system

Abstract

The system according to this embodiment aims to enable the selection of the optimal rice for each menu item and efficient food ingredient management. [Solution] The system according to this embodiment comprises an acquisition unit, an analysis unit, a suggestion unit, and a management unit. The acquisition unit acquires menu information. The analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit. The suggestion unit suggests a special blend of rice based on the information analyzed by the analysis unit. The management unit manages the inventory and ordering of the special blend of rice suggested by the suggestion 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that the selection of rice suitable for the menu is complicated and time-consuming, and the food material management is not efficient and waste is likely to occur.

[0005] The system according to the embodiment aims to realize an optimal selection of rice suitable for the menu and efficient food material management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a suggestion unit, and a management unit. The acquisition unit acquires menu information. The analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit. The suggestion unit suggests a special blend of rice based on the information analyzed by the analysis unit. The management unit manages the inventory and ordering of the special blend of rice suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable the selection of the optimal rice for each menu item and efficient food ingredient management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that utilizes a recipe management system and a generating AI to suggest the most suitable rice for a restaurant's menu and streamline ingredient management. First, the system works in conjunction with existing recipe management software, and the AI ​​acquires menu information. Next, the AI ​​analyzes and suggests the most suitable type of rice for each dish. Furthermore, the generating AI reflects the restaurant's preferences and incorporates the expertise of rice suppliers to suggest a specially blended rice. Finally, the AI ​​centrally manages the inventory and ordering of the optimal rice within the supply chain system, reducing unnecessary stock and achieving efficient distribution. This mechanism makes it easy to select the most suitable rice for each menu, allowing restaurants to differentiate themselves through original blended rice. In addition, ingredient management and distribution efficiency are improved, and costs can be reduced. For example, the system works in conjunction with existing recipe management software, and the AI ​​acquires menu information. At this time, the recipe information for each dish is analyzed in detail to identify which type of rice is most suitable. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. This allows the system to analyze and suggest the most suitable type of rice for each dish. Next, the generating AI reflects the restaurant's preferences and incorporates the rice supplier's expertise to propose a special blend of rice. For example, in response to requests such as wanting to use a specific brand of rice or adjusting the ratio of new to old rice, the generating AI proposes the optimal blend. This allows restaurants to offer their own unique blends, differentiating them from other restaurants. Furthermore, the AI ​​centrally manages the optimal rice inventory and ordering through a supply chain system. For example, the AI ​​monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. This reduces unnecessary stock and enables efficient distribution. This makes it easy to select the optimal rice for each menu item. By using the optimal rice for each dish, restaurants can improve the quality of their cuisine. They can also differentiate themselves through their original blended rice. In addition, ingredient management and distribution efficiency are improved, and costs can be reduced. For example, by reducing unnecessary stock, the effort of inventory management is reduced, and costs can be lowered.In this way, by utilizing a recipe management system and generation AI, it is possible to suggest the most suitable rice for a restaurant's menu and streamline ingredient management. This allows restaurants to provide high-quality dishes and improve customer satisfaction. The system can suggest the most suitable rice for a restaurant's menu and streamline ingredient management.

[0029] The system according to this embodiment comprises an acquisition unit, an analysis unit, a proposal unit, and a management unit. The acquisition unit acquires menu information. The acquisition unit can acquire menu information by, for example, cooperating with existing recipe management software. The acquisition unit needs to clarify the specific content and format of the menu information. For example, this includes the name of the dish, ingredients, and cooking method. The analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit. The analysis unit, for example, analyzes the recipe information for each dish in detail to identify which type of rice is optimal. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. The analysis unit needs to clarify the specific criteria and selection method for the recommended type of rice for each dish. For example, this includes taste, texture, and nutritional value. The proposal unit proposes a special blend of rice based on the information analyzed by the analysis unit. The proposal unit proposes a special blend of rice using a generating AI. For example, in response to requests such as wanting to use a specific brand of rice or wanting to adjust the ratio of new rice to old rice, the generating AI proposes the optimal blend of rice. The proposal department needs to clarify the specific details and creation method of the special blended rice. This includes, for example, the type of rice used and the blending ratio. The management department manages the inventory and ordering of the special blended rice proposed by the proposal department. For example, the management department monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. The management department needs to clarify the specific details and management method of the inventory. This includes, for example, the type of inventory, quantity, and storage method. The management department needs to clarify the specific methods and criteria for ordering. This includes, for example, the timing of ordering, the order quantity, and the supplier. As a result, the system according to the embodiment makes it easy to select the optimal rice to match the menu and differentiates the restaurant through its original blended rice.

[0030] The acquisition unit retrieves menu information. For example, it can integrate with existing recipe management software to acquire menu information. Specifically, the acquisition unit communicates with the recipe management software via an API and automatically retrieves detailed menu information such as dish name, ingredients, and cooking method. The acquisition unit stores this information in a standardized format, allowing the subsequent analysis unit to process it efficiently. For example, dish names are stored as strings, and ingredients are stored as lists. Cooking methods are stored step by step, with each step containing detailed information such as required time and temperature. The acquisition unit allows setting the update frequency of menu information and periodically retrieves new menu information. The acquisition unit also provides an interface for users to manually input menu information, enabling flexible data acquisition. This allows the acquisition unit to support both automatic acquisition from recipe management software and manual user input, enabling the collection of comprehensive menu information. Furthermore, the acquisition unit stores the acquired menu information in a database and can integrate with other systems and departments as needed. For example, the acquired menu information can be stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, the data acquisition unit can adjust the frequency and accuracy of data acquisition, enabling flexible responses to specific situations and conditions. This allows the acquisition unit to efficiently and effectively collect menu information, thereby improving the overall system performance.

[0031] The analysis department analyzes the recommended type of rice for each dish based on the menu information acquired by the data acquisition department. Specifically, the analysis department analyzes the recipe information for each dish in detail to identify which type of rice is optimal. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. The analysis department needs to clarify specific criteria and selection methods for the recommended type of rice for each dish. These criteria include, for example, taste, texture, and nutritional value. The analysis department uses AI to identify the type of rice that meets these criteria. Based on past data and user feedback, the AI ​​learns the optimal type of rice for each dish and performs highly accurate analysis. For example, the AI ​​analyzes the recipe information for sushi, identifies that sticky rice is necessary, and suggests an appropriate brand. It also analyzes the recipe information for curry, identifies that firmer rice is suitable, and suggests the optimal brand. Furthermore, the analysis department can dynamically adjust the rice selection criteria according to the type of dish and cooking method. For example, even for the same dish, different types of rice may be suitable depending on the cooking method. This allows the analysis department to accurately identify the optimal type of rice for each dish and provide this information to the proposal department.

