based on past inventory data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit inputs past inventory data into an AI model, and the AI ​​model outputs the optimal optimization algorithm.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects food consumption data. The data collection unit can acquire data from, for example, the store's POS system. The data collection unit can also collect manually entered data. Furthermore, the data collection unit can collect data in real time using sensors. For example, the data collection unit can acquire sales data from the POS system to understand food consumption. Manually entered data can be collected by, for example, digitizing data that employees have recorded by hand. Data collection using sensors can be done by, for example, monitoring the temperature and humidity inside a refrigerator in real time to understand food consumption. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generative AI. The generative AI can predict future consumption based on past consumption data. The analysis unit can also analyze the data using statistical analysis. For example, the generative AI learns from past consumption data and predicts future consumption. Statistical analysis uses, for example, regression analysis to understand data trends. Step 3: The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. The optimization unit adjusts inventory levels, for example. The optimization unit can also automatically determine order quantities. Furthermore, the optimization unit can optimize inventory turnover. For example, the optimization unit monitors inventory levels in real time and automatically orders necessary ingredients. Determining order quantities involves, for example, calculating the optimal order quantity based on past consumption data. Optimizing inventory turnover involves, for example, prioritizing the use of older inventory to increase inventory turnover. Step 4: The proposal department proposes new menu items based on the inventory optimized by the optimization department. The proposal department proposes new menu items using, for example, a generative AI. The generative AI can propose new menu items considering the season and trends. The proposal department can also propose new menu items based on the popularity of past menu items. For example, the generative AI proposes new menu items based on seasonal food consumption data. Proposals based on the popularity of past menu items, for example, devise new menu items by referring to popular menu items from the past.

[0063] (Example of form 2) The AI ​​Ingredient Smart Manager according to an embodiment of the present invention is a system that streamlines ingredient management and menu suggestion in the food and beverage industry. The AI ​​Ingredient Smart Manager utilizes generative AI to manage ingredient consumption data and optimize inventory and suggest new menu items. This reduces food waste and improves operational efficiency. For example, the AI ​​Ingredient Smart Manager automatically collects ingredient consumption data from stores, and the generative AI analyzes this data. Next, it optimizes inventory based on the analysis results and automatically orders necessary ingredients. The generative AI also suggests new menu items based on seasons and trends. This reduces the effort of manual management and supports efficient operation. For example, the generative AI suggests new menu items considering past consumption data, seasons, and trends. This allows stores to always offer menus that are in line with the latest trends, thereby increasing customer satisfaction. In addition, in inventory management, the generative AI maintains optimal inventory in real time, reducing food waste and lowering costs. Furthermore, by analyzing ingredient consumption data, the generative AI understands which ingredients are used and how frequently, preventing inventory shortages and excesses. This reduces food loss and supports sustainable business operations. This system targets small and medium-sized restaurants with 10 or more employees, as well as large franchise chains, aiming to improve efficiency and reduce environmental impact across the entire food service industry. For example, a company with multiple stores nationwide, primarily in urban areas, can improve operational efficiency through streamlined ingredient management and new menu suggestions. In this way, the AI ​​Ingredient Smart Manager can streamline ingredient management and menu suggestions in the food service industry, thereby improving operational efficiency.

[0064] The AI ​​Food Smart Manager according to this embodiment comprises a collection unit, an analysis unit, an optimization unit, and a suggestion unit. The collection unit collects food consumption data. The collection unit acquires data from, for example, a store's POS system. The collection unit can also collect manually entered data. Furthermore, the collection unit can collect data in real time using sensors. For example, the collection unit acquires sales data from a POS system to understand food consumption. Manually entered data is collected by, for example, digitizing data recorded by employees by hand. Data collection using sensors involves, for example, monitoring the temperature and humidity inside a refrigerator in real time to understand food consumption. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generative AI. The generative AI can predict future consumption based on past consumption data. Furthermore, the analysis unit can also analyze the data using statistical analysis. For example, the generative AI learns from past consumption data to predict future consumption. Statistical analysis involves, for example, using regression analysis to understand data trends. The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. For example, the optimization unit adjusts inventory levels. The optimization unit can also automatically determine order quantities. Furthermore, the optimization unit can optimize inventory turnover. For example, the optimization unit monitors inventory levels in real time and automatically orders necessary ingredients. Determining order quantities involves, for example, calculating the optimal order quantity based on past consumption data. Optimizing inventory turnover involves, for example, prioritizing the use of older inventory to increase turnover. The suggestion unit proposes new menus based on the inventory optimized by the optimization unit. The suggestion unit proposes new menus using, for example, generative AI. Generative AI can propose new menus considering seasons and trends. The suggestion unit can also propose new menus based on the popularity of past menus. For example, generative AI proposes new menus based on seasonal ingredient consumption data. Suggestions based on the popularity of past menus involve, for example, devising new menus by referring to popular menus from the past.As a result, the AI-powered food smart manager according to this embodiment can efficiently collect and analyze food consumption data, optimize inventory, and suggest new menu items.

[0065] The data collection unit collects food consumption data. For example, it obtains data from a store's POS system. Specifically, the POS system records sales information for each product in real time, and by acquiring this data, the data collection unit can accurately understand which food items are being consumed and to what extent. The data collection unit can also collect manually entered data. For example, by digitizing data handwritten by employees and entering it into the system, data on special orders and events not recorded in the POS system can also be collected. Furthermore, the data collection unit can collect data in real time using sensors. For example, temperature and humidity sensors installed in refrigerators can be used to monitor the storage conditions of food items and understand their consumption status. This allows the data collection unit to integrate sales data from the POS system, manually entered data, and real-time data from sensors to collect comprehensive food consumption data. This data is stored in a central database and made accessible to the analysis and optimization units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a particular food item is being consumed rapidly, increasing the data collection frequency for that food item allows for quicker inventory assessment and appropriate action. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the data. Generative AI can predict future consumption based on past consumption data. Specifically, it learns from past consumption data and predicts future consumption by considering factors such as season, day of the week, and specific events. The analysis unit can also analyze data using statistical analysis. For example, it can use regression analysis to understand data trends and reveal consumption patterns. Generative AI can predict complex consumption patterns with high accuracy by utilizing neural networks and deep learning technologies. This allows the analysis unit to quickly and accurately analyze collected data and understand future consumption trends. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual consumption patterns and abnormal data, issuing early warnings. For example, if the consumption of a particular food item increases sharply, it can identify the cause and take appropriate action. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term consumption forecasting and anomaly detection, improving the overall reliability and efficiency of the system.

[0067] The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. For example, the optimization unit adjusts inventory levels. Specifically, it evaluates the current inventory level based on the consumption forecast data from the analysis unit and orders the necessary ingredients at the appropriate time. The optimization unit can also automatically determine order quantities. For example, it calculates the optimal order quantity considering past consumption data and the current inventory situation and places orders automatically. Furthermore, the optimization unit can optimize inventory turnover. For example, to increase inventory turnover, the system is configured to use older inventory first. This reduces food waste and lowers costs. The optimization unit monitors inventory levels in real time and can respond immediately as needed. For example, if a particular ingredient is being consumed rapidly, it can immediately check the inventory level of that ingredient and place an additional order. The optimization unit can also perform special inventory management according to seasons and events. For example, it can secure inventory of ingredients that are in high demand in advance to coincide with specific seasons or events. This allows the optimization unit to manage inventory efficiently and effectively, improving the overall system performance.