[0032] The Proposal Department proposes special blended rice based on information analyzed by the Analysis Department. The Proposal Department uses a generative AI to propose special blended rice. Specifically, the Proposal Department uses the generative AI to propose the optimal blended rice in response to requests such as wanting to use a specific brand of rice or wanting to adjust the ratio of new rice to old rice. The generative AI calculates and proposes the optimal blending ratio based on the user's requests and the type of dish. For example, for sushi, a blend of sticky rice and fragrant rice is suggested, and for curry, a blend of firm rice and sweet rice is suggested. The Proposal Department needs to clarify the specific contents and creation method of the special blended rice. This includes, for example, the types of rice to be used and the blending ratio. The Proposal Department provides the user with information on the blended rice proposed by the generative AI, so that the user can easily create their own blended rice. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy of the generative AI's suggestions. For example, the user can provide feedback on the results of trying the suggested blended rice, and the generative AI will learn from this information and reflect it in future suggestions. This allows the proposal department to suggest the optimal blend of rice that meets the user's needs, thereby improving overall satisfaction with the system.

[0033] The Management Department manages the inventory and ordering of the special blend rice proposed by the Proposal Department. Specifically, the Management Department monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. The Management Department needs to clarify the specific contents and management methods of the inventory. For example, this includes the type of inventory, quantity, and storage method. The Management Department also needs to clarify the specific methods and criteria for ordering. For example, this includes the timing of orders, order quantity, and supplier. The Management Department uses an inventory management system to grasp the inventory status in real time and automatically place orders at the necessary time. For example, if the inventory falls below a certain amount, the system will automatically place an order and replenish the inventory. The Management Department also selects suppliers and adjusts order quantities to procure rice at the optimal cost. Furthermore, the Management Department optimizes the storage methods of the inventory to maintain the quality of the rice. For example, it controls temperature and humidity to store the rice in a way that prevents deterioration. In this way, the Management Department can efficiently manage the inventory and ordering of the special blend rice and support the operation of the restaurant. Furthermore, the management department can analyze inventory levels and order history to predict future demand. This allows the management department to maintain optimal inventory levels at all times and reduce unnecessary inventory.

[0034] The proposal department can propose special blended rice using generative AI. For example, the proposal department can use generative AI to propose special blended rice using a specific brand of rice or by adjusting the ratio of new rice to old rice. By using generative AI, the proposal department can efficiently propose special blended rice. For example, the generative AI proposes special blended rice based on the algorithm and training data used. The generative AI needs to clarify the specific contents and creation method of the special blended rice. For example, this includes the type of rice used and the blending ratio. As a result, the proposal department can propose special blended rice using generative AI.

[0035] The analysis department can analyze the recipe information for each dish in detail to identify which type of rice is best suited. For example, the analysis department needs to clearly define specific criteria and selection methods for the recommended rice type for each dish. These criteria may include factors such as taste, texture, and nutritional value. The analysis department can use AI to analyze the recipe information for each dish in detail. For example, the AI ​​can take the recipe information for each dish as input and output the optimal rice type. This allows the analysis department to identify the best rice type for each dish, thereby improving the quality of the dishes.

[0036] The management department can monitor the restaurant's inventory status in real time and order the optimal amount of rice at the necessary time. For example, the management department can monitor the restaurant's inventory status in real time and order the optimal amount of rice at the necessary time. The management department needs to clarify the specific contents and management methods of the inventory. For example, this includes the type of inventory, quantity, and storage method. The management department needs to clarify the specific methods and criteria for ordering. For example, this includes the timing of orders, order quantity, and supplier. The management department can use AI to monitor inventory status in real time. For example, the AI ​​can take inventory status as input and output the optimal ordering timing. This allows the management department to reduce unnecessary stock and achieve efficient distribution.

[0037] The proposal department can propose special blends of rice that reflect the restaurant's preferences and incorporate the expertise of rice suppliers, adjusting the ratio of new to old rice to match specific rice varieties. For example, the proposal department can propose special blends of rice that reflect the restaurant's preferences and incorporate the expertise of rice suppliers, adjusting the ratio of new to old rice to match specific rice varieties. The proposal department uses a generation AI to propose special blends of rice. For example, the generation AI takes the restaurant's preferences as input and outputs special blends of rice. The generation AI needs to clearly define the specific contents and creation method of the special blends of rice. For example, this includes the types of rice used and the blending ratio. In this way, the proposal department can differentiate itself from other restaurants by proposing special blends of rice that meet the restaurant's needs.

[0038] The management department can reduce unnecessary stock and achieve efficient distribution. For example, the management department can reduce unnecessary stock and achieve efficient distribution. The management department needs to clarify the specific details and management methods of inventory. This includes, for example, the type, quantity, and storage method of inventory. The management department also needs to clarify the specific methods and criteria for ordering. This includes, for example, the timing of orders, the order quantity, and the supplier. The management department can reduce unnecessary stock using AI. For example, the AI ​​takes inventory status as input and outputs the optimal ordering timing. This allows the management department to reduce costs and achieve efficient distribution.

[0039] The acquisition unit can analyze the user's past menu selection history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past menu selection history and selects the optimal acquisition method. The acquisition unit needs to clarify the specific content and analysis method of the past menu selection history. For example, this includes selection frequency and selection trends. The acquisition unit needs to clarify the specific criteria and selection method for the optimal acquisition method. For example, this includes user preferences and past data. The acquisition unit can analyze the user's past menu selection history using AI. For example, the AI ​​takes past menu selection history as input and outputs the optimal acquisition method. For example, the acquisition unit can analyze the trends of menus the user has selected in the past and prioritize the acquisition of similar menu information. In addition, the acquisition unit can predict the menus the user will select during a specific time period and acquire menu information accordingly. Furthermore, the acquisition unit can predict the menus selected on a specific day of the week based on the user's past selection history and acquire menu information accordingly. In this way, the acquisition unit can acquire the most suitable menu information for the user by analyzing past menu selection history.