[0068] The Proposal Department proposes new menu items based on inventory optimized by the Optimization Department. The Proposal Department uses, for example, generative AI to propose new menu items. Generative AI can propose new menu items considering seasons and trends. Specifically, it analyzes seasonal food consumption trends based on past consumption data and current inventory levels to devise new menu items. The Proposal Department can also propose new menu items based on the popularity of past menu items. For example, it can devise new menu items by referencing popular menu items from the past. Generative AI can also automatically generate menu descriptions and recipes using natural language processing technology. This allows the Proposal Department to propose new menu items quickly and efficiently, increasing the diversity of the store's menu. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can adjust the content of future suggestions based on sales data and customer evaluations of new menu items. The Proposal Department can also provide more flexible suggestions by presenting multiple menu options and allowing users to choose. This allows the Proposal Department to offer attractive new menu items to users and contribute to increased store sales.

[0069] The proposal department can suggest new menus that take into account the seasons and trends. For example, the proposal department can suggest new menus based on seasonal food consumption data. For instance, they could suggest menus using spring vegetables in the spring and cold desserts in the summer. Furthermore, the proposal department can also suggest new menus that take trends into account. For example, they could analyze current food trends and suggest new menus based on those trends. This makes it possible to suggest new menus that are in line with the seasons and trends.

[0070] The optimization unit can manage inventory to prevent shortages and excesses. For example, the optimization unit can monitor inventory levels in real time and make adjustments to prevent shortages and excesses. For example, the optimization unit can automatically place orders when inventory falls below a certain level. The optimization unit can also adjust to prioritize the use of specific ingredients when inventory becomes excessive. This prevents shortages and excesses. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs inventory level monitoring and adjustments into an AI model, and the AI ​​model outputs the optimal inventory management method.

[0071] The data collection unit can collect food consumption data in real time. The data collection unit collects data in real time, for example, using sensors. The data collection unit monitors the temperature and humidity inside the refrigerator in real time to understand the status of food consumption. The data collection unit can also acquire sales data in real time from a POS system. This enables real-time data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs data acquired from sensors into an AI model, and the AI ​​model analyzes the data in real time.

[0072] The analysis unit can analyze past consumption data and provide data to maintain optimal inventory levels. For example, the analysis unit uses a generative AI to analyze past consumption data. The generative AI can make future consumption predictions based on past consumption data. For example, the analysis unit can analyze consumption data for the past year to calculate the optimal inventory level. The analysis unit can also analyze consumption data for a specific period to understand seasonal consumption patterns. This allows for maintaining optimal inventory levels based on past consumption data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit inputs past consumption data into a generative AI, and the generative AI outputs the optimal inventory level.

[0073] The suggestion unit can automatically propose new menus using a generation AI. For example, the suggestion unit proposes new menus using the generation AI. The generation AI can propose new menus considering past consumption data, seasons, and trends. For example, the suggestion unit has the generation AI propose new menus based on past consumption data. The generation AI can also propose new menus based on seasonal food consumption data. This enables the generation AI to automatically propose new menus. Some or all of the above processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs past consumption data into the generation AI, and the generation AI outputs new menus.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of food consumption data collection based on the estimated user emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection timing to be adjusted 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. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs image data of the user captured by the camera into a generating AI, which then estimates the user's emotions.

[0075] The data collection unit can optimize its collection method when collecting food consumption data, taking into account the store's operating status and event information. For example, if a store is in a busy period, the data collection unit can increase the collection frequency to improve data accuracy. If a store is holding an event, the data collection unit can focus on collecting consumption data for specific food items. The data collection unit can also temporarily suspend collection when a store is closed to avoid collecting unnecessary data. This enables optimal data collection tailored to the store's situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store operating status and event information into an AI model, and the AI ​​model can output the optimal collection method.