[0040] The acquisition unit can filter menu information based on the user's current ingredient inventory status. For example, the acquisition unit can filter menu information based on the user's current ingredient inventory status. The acquisition unit needs to clarify the specific details and acquisition method of ingredient inventory status. For example, this includes the type, quantity, and expiration date of the inventory. The acquisition unit also needs to clarify the specific methods and criteria for filtering. For example, this includes the availability of the inventory and the frequency of use. The acquisition unit can use AI to analyze the user's current ingredient inventory status. For example, the AI ​​takes ingredient inventory status as input and outputs the filtering results. For example, the acquisition unit can check the user's current ingredient inventory and prioritize acquiring menu information that uses ingredients that are in stock. Furthermore, the acquisition unit can suggest menu information to replenish any missing ingredients based on the user's inventory status. In addition, the acquisition unit can monitor the user's inventory status in real time and acquire new menu information when inventory decreases. As a result, the acquisition unit can filter menu information based on the current ingredient inventory status, enabling efficient menu suggestions.

[0041] The acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location when acquiring menu information. For example, when acquiring menu information, the acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location. The acquisition unit needs to clarify the specific methods for acquiring and using geographical location information. For example, this includes GPS data and location information services. The acquisition unit needs to clarify the specific criteria and selection methods for highly relevant menu information. For example, this includes local specialties and seasonal ingredients. The acquisition unit can analyze the user's geographical location information using AI. For example, the AI ​​takes geographical location information as input and outputs highly relevant menu information. For example, if the user is in a specific region, the acquisition unit can prioritize the acquisition of menu information using local specialties of that region. Also, if the user is traveling, the acquisition unit can prioritize the acquisition of menu information of local cuisine at the travel destination. Furthermore, if the user is at home, the acquisition unit can prioritize the acquisition of menu information using ingredients available at nearby supermarkets. In this way, the acquisition unit can provide highly relevant menu information to the user by considering geographical location information.

[0042] The acquisition unit can analyze a user's social media activity when acquiring menu information and acquire relevant menu information. For example, when acquiring menu information, the acquisition unit analyzes a user's social media activity and acquires relevant menu information. The acquisition unit needs to clarify the specific content and analysis method of social media activity. For example, this includes the content of posts and the number of likes. The acquisition unit needs to clarify the specific criteria and selection method for relevant menu information. For example, this includes the user's interests and trends. The acquisition unit can analyze a user's social media activity using AI. For example, the AI ​​takes social media activity as input and outputs relevant menu information. For example, the acquisition unit can analyze photos of dishes shared by a user on social media and acquire similar menu information. The acquisition unit can also analyze posts from food accounts that a user follows and acquire relevant menu information. Furthermore, the acquisition unit can analyze food posts that a user has "liked" on social media and acquire relevant menu information. In this way, the acquisition unit can provide menu information relevant to the user by analyzing social media activity.

[0043] The analysis department can adjust the level of detail in its analysis based on the importance of the dishes. For example, the analysis department can adjust the level of detail in its analysis based on the importance of the dishes. The analysis department needs to clarify specific criteria and evaluation methods for the importance of dishes. For example, these may include popularity and sales. The analysis department needs to clarify specific methods and criteria for adjusting the level of detail in its analysis. For example, these may include the number of analysis items and the depth of the analysis. The analysis department can use AI to evaluate the importance of dishes. For example, the AI ​​takes the importance of the dishes as input and outputs evaluation results. For example, in the case of a main dish, the analysis department can perform a detailed analysis and explain in detail the type of rice used and the cooking method. In the case of a side dish, the analysis department can perform a concise analysis and provide only basic information. Furthermore, in the case of a dessert, the analysis department can perform an analysis that emphasizes the type of rice used and the balance of sweetness. This allows the analysis department to provide more appropriate analysis results by adjusting the level of detail in its analysis based on the importance of the dishes.

[0044] The analysis unit can apply different analysis algorithms depending on the category of cuisine during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of cuisine during analysis. The analysis unit needs to clarify the specific classification methods and criteria for cuisine categories. For example, these include Japanese cuisine, Western cuisine, and Chinese cuisine. The analysis unit also needs to clarify the specific types and application methods of analysis algorithms. For example, these include clustering and regression analysis. The analysis unit can use AI to apply analysis algorithms appropriate to the category of cuisine. For example, the AI ​​takes the category of cuisine as input and outputs the analysis algorithm to apply. For example, in the case of Japanese cuisine, the analysis unit can apply an analysis algorithm based on traditional cooking methods and the type of rice used. In the case of Western cuisine, the analysis unit can apply an analysis algorithm based on cooking time and the combination of ingredients used. Furthermore, in the case of Chinese cuisine, the analysis unit can apply an analysis algorithm based on seasonings and cooking methods. As a result, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of cuisine.

[0045] The analysis department can prioritize analyses based on the submission timing of dishes. For example, the analysis department can prioritize analyses based on the submission timing of dishes. The analysis department needs to clarify specific criteria and methods for determining submission timing, such as season and events. The analysis department also needs to clarify specific criteria and methods for determining priorities, such as submission deadlines and importance. The analysis department can use AI to evaluate the submission timing of dishes. For example, the AI ​​takes the submission timing as input and outputs evaluation results. The analysis department can, for example, prioritize the analysis of dishes that will be served soon, allowing for quick results. The analysis department can also prioritize the analysis of regularly served dishes to maintain consistent quality. Furthermore, the analysis department can prioritize the analysis of seasonal dishes, enabling suggestions that utilize seasonal ingredients. By prioritizing analyses based on the submission timing of dishes, the analysis department can provide more appropriate analysis results.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the dishes during the analysis process. For example, the analysis unit can adjust the order of analysis based on the relevance of the dishes during the analysis process. The analysis unit needs to clarify specific criteria and evaluation methods for the relevance of dishes. For example, this includes commonality of ingredients and similarity of cooking methods. The analysis unit needs to clarify specific methods and criteria for adjusting the order of analysis. For example, this includes ordering by relevance or importance. The analysis unit can use AI to evaluate the relevance of dishes. For example, the AI ​​takes the relevance of dishes as input and outputs evaluation results. The analysis unit can consider the relevance of main dishes and side dishes and analyze them simultaneously. Furthermore, the analysis unit can consider the relevance of desserts and appetizers and adjust the order of analysis. In addition, the analysis unit can consider the relevance of dishes that use the same ingredients and perform analysis efficiently. As a result, the analysis unit can perform more efficient analysis by adjusting the order of analysis based on the relevance of dishes.

[0047] The proposal department can adjust the level of detail in a proposal based on the importance of the specially blended rice. For example, the proposal department can adjust the level of detail in a proposal based on the importance of the specially blended rice. The proposal department needs to clarify the specific criteria and evaluation methods for the importance of the specially blended rice. For example, this could include sales figures and customer reviews. The proposal department needs to clarify the specific methods and criteria for adjusting the level of detail in a proposal. For example, this could include the depth of the proposal content and the number of proposal items. The proposal department can use AI to evaluate the importance of the specially blended rice. For example, the AI ​​takes the importance of the specially blended rice as input and outputs the evaluation result. For example, in the case of a main dish, the proposal department can make a detailed proposal, explaining in detail the type of rice to use and the cooking method. Also, in the case of a side dish, the proposal department can make a concise proposal, providing only basic information. Furthermore, in the case of a dessert, the proposal department can make a proposal that emphasizes the type of rice to use and the balance of sweetness. As a result, the proposal department can make more appropriate proposals by adjusting the level of detail in a proposal based on the importance of the specially blended rice.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the special blended rice when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the category of the special blended rice when making a proposal. The proposal unit needs to clarify the specific classification method and criteria for the category of special blended rice. For example, this includes the type of rice used and the blending ratio. The proposal unit needs to clarify the specific type and application method of the proposal algorithm. For example, this includes recommendation systems and machine learning algorithms. The proposal unit can use AI to apply a proposal algorithm according to the category of the special blended rice. For example, the AI ​​takes the category of the special blended rice as input and outputs the proposal algorithm to apply. For example, in the case of Japanese cuisine, the proposal unit can apply a proposal algorithm based on traditional cooking methods and the type of rice used. In the case of Western cuisine, the proposal unit can apply a proposal algorithm based on cooking time and the combination of ingredients used. Furthermore, in the case of Chinese cuisine, the proposal unit can apply a proposal algorithm based on seasonings and cooking methods. As a result, the proposal unit can make more appropriate proposals by applying different proposal algorithms depending on the category of the special blended rice.

[0049] The proposal department can prioritize proposals based on the submission timing of the special blend rice. For example, the proposal department can prioritize proposals based on the submission timing of the special blend rice. The proposal department needs to clarify the specific criteria and methods for determining the submission timing. For example, this could include seasons, events, etc. The proposal department also needs to clarify the specific methods and criteria for determining priority. For example, this could include submission deadlines, importance, etc. The proposal department can use AI to evaluate the submission timing of the special blend rice. For example, the AI ​​takes the submission timing as input and outputs evaluation results. For example, the proposal department can prioritize proposals for special blend rice that will be provided soon and provide results quickly. The proposal department can also prioritize proposals for special blend rice that will be provided regularly and maintain consistent quality. Furthermore, the proposal department can prioritize proposals for seasonal special blend rice and make proposals that utilize seasonal ingredients. As a result, the proposal department can make more appropriate proposals by prioritizing proposals based on the submission timing of the special blend rice.

[0050] The proposal unit can adjust the order of proposals based on the relationships between specially blended rice dishes when making proposals. For example, the proposal unit can adjust the order of proposals based on the relationships between specially blended rice dishes when making proposals. The proposal unit needs to clarify specific criteria and evaluation methods for the relationships between specially blended rice dishes. For example, this includes the type of rice used and the blending ratio. The proposal unit needs to clarify specific methods and criteria for adjusting the order of proposals. For example, this includes sorting by relevance and sorting by importance. The proposal unit can use AI to evaluate the relationships between specially blended rice dishes. For example, the AI ​​takes the relationships between specially blended rice dishes as input and outputs the evaluation results. For example, the proposal unit can consider the relationships between main dishes and side dishes and make proposals simultaneously. The proposal unit can also consider the relationships between desserts and appetizers and adjust the order when making proposals. Furthermore, the proposal unit can consider the relationships between specially blended rice dishes that use the same ingredients and make proposals efficiently. As a result, the proposal unit can make more efficient proposals by adjusting the order of proposals based on the relationships between specially blended rice dishes.

[0051] The management department can analyze users' past consumption behavior to select the optimal inventory management method during inventory management. For example, the management department can analyze users' past consumption behavior to select the optimal inventory management method. The management department needs to clarify the specific content and analysis methods of past consumption behavior. For example, this includes purchase history and consumption patterns. The management department needs to clarify the specific criteria and selection methods for the optimal inventory management method. For example, this includes demand forecasting and consumption volume. The management department can use AI to analyze users' past consumption behavior. For example, the AI ​​can take past consumption behavior as input and output the optimal inventory management method. For example, the management department can analyze users' past consumption patterns and prioritize the management of inventory of frequently used ingredients. In addition, the management department can predict and manage the inventory of ingredients needed at specific times based on users' consumption behavior. Furthermore, the management department can propose an inventory management method that minimizes waste based on users' past consumption history. In this way, the management department can select the optimal inventory management method by analyzing past consumption behavior.

[0052] The management department can customize inventory management methods based on the user's current lifestyle. For example, the management department can customize inventory management methods based on the user's current lifestyle. The management department needs to clarify the specific details and methods of obtaining the user's current lifestyle. For example, this may include family structure and lifestyle patterns. The management department also needs to clarify the specific methods and criteria for customizing inventory management methods. For example, this may include inventory type, quantity, and storage method. The management department can use AI to analyze the user's current lifestyle. For example, the AI ​​can take the user's current lifestyle as input and output customized inventory management methods. For example, the management department can provide a way for users to easily check inventory status when they are busy. Furthermore, if a user is traveling, the management department can automate inventory management during their trip. In addition, if a user prioritizes health management, the management department can provide health-conscious inventory management methods. As a result, the management department can enable more appropriate inventory management by customizing inventory management methods based on the user's current lifestyle.

[0053] The management department can select the optimal inventory management method when managing inventory, taking into account the user's geographical location information. For example, the management department can select the optimal inventory management method when managing inventory, taking into account the user's geographical location information. The management department needs to clarify the specific methods for acquiring and using geographical location information. For example, this includes GPS data and location information services. The management department needs to clarify the specific criteria and selection methods for the optimal inventory management method. For example, this includes demand forecasting and consumption volume. The management department can use AI to analyze the user's geographical location information. For example, the AI ​​can take geographical location information as input and output the optimal inventory management method. For example, if the user is in a specific region, the management department can prioritize inventory management of local specialty products. Also, if the user is traveling, the management department can manage inventory considering ingredients available at the travel destination. Furthermore, if the user is at home, the management department can prioritize inventory management of ingredients available at nearby supermarkets. In this way, the management department can select the optimal inventory management method by taking geographical location information into account.