[0076] The data collection unit can improve the accuracy of data collection based on the type and quality of the food ingredients when collecting consumption data. For example, for expensive food ingredients, the data collection unit can collect detailed consumption data to strictly manage inventory. For example, for food ingredients with short shelf lives, the data collection unit can collect consumption data frequently to prevent waste. In addition, if the quality of food ingredients is prone to fluctuations, the data collection unit can also collect and manage quality data. This enables highly accurate data collection tailored to the type and quality of the food ingredients. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs food ingredient type and quality data into an AI model, and the AI ​​model outputs the optimal data collection method.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, for example, the data collection unit will collect detailed data to improve the accuracy of the analysis. The data collection unit can also reduce the amount of data collected to alleviate the workload if the user is busy. This allows for the prioritization of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's emotion data into the generative AI, which then determines the priority of the data.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of stores when collecting food consumption data. For example, in urban stores, the data collection unit increases the frequency of data collection to grasp trends. In suburban stores, for example, the data collection unit considers region-specific consumption patterns when collecting data. Furthermore, in tourist areas, the data collection unit can prioritize the collection of consumption data corresponding to the season or events. This enables the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the geographical location information of stores into an AI model, and the AI ​​model outputs the optimal collection method.

[0079] The data collection unit can analyze a store's social media activity and collect relevant data when collecting food consumption data. For example, the data collection unit can analyze a store's social media posts and collect consumption data for a specific menu item. For example, the data collection unit can collect consumption data for popular ingredients by referring to online reviews of the store. The data collection unit can also determine the priority of data collection by considering the store's follower count and engagement rate. This enables data collection based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the store's social media data into an AI model, and the AI ​​model can output the optimal collection method.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis 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. The analysis unit can also provide a concise display method if the user is in a hurry. This allows for the display of analysis results to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI outputs the optimal display method.

[0081] The analysis unit can optimize its analysis algorithm based on the consumption patterns of ingredients during analysis. For example, the analysis unit uses a generative AI to analyze the consumption patterns of ingredients and select the optimal analysis algorithm. For example, the analysis unit performs a detailed analysis of ingredients that are consumed frequently to understand their consumption patterns. For example, the analysis unit performs seasonal analysis for ingredients whose consumption patterns change seasonally. The analysis unit can also perform event-specific analysis for ingredients whose consumption increases during specific events or campaigns. This enables optimal analysis based on consumption patterns. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal analysis algorithm.

[0082] The analysis unit can apply different analysis methods depending on the frequency of consumption and usage of ingredients during analysis. For example, the analysis unit uses a generative AI to analyze the frequency of consumption and usage of ingredients and selects the optimal analysis method. For example, the analysis unit applies a detailed analysis method to ingredients that are consumed frequently. For example, the analysis unit applies a simpler analysis method to ingredients that are consumed infrequently. Furthermore, the analysis unit can also apply a dynamic analysis method to ingredients whose usage is prone to fluctuations. This allows for the application of analysis methods that are appropriate to the frequency of consumption and usage. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal analysis method.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying only the most important analysis results. If the user is relaxed, for example, the analysis unit will display detailed analysis results to improve the accuracy of the analysis. The analysis unit can also reduce the amount of analysis results displayed to alleviate the workload if the user is busy. This allows for the prioritization of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs user emotion data into the generative AI, and the generative AI outputs the optimal priority.

[0084] The analysis unit can adjust the priority of analysis based on the consumption period of ingredients during the analysis process. For example, the analysis unit uses a generative AI to analyze the consumption period of ingredients and determine the optimal analysis priority. For example, for ingredients whose consumption increases seasonally, the analysis unit prioritizes seasonal analysis. For example, for ingredients whose consumption increases during specific events or campaigns, the analysis unit prioritizes event-specific analysis. In addition, for ingredients with irregular consumption periods, the analysis unit can perform analysis considering fluctuations in consumption data. This allows for adjustment of the analysis priority based on the consumption period. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal priority.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on food ingredients during the analysis process. For example, the analysis unit uses a generation AI to analyze relevant literature on food ingredients and select the optimal analysis method. For example, the analysis unit refers to literature on the nutritional value of food ingredients and reflects it in the analysis results. For example, the analysis unit refers to literature on food ingredient preservation methods and reflects it in the inventory management analysis. The analysis unit can also refer to literature on food ingredient cooking methods and reflect it in the analysis of new menu suggestions. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs relevant literature into the generation AI, and the generation AI outputs the optimal analysis method.