[0054] The management department can analyze users' social media activity and propose inventory management methods during inventory management. For example, the management department can analyze users' social media activity and propose inventory management methods during inventory management. The management department needs to clarify the specific content and analysis methods of social media activity. For example, this includes the content of posts and the number of likes. The management department needs to clarify the specific methods and criteria for proposing inventory management methods. For example, this includes the type of inventory, quantity, and storage method. The management department can use AI to analyze users' social media activity. For example, the AI ​​can take social media activity as input and output inventory management methods. For example, the management department can analyze photos of dishes shared by users on social media and manage the inventory of related ingredients. Furthermore, the management department can analyze posts from cooking accounts that users follow and manage the inventory of related ingredients. In addition, the management department can analyze posts of dishes that users "like" on social media and manage the inventory of related ingredients. As a result, the management department can propose the optimal inventory management method by analyzing social media activity.

[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 data acquisition unit can acquire the user's dietary preferences and allergy information and filter menu information based on this information. For example, if a user is allergic to a specific ingredient, menu information containing that ingredient can be excluded. Also, if a user likes a particular ingredient, menu information containing that ingredient can be prioritized. Furthermore, the data acquisition unit can update the user's dietary preferences and allergy information in real time and acquire menu information based on the latest information. As a result, the data acquisition unit can provide menu information that takes into account the user's health and preferences.

[0057] The suggestion unit can analyze a user's past menu selection history and suggest a special blend of rice based on this analysis. For example, it can analyze the trends in menus a user has selected in the past and suggest a special blend of rice that would suit similar menus. It can also predict the menus a user will select at a specific time of day and suggest a special blend of rice that would suit that time. Furthermore, based on the user's past selection history, it can suggest a special blend of rice that would suit the menus selected on specific days of the week. In this way, the suggestion unit can provide a special blend of rice tailored to the user's preferences.

[0058] The data acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the acquisition of menu information using local specialties from that region. Also, if the user is traveling, it can prioritize the acquisition of menu information featuring local cuisine from their travel destination. Furthermore, if the user is at home, it can prioritize the acquisition of menu information using ingredients available at nearby supermarkets. In this way, the data acquisition unit can provide the user with highly relevant menu information by considering their geographical location.

[0059] The management department can analyze users' past consumption behavior to select the optimal inventory management method. For example, by analyzing users' past consumption patterns, they can prioritize the management of inventory for frequently used ingredients. They can also predict and manage the inventory of ingredients needed at specific times based on users' consumption behavior. Furthermore, they can propose inventory management methods that minimize waste based on users' past consumption history. In this way, the management department can select the optimal inventory management method by analyzing past consumption behavior.

[0060] The analysis department can adjust the level of detail in its analysis based on the importance of each dish. For example, for a main dish, a detailed analysis can be performed, providing a detailed explanation of the type of rice used and the cooking method. For a side dish, a concise analysis can be performed, providing only basic information. Furthermore, for desserts, an analysis can be performed that emphasizes the type of rice used and the balance of sweetness. In this way, the analysis department can provide more appropriate analysis results by adjusting the level of detail in its analysis based on the importance of each dish.

[0061] The acquisition unit can analyze a user's social media activity and obtain relevant menu information. For example, it can analyze photos of dishes a user has shared on social media and obtain similar menu information. It can also analyze posts from food accounts a user follows and obtain relevant menu information. Furthermore, it can analyze food posts a user has "liked" on social media and obtain relevant menu information. In this way, the acquisition unit can provide menu information relevant to the user by analyzing their social media activity.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The acquisition unit acquires menu information. The acquisition unit can acquire menu information by, for example, linking with existing recipe management software. The acquisition unit needs to clarify the specific content and format of the menu information. For example, this may include the dish name, ingredients, and cooking method. Step 2: The analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit. The analysis unit, for example, analyzes the recipe information for each dish in detail to identify which type of rice is best. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. The analysis unit needs to clarify specific criteria and selection methods for the recommended type of rice for each dish. For example, these may include taste, texture, and nutritional value. Step 3: The Proposal Department proposes a special blend of rice based on the information analyzed by the Analysis Department. The Proposal Department uses a generating AI to propose a special blend of rice. The Proposal Department's generating AI proposes the optimal blend of rice in response to requests such as wanting to use a specific brand of rice or wanting to adjust the ratio of new rice to old rice. The Proposal Department needs to clarify the specific contents and creation method of the special blend of rice. This includes, for example, the types of rice to be used and the blending ratio. Step 4: The management department manages the inventory and ordering of the special blend rice proposed by the proposal department. For example, the management department monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. The management department needs to clarify the specific contents and management methods of the inventory. For example, this includes the type of inventory, quantity, and storage method. The management department needs to clarify the specific methods and criteria for ordering. For example, this includes the timing of orders, order quantity, and supplier.

[0064] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes a recipe management system and a generating AI to suggest the most suitable rice for a restaurant's menu and streamline ingredient management. First, the system works in conjunction with existing recipe management software, and the AI ​​acquires menu information. Next, the AI ​​analyzes and suggests the most suitable type of rice for each dish. Furthermore, the generating AI reflects the restaurant's preferences and incorporates the expertise of rice suppliers to suggest a specially blended rice. Finally, the AI ​​centrally manages the inventory and ordering of the optimal rice within the supply chain system, reducing unnecessary stock and achieving efficient distribution. This mechanism makes it easy to select the most suitable rice for each menu, allowing restaurants to differentiate themselves through original blended rice. In addition, ingredient management and distribution efficiency are improved, and costs can be reduced. For example, the system works in conjunction with existing recipe management software, and the AI ​​acquires menu information. At this time, the recipe information for each dish is analyzed in detail to identify which type of rice is most suitable. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. This allows the system to analyze and suggest the most suitable type of rice for each dish. Next, the generating AI reflects the restaurant's preferences and incorporates the rice supplier's expertise to propose a special blend of rice. For example, in response to requests such as wanting to use a specific brand of rice or adjusting the ratio of new to old rice, the generating AI proposes the optimal blend. This allows restaurants to offer their own unique blends, differentiating them from other restaurants. Furthermore, the AI ​​centrally manages the optimal rice inventory and ordering through a supply chain system. For example, the AI ​​monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. This reduces unnecessary stock and enables efficient distribution. This makes it easy to select the optimal rice for each menu item. By using the optimal rice for each dish, restaurants can improve the quality of their cuisine. They can also differentiate themselves through their original blended rice. In addition, ingredient management and distribution efficiency are improved, and costs can be reduced. For example, by reducing unnecessary stock, the effort of inventory management is reduced, and costs can be lowered.In this way, by utilizing a recipe management system and generation AI, it is possible to suggest the most suitable rice for a restaurant's menu and streamline ingredient management. This allows restaurants to provide high-quality dishes and improve customer satisfaction. The system can suggest the most suitable rice for a restaurant's menu and streamline ingredient management.