[0086] The optimization unit can estimate the user's emotions and adjust the inventory optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit provides a simple optimization method. For example, if the user is relaxed, the optimization unit provides a detailed optimization method. The optimization unit can also provide a method for quickly optimizing inventory if the user is busy. This allows for inventory optimization methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit is performed using generative AI. For example, the optimization unit inputs user emotion data into the generative AI, and the generative AI outputs the optimal optimization method.

[0087] The optimization unit can optimize its optimization algorithm by referring to past inventory data when optimizing inventory. For example, the optimization unit can determine the optimal ordering timing based on past inventory data. For example, the optimization unit can calculate the appropriate amount of inventory based on past inventory data. The optimization unit can also optimize the inventory turnover rate based on past inventory data. This enables optimal inventory management based on past inventory data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit inputs past inventory data into an AI model, and the AI ​​model outputs the optimal optimization algorithm.

[0088] The optimization unit can optimize inventory by considering the expiration dates and quality information of ingredients. For example, the optimization unit can optimize to prioritize the use of ingredients with approaching expiration dates. For example, the optimization unit can optimize to use ingredients that spoil easily as soon as possible. The optimization unit can also adjust the appropriate inventory quantity for ingredients with long expiration dates. This enables optimal inventory management based on expiration dates and quality information. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit inputs expiration dates and quality information into an AI model, and the AI ​​model outputs the optimal optimization method.

[0089] The optimization unit can estimate the user's emotions and determine inventory priorities based on those emotions. For example, if the user is stressed, the optimization unit will prioritize managing only critical inventory. If the user is relaxed, the optimization unit will perform detailed inventory management. The optimization unit can also provide ways to reduce the burden of inventory management if the user is busy. This allows for inventory priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit is performed using generative AI. For example, the optimization unit inputs user emotion data into the generative AI, and the generative AI outputs the optimal priority.

[0090] The optimization unit can select the optimal inventory management method by considering the geographical location information of stores when optimizing inventory. For example, in urban stores, the optimization unit increases the frequency of collecting consumption data to grasp trends. For example, in suburban stores, the optimization unit collects data considering consumption patterns specific to the region. Furthermore, in stores in tourist areas, the optimization unit can prioritize the collection of consumption data according to the season and events. This enables optimal inventory management based on geographical location information. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit inputs the geographical location information of stores into an AI model, and the AI ​​model outputs the optimal inventory management method.

[0091] The optimization unit can improve the accuracy of inventory optimization by referring to relevant literature on ingredients. For example, the optimization unit can refer to literature on the nutritional value of ingredients and reflect it in inventory management. For example, the optimization unit can refer to literature on how to preserve ingredients and reflect it in inventory management. The optimization unit can also refer to literature on how to cook ingredients and reflect it in inventory management. In this way, the accuracy of inventory management is improved by referring to relevant literature. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit inputs relevant literature into an AI model, and the AI ​​model outputs the optimal optimization method.

[0092] The suggestion unit can estimate the user's emotions and adjust how it suggests new menu items based on those emotions. For example, if the user is relaxed, the suggestion unit will make suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit will make concise suggestions. The suggestion unit can also make visually appealing suggestions if the user is excited. This allows for a new menu suggestion method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs user emotion data into the generative AI, and the generative AI outputs the optimal suggestion method.

[0093] The proposal unit can optimize its proposal algorithm when proposing new menu items, taking into account the consumption patterns and trends of ingredients. For example, the proposal unit can use generative AI to analyze the consumption patterns and trends of ingredients and select the optimal proposal algorithm. For example, the proposal unit can consider seasonal consumption patterns and propose new menu items that are appropriate for the season. For example, the proposal unit can propose new menu items using popular ingredients based on trends. Furthermore, the proposal unit can also propose new menu items that are timed to coincide with periods of increased consumption, based on past consumption data. This makes it possible to propose optimal new menu items based on consumption patterns and trends. Some or all of the above processes in the proposal unit are performed using generative AI. For example, the proposal unit inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal proposal algorithm.

[0094] The proposal unit can apply different proposal methods depending on the category and quality of the ingredients when proposing new menu items. For example, the proposal unit uses generative AI to analyze the category and quality of ingredients and select the optimal proposal method. For example, for new menu items using high-quality ingredients, the proposal unit will provide a proposal that includes a detailed explanation. For new menu items using common ingredients, the proposal unit will provide a concise proposal. Furthermore, for new menu items using ingredients from a specific category, the proposal unit can apply a category-specific proposal method. This makes it possible to propose the optimal new menu item according to the category and quality of the ingredients. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs ingredient category and quality data into the generative AI, and the generative AI outputs the optimal proposal method.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of new menu items based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting new menu items that are easy and quick to prepare. If the user is relaxed, the suggestion unit will suggest new menu items that take more time to prepare. Furthermore, if the user is busy, the suggestion unit can prioritize suggesting new menu items that can be prepared quickly. This allows for the determination of new menu item priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs user emotion data into the generative AI, which then outputs the optimal priority order.

[0096] The proposal department can adjust the priority of new menu suggestions based on the consumption period of ingredients. For example, the proposal department can use generative AI to analyze the consumption period of ingredients and determine the optimal priority of suggestions. For example, the proposal department can consider seasonal consumption patterns and prioritize suggesting new menus that are appropriate for the season. For example, the proposal department can prioritize suggesting new menus that use ingredients whose consumption increases during specific events or campaigns. Furthermore, for new menus using ingredients with irregular consumption periods, the proposal department can also make suggestions considering fluctuations in consumption data. This allows for adjustment of the priority of new menu suggestions based on consumption period. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs ingredient consumption data into the generative AI, and the generative AI outputs the optimal priority.

[0097] The proposal unit can improve the accuracy of its new menu suggestions by referring to relevant literature on ingredients. For example, the proposal unit uses generative AI to analyze relevant literature on ingredients and select the optimal suggestion method. For example, the proposal unit refers to literature on the nutritional value of ingredients and incorporates it into the new menu suggestions. For example, the proposal unit refers to literature on food preservation methods and incorporates it into the new menu suggestions. The proposal unit can also refer to literature on cooking methods and incorporate it into the new menu suggestions. In this way, the accuracy of new menu suggestions is improved by referring to relevant literature. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs relevant literature into the generative AI, and the generative AI outputs the optimal suggestion method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The AI ​​Ingredient Smart Manager can further estimate the user's emotions and suggest new menu items based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest a new menu item that can be prepared over a longer period of time. If the user is busy, it can suggest a new menu item that can be prepared in a shorter time. Furthermore, if the user is stressed, it can suggest a new menu item that is easy and convenient to prepare. This enables the suggestion of new menu items that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs the user's emotion data into the generative AI, which then outputs the optimal suggestion method.

[0100] The AI ​​Food Smart Manager can further optimize its data collection methods by considering store operating conditions and event information when collecting food consumption data. For example, the collection unit can increase the collection frequency to improve data accuracy when a store is in a busy period. It can also focus on collecting consumption data for specific ingredients when a store is holding an event. Furthermore, it can temporarily suspend data collection when a store is closed to avoid collecting unnecessary data. This enables optimal data collection tailored to the store's situation. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs store operating conditions and event information into an AI model, and the AI ​​model outputs the optimal collection method.

[0101] The AI ​​Ingredient Smart Manager can further estimate the user's emotions and adjust the inventory optimization method based on the estimated emotions. For example, the optimization unit can provide a simple optimization method if the user is stressed. It can also provide a detailed optimization method if the user is relaxed. Furthermore, it can provide a method for quickly optimizing inventory if the user is busy. This allows for inventory optimization methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit is performed using generative AI. For example, the optimization unit inputs user emotion data into the generative AI, and the generative AI outputs the optimal optimization method.