[0065] The system according to this embodiment comprises an acquisition unit, an analysis unit, a proposal unit, and a management unit. The acquisition unit acquires menu information. The acquisition unit can acquire menu information by, for example, cooperating with existing recipe management software. The acquisition unit needs to clarify the specific content and format of the menu information. For example, this includes the name of the dish, ingredients, and cooking method. The analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit. The analysis unit, for example, analyzes the recipe information for each dish in detail to identify which type of rice is optimal. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. The analysis unit needs to clarify the specific criteria and selection method for the recommended type of rice for each dish. For example, this includes taste, texture, and nutritional value. The proposal unit proposes a special blend of rice based on the information analyzed by the analysis unit. The proposal unit proposes a special blend of rice using a generating AI. For example, in response to requests such as wanting to use a specific brand of rice or wanting to adjust the ratio of new rice to old rice, the generating AI proposes the optimal blend of rice. The proposal department needs to clarify the specific details and creation method of the special blended rice. This includes, for example, the type of rice used and the blending ratio. The management department manages the inventory and ordering of the special blended rice proposed by the proposal department. For example, the management department monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. The management department needs to clarify the specific details and management method of the inventory. This includes, for example, the type of inventory, quantity, and storage method. The management department needs to clarify the specific methods and criteria for ordering. This includes, for example, the timing of ordering, the order quantity, and the supplier. As a result, the system according to the embodiment makes it easy to select the optimal rice to match the menu and differentiates the restaurant through its original blended rice.

[0066] The acquisition unit retrieves menu information. For example, it can integrate with existing recipe management software to acquire menu information. Specifically, the acquisition unit communicates with the recipe management software via an API and automatically retrieves detailed menu information such as dish name, ingredients, and cooking method. The acquisition unit stores this information in a standardized format, allowing the subsequent analysis unit to process it efficiently. For example, dish names are stored as strings, and ingredients are stored as lists. Cooking methods are stored step by step, with each step containing detailed information such as required time and temperature. The acquisition unit allows setting the update frequency of menu information and periodically retrieves new menu information. The acquisition unit also provides an interface for users to manually input menu information, enabling flexible data acquisition. This allows the acquisition unit to support both automatic acquisition from recipe management software and manual user input, enabling the collection of comprehensive menu information. Furthermore, the acquisition unit stores the acquired menu information in a database and can integrate with other systems and departments as needed. For example, the acquired menu information can be stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, the data acquisition unit can adjust the frequency and accuracy of data acquisition, enabling flexible responses to specific situations and conditions. This allows the acquisition unit to efficiently and effectively collect menu information, thereby improving the overall system performance.

[0067] The analysis department analyzes the recommended type of rice for each dish based on the menu information acquired by the data acquisition department. Specifically, the analysis department analyzes the recipe information for each dish in detail to identify which type of rice is optimal. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. The analysis department needs to clarify specific criteria and selection methods for the recommended type of rice for each dish. These criteria include, for example, taste, texture, and nutritional value. The analysis department uses AI to identify the type of rice that meets these criteria. Based on past data and user feedback, the AI ​​learns the optimal type of rice for each dish and performs highly accurate analysis. For example, the AI ​​analyzes the recipe information for sushi, identifies that sticky rice is necessary, and suggests an appropriate brand. It also analyzes the recipe information for curry, identifies that firmer rice is suitable, and suggests the optimal brand. Furthermore, the analysis department can dynamically adjust the rice selection criteria according to the type of dish and cooking method. For example, even for the same dish, different types of rice may be suitable depending on the cooking method. This allows the analysis department to accurately identify the optimal type of rice for each dish and provide this information to the proposal department.

[0068] The Proposal Department proposes special blended rice based on information analyzed by the Analysis Department. The Proposal Department uses a generative AI to propose special blended rice. Specifically, the Proposal Department uses the generative AI to propose the optimal blended rice in response to requests such as wanting to use a specific brand of rice or wanting to adjust the ratio of new rice to old rice. The generative AI calculates and proposes the optimal blending ratio based on the user's requests and the type of dish. For example, for sushi, a blend of sticky rice and fragrant rice is suggested, and for curry, a blend of firm rice and sweet rice is suggested. The Proposal Department needs to clarify the specific contents and creation method of the special blended rice. This includes, for example, the types of rice to be used and the blending ratio. The Proposal Department provides the user with information on the blended rice proposed by the generative AI, so that the user can easily create their own blended rice. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy of the generative AI's suggestions. For example, the user can provide feedback on the results of trying the suggested blended rice, and the generative AI will learn from this information and reflect it in future suggestions. This allows the proposal department to suggest the optimal blend of rice that meets the user's needs, thereby improving overall satisfaction with the system.

[0069] The Management Department manages the inventory and ordering of the special blend rice proposed by the Proposal Department. Specifically, the Management Department monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. The Management Department needs to clarify the specific contents and management methods of the inventory. For example, this includes the type of inventory, quantity, and storage method. The Management Department also needs to clarify the specific methods and criteria for ordering. For example, this includes the timing of orders, order quantity, and supplier. The Management Department uses an inventory management system to grasp the inventory status in real time and automatically place orders at the necessary time. For example, if the inventory falls below a certain amount, the system will automatically place an order and replenish the inventory. The Management Department also selects suppliers and adjusts order quantities to procure rice at the optimal cost. Furthermore, the Management Department optimizes the storage methods of the inventory to maintain the quality of the rice. For example, it controls temperature and humidity to store the rice in a way that prevents deterioration. In this way, the Management Department can efficiently manage the inventory and ordering of the special blend rice and support the operation of the restaurant. Furthermore, the management department can analyze inventory levels and order history to predict future demand. This allows the management department to maintain optimal inventory levels at all times and reduce unnecessary inventory.