[0102] The AI ​​Food Smart Manager can further improve the accuracy of food consumption data collection based on the type and quality of the food. For example, the collection unit can collect detailed consumption data for expensive food items to enable strict inventory management. For food items with short shelf lives, consumption data can be collected frequently to prevent waste. Furthermore, if the quality of the food item is prone to fluctuations, quality data can also be collected and managed. This enables highly accurate data collection tailored to the type and quality of the food item. Some or all of the above-described processes in the collection unit may be performed using AI, or they may not. For example, the collection unit inputs food type and quality data into an AI model, and the AI ​​model outputs the optimal collection method.

[0103] The AI ​​Food Smart Manager can further estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. This allows for the display of analysis results to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI outputs the optimal display method.

[0104] The AI ​​Food Smart Manager can further prioritize the collection of highly relevant data by considering the geographical location of stores when collecting food consumption data. For example, the collection unit can increase the frequency of data collection to understand trends in urban stores. In suburban stores, it can collect data considering region-specific consumption patterns. Furthermore, in stores in tourist areas, it can prioritize the collection of consumption data corresponding to seasons and events. This enables the collection of highly relevant data based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the geographical location information of stores into an AI model, and the AI ​​model outputs the optimal collection method.

[0105] The AI ​​Food Smart Manager can further estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, it can collect detailed data to improve the accuracy of the analysis. Furthermore, if the user is busy, the amount of data collected can be reduced to alleviate the workload. This allows for data prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's emotion data into the generative AI, which then determines the data priority.

[0106] The AI ​​Ingredient Smart Manager can further analyze a store's social media activity and collect relevant data when gathering ingredient consumption data. For example, the collection unit can analyze a store's social media posts and collect consumption data for specific menu items. It can also collect consumption data for popular ingredients by referring to online reviews of the store. Furthermore, it can determine the priority of data collection by considering the store's follower count and engagement rate. This enables data collection based on social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the store's social media data into an AI model, and the AI ​​model outputs the optimal collection method.

[0107] The AI ​​Food Smart Manager can further estimate the user's emotions and prioritize analysis results based on those emotions. For example, if the user is stressed, the analysis unit can prioritize displaying only the most important analysis results. If the user is relaxed, it can display more detailed analysis results to improve the accuracy of the analysis. Furthermore, if the user is busy, the amount of analysis results can be reduced to lessen the workload. This allows for the prioritization of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, which then outputs the optimal priority.

[0108] The AI ​​Food Smart Manager can further optimize its optimization algorithm by referencing past inventory data when collecting food consumption data. For example, the optimization unit can determine the optimal ordering timing based on past inventory data. It can also calculate the appropriate amount of inventory based on past inventory data. Furthermore, it can optimize the inventory turnover rate based on past inventory data. This enables optimal inventory management based on past inventory data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit inputs past inventory data into an AI model, and the AI ​​model outputs the optimal optimization algorithm.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects food consumption data. The data collection unit can acquire data from, for example, the store's POS system. The data collection unit can also collect manually entered data. Furthermore, the data collection unit can collect data in real time using sensors. For example, the data collection unit can acquire sales data from the POS system to understand food consumption. Manually entered data can be collected by, for example, digitizing data that employees have recorded by hand. Data collection using sensors can be done by, for example, monitoring the temperature and humidity inside a refrigerator in real time to understand food consumption. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generative AI. The generative AI can predict future consumption based on past consumption data. The analysis unit can also analyze the data using statistical analysis. For example, the generative AI learns from past consumption data and predicts future consumption. Statistical analysis uses, for example, regression analysis to understand data trends. Step 3: The optimization unit optimizes inventory based on the analysis results obtained by the analysis unit. The optimization unit adjusts inventory levels, for example. The optimization unit can also automatically determine order quantities. Furthermore, the optimization unit can optimize inventory turnover. For example, the optimization unit monitors inventory levels in real time and automatically orders necessary ingredients. Determining order quantities involves, for example, calculating the optimal order quantity based on past consumption data. Optimizing inventory turnover involves, for example, prioritizing the use of older inventory to increase inventory turnover. Step 4: The proposal department proposes new menu items based on the inventory optimized by the optimization department. The proposal department proposes new menu items using, for example, a generative AI. The generative AI can propose new menu items considering the season and trends. The proposal department can also propose new menu items based on the popularity of past menu items. For example, the generative AI proposes new menu items based on seasonal food consumption data. Proposals based on the popularity of past menu items, for example, devise new menu items by referring to popular menu items from the past.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit has the function of acquiring data from the sensors of the smart device 14 and the POS system, and is also implemented by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generating AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts inventory levels and determines order quantities. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes new menus using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit has the function of acquiring data from the sensors of the smart glasses 214 and the POS system, and is also implemented by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generating AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts inventory levels and determines order quantities. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes new menus using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit has the function of acquiring data from the sensors of the headset terminal 314 and the POS system, and is also implemented by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generating AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts inventory levels and determines order quantities. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes new menus using generating AI. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, optimization unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit has the function of acquiring data from the robot 414's sensors and POS system, and is also implemented by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generating AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts inventory levels and determines order quantities. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes new menus using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit that collects food consumption data, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit optimizes inventory based on the analysis results obtained by the analysis unit, A proposal unit proposes new menus based on the inventory optimized by the aforementioned optimization unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose new menu items that take into account the season and current trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Implement management measures to prevent inventory shortages and excess inventory. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect food consumption data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze past consumption data to provide data for maintaining optimal inventory levels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The AI ​​generates and automatically suggests new menu items. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of food consumption data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting food consumption data, we optimize the collection method by taking into account store operating status and event information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting food consumption data, improve the accuracy of the collection based on the type and quality of the food. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting food consumption data, the geographical location of stores is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting food consumption data, we analyze the store's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized based on the consumption patterns of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analytical methods are applied depending on the frequency of consumption and usage of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is adjusted based on when the ingredients were consumed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature on the ingredients to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, It estimates user sentiment and adjusts inventory optimization methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, When optimizing inventory, the optimization algorithm is optimized by referring to past inventory data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, When optimizing inventory, the expiration date and quality information of ingredients are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, It estimates user sentiment and determines inventory priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, When optimizing inventory, the optimal inventory management method is selected by considering the geographical location of the stores. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, When optimizing inventory, referencing relevant literature on ingredients improves the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, The system estimates the user's emotions and adjusts how new menu items are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When proposing new menu items, we optimize the suggestion algorithm by considering food consumption patterns and trends. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When proposing new menu items, different proposal methods are applied depending on the category and quality of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates user sentiment and prioritizes new menu items based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing new menu items, adjust the priority of suggestions based on the consumption period of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When proposing new menu items, we refer to relevant literature on ingredients to improve the accuracy of the proposals. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 unit that collects food consumption data, An analysis unit analyzes the data collected by the aforementioned collection unit, An optimization unit optimizes inventory based on the analysis results obtained by the analysis unit, A proposal unit proposes new menus based on the inventory optimized by the aforementioned optimization unit, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, We propose new menu items that take into account the season and current trends. The system according to feature 1.

3. The optimization unit, Implement management measures to prevent inventory shortages and excess inventory. The system according to feature 1.

4. The aforementioned collection unit is Collect food consumption data in real time. The system according to feature 1.

5. The aforementioned analysis unit, We analyze past consumption data to provide data for maintaining optimal inventory levels. The system according to feature 1.

6. The aforementioned proposal section is, The AI ​​generates and automatically suggests new menu items. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of food consumption data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting food consumption data, we optimize the collection method by taking into account store operating status and event information. The system according to feature 1.

Images