[0070] The proposal department can propose special blended rice using generative AI. For example, the proposal department can use generative AI to propose special blended rice using a specific brand of rice or by adjusting the ratio of new rice to old rice. By using generative AI, the proposal department can efficiently propose special blended rice. For example, the generative AI proposes special blended rice based on the algorithm and training data used. The generative AI needs to clarify the specific contents and creation method of the special blended rice. For example, this includes the type of rice used and the blending ratio. As a result, the proposal department can propose special blended rice using generative AI.

[0071] The analysis department can analyze the recipe information for each dish in detail to identify which type of rice is best suited. For example, the analysis department needs to clearly define specific criteria and selection methods for the recommended rice type for each dish. These criteria may include factors such as taste, texture, and nutritional value. The analysis department can use AI to analyze the recipe information for each dish in detail. For example, the AI ​​can take the recipe information for each dish as input and output the optimal rice type. This allows the analysis department to identify the best rice type for each dish, thereby improving the quality of the dishes.

[0072] The management department can monitor the restaurant's inventory status in real time and order the optimal amount of rice at the necessary time. For example, the management department can monitor the restaurant's inventory status in real time and order the optimal amount of rice at the necessary time. The management department needs to clarify the specific contents and management methods of the inventory. For example, this includes the type of inventory, quantity, and storage method. The management department needs to clarify the specific methods and criteria for ordering. For example, this includes the timing of orders, order quantity, and supplier. The management department can use AI to monitor inventory status in real time. For example, the AI ​​can take inventory status as input and output the optimal ordering timing. This allows the management department to reduce unnecessary stock and achieve efficient distribution.

[0073] The proposal department can propose special blends of rice that reflect the restaurant's preferences and incorporate the expertise of rice suppliers, adjusting the ratio of new to old rice to match specific rice varieties. For example, the proposal department can propose special blends of rice that reflect the restaurant's preferences and incorporate the expertise of rice suppliers, adjusting the ratio of new to old rice to match specific rice varieties. The proposal department uses a generation AI to propose special blends of rice. For example, the generation AI takes the restaurant's preferences as input and outputs special blends of rice. The generation AI needs to clearly define the specific contents and creation method of the special blends of rice. For example, this includes the types of rice used and the blending ratio. In this way, the proposal department can differentiate itself from other restaurants by proposing special blends of rice that meet the restaurant's needs.

[0074] The management department can reduce unnecessary stock and achieve efficient distribution. For example, the management department can reduce unnecessary stock and achieve efficient distribution. The management department needs to clarify the specific details and management methods of inventory. This includes, for example, the type, quantity, and storage method of inventory. The management department also needs to clarify the specific methods and criteria for ordering. This includes, for example, the timing of orders, the order quantity, and the supplier. The management department can reduce unnecessary stock using AI. For example, the AI ​​takes inventory status as input and outputs the optimal ordering timing. This allows the management department to reduce costs and achieve efficient distribution.

[0075] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring menu information based on the estimated emotions. The acquisition unit is implemented using an emotion estimation function, such as an emotion engine or generative AI. The generative AI, for example, takes the user's emotions as input and outputs the emotion estimation result. The acquisition unit needs to clarify the specific methods and criteria for adjusting the timing of acquiring menu information. For example, these include the time of day and the user's behavior patterns. For example, if the user is stressed, the AI ​​can delay acquiring menu information and wait until the user is relaxed. Also, if the user is relaxed, the AI ​​can immediately acquire menu information and quickly make suggestions. Furthermore, if the user is in a hurry, the AI ​​can prioritize acquiring menu information and quickly start analysis. In this way, the acquisition unit can acquire information at a more appropriate time by adjusting the timing of acquiring menu information according to the user's emotions.

[0076] The acquisition unit can analyze the user's past menu selection history and select the optimal acquisition method. For example, the acquisition unit analyzes the user's past menu selection history and selects the optimal acquisition method. The acquisition unit needs to clarify the specific content and analysis method of the past menu selection history. For example, this includes selection frequency and selection trends. The acquisition unit needs to clarify the specific criteria and selection method for the optimal acquisition method. For example, this includes user preferences and past data. The acquisition unit can analyze the user's past menu selection history using AI. For example, the AI ​​takes past menu selection history as input and outputs the optimal acquisition method. For example, the acquisition unit can analyze the trends of menus the user has selected in the past and prioritize the acquisition of similar menu information. In addition, the acquisition unit can predict the menus the user will select during a specific time period and acquire menu information accordingly. Furthermore, the acquisition unit can predict the menus selected on a specific day of the week based on the user's past selection history and acquire menu information accordingly. In this way, the acquisition unit can acquire the most suitable menu information for the user by analyzing past menu selection history.

[0077] The acquisition unit can filter menu information based on the user's current ingredient inventory status. For example, the acquisition unit can filter menu information based on the user's current ingredient inventory status. The acquisition unit needs to clarify the specific details and acquisition method of ingredient inventory status. For example, this includes the type, quantity, and expiration date of the inventory. The acquisition unit also needs to clarify the specific methods and criteria for filtering. For example, this includes the availability of the inventory and the frequency of use. The acquisition unit can use AI to analyze the user's current ingredient inventory status. For example, the AI ​​takes ingredient inventory status as input and outputs the filtering results. For example, the acquisition unit can check the user's current ingredient inventory and prioritize acquiring menu information that uses ingredients that are in stock. Furthermore, the acquisition unit can suggest menu information to replenish any missing ingredients based on the user's inventory status. In addition, the acquisition unit can monitor the user's inventory status in real time and acquire new menu information when inventory decreases. As a result, the acquisition unit can filter menu information based on the current ingredient inventory status, enabling efficient menu suggestions.

[0078] The acquisition unit can estimate the user's emotions and determine the priority of menu information to acquire based on the estimated emotions. For example, the acquisition unit estimates the user's emotions and determines the priority of menu information to acquire based on the estimated emotions. The acquisition unit is implemented using an emotion estimation function, such as an emotion engine or a generative AI. For example, the generative AI takes the user's emotions as input and outputs the emotion estimation result. The acquisition unit needs to clarify the specific method and criteria for determining priority. For example, this may include the user's preferences and past selection history. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring menu information with a relaxing effect. Also, if the user is relaxed, the acquisition unit can prioritize acquiring health-conscious menu information. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring menu information with a short cooking time. In this way, the acquisition unit can provide more appropriate menu information by determining the priority of menu information according to the user's emotions.

[0079] The acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location when acquiring menu information. For example, when acquiring menu information, the acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location. The acquisition unit needs to clarify the specific methods for acquiring and using geographical location information. For example, this includes GPS data and location information services. The acquisition unit needs to clarify the specific criteria and selection methods for highly relevant menu information. For example, this includes local specialties and seasonal ingredients. The acquisition unit can analyze the user's geographical location information using AI. For example, the AI ​​takes geographical location information as input and outputs highly relevant menu information. For example, if the user is in a specific region, the acquisition unit can prioritize the acquisition of menu information using local specialties of that region. Also, if the user is traveling, the acquisition unit can prioritize the acquisition of menu information of local cuisine at the travel destination. Furthermore, if the user is at home, the acquisition unit can prioritize the acquisition of menu information using ingredients available at nearby supermarkets. In this way, the acquisition unit can provide highly relevant menu information to the user by considering geographical location information.

[0080] The acquisition unit can analyze a user's social media activity when acquiring menu information and acquire relevant menu information. For example, when acquiring menu information, the acquisition unit analyzes a user's social media activity and acquires relevant menu information. The acquisition unit needs to clarify the specific content and analysis method of social media activity. For example, this includes the content of posts and the number of likes. The acquisition unit needs to clarify the specific criteria and selection method for relevant menu information. For example, this includes the user's interests and trends. The acquisition unit can analyze a user's social media activity using AI. For example, the AI ​​takes social media activity as input and outputs relevant menu information. For example, the acquisition unit can analyze photos of dishes shared by a user on social media and acquire similar menu information. The acquisition unit can also analyze posts from food accounts that a user follows and acquire relevant menu information. Furthermore, the acquisition unit can analyze food posts that a user has "liked" on social media and acquire relevant menu information. In this way, the acquisition unit can provide menu information relevant to the user by analyzing social media activity.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those estimated emotions. The analysis unit is implemented using emotion estimation functionality, such as an emotion engine or generative AI. The generative AI, for example, takes the user's emotions as input and outputs the estimated emotion. The analysis unit needs to clearly define specific methods and criteria for adjusting the presentation of the analysis. These include, for example, graph displays and text displays. For example, if the user is relaxed, the analysis unit can provide detailed analysis results, including background information on the food. If the user is in a hurry, the analysis unit can provide concise analysis results, displaying only the key points. Furthermore, if the user is excited, the analysis unit can display the analysis results using visually appealing graphs or charts. This allows the analysis unit to provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions.

[0082] The analysis department can adjust the level of detail in its analysis based on the importance of the dishes. For example, the analysis department can adjust the level of detail in its analysis based on the importance of the dishes. The analysis department needs to clarify specific criteria and evaluation methods for the importance of dishes. For example, these may include popularity and sales. The analysis department needs to clarify specific methods and criteria for adjusting the level of detail in its analysis. For example, these may include the number of analysis items and the depth of the analysis. The analysis department can use AI to evaluate the importance of dishes. For example, the AI ​​takes the importance of the dishes as input and outputs evaluation results. For example, in the case of a main dish, the analysis department can perform a detailed analysis and explain in detail the type of rice used and the cooking method. In the case of a side dish, the analysis department can perform a concise analysis and provide only basic information. Furthermore, in the case of a dessert, the analysis department can perform an analysis that emphasizes the type of rice used and the balance of sweetness. This allows the analysis department to provide more appropriate analysis results by adjusting the level of detail in its analysis based on the importance of the dishes.

[0083] The analysis unit can apply different analysis algorithms depending on the category of cuisine during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of cuisine during analysis. The analysis unit needs to clarify the specific classification methods and criteria for cuisine categories. For example, these include Japanese cuisine, Western cuisine, and Chinese cuisine. The analysis unit also needs to clarify the specific types and application methods of analysis algorithms. For example, these include clustering and regression analysis. The analysis unit can use AI to apply analysis algorithms appropriate to the category of cuisine. For example, the AI ​​takes the category of cuisine as input and outputs the analysis algorithm to apply. For example, in the case of Japanese cuisine, the analysis unit can apply an analysis algorithm based on traditional cooking methods and the type of rice used. In the case of Western cuisine, the analysis unit can apply an analysis algorithm based on cooking time and the combination of ingredients used. Furthermore, in the case of Chinese cuisine, the analysis unit can apply an analysis algorithm based on seasonings and cooking methods. As a result, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of cuisine.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit is implemented using emotion estimation capabilities, such as an emotion engine or generative AI. Generative AI, for example, takes the user's emotions as input and outputs the estimated emotions. The analysis unit needs to clearly define specific methods and criteria for adjusting the length of the analysis. These include, for example, analysis time and the volume of the analysis results. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions.

[0085] The analysis department can prioritize analyses based on the submission timing of dishes. For example, the analysis department can prioritize analyses based on the submission timing of dishes. The analysis department needs to clarify specific criteria and methods for determining submission timing, such as season and events. The analysis department also needs to clarify specific criteria and methods for determining priorities, such as submission deadlines and importance. The analysis department can use AI to evaluate the submission timing of dishes. For example, the AI ​​takes the submission timing as input and outputs evaluation results. The analysis department can, for example, prioritize the analysis of dishes that will be served soon, allowing for quick results. The analysis department can also prioritize the analysis of regularly served dishes to maintain consistent quality. Furthermore, the analysis department can prioritize the analysis of seasonal dishes, enabling suggestions that utilize seasonal ingredients. By prioritizing analyses based on the submission timing of dishes, the analysis department can provide more appropriate analysis results.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the dishes during the analysis process. For example, the analysis unit can adjust the order of analysis based on the relevance of the dishes during the analysis process. The analysis unit needs to clarify specific criteria and evaluation methods for the relevance of dishes. For example, this includes commonality of ingredients and similarity of cooking methods. The analysis unit needs to clarify specific methods and criteria for adjusting the order of analysis. For example, this includes ordering by relevance or importance. The analysis unit can use AI to evaluate the relevance of dishes. For example, the AI ​​takes the relevance of dishes as input and outputs evaluation results. The analysis unit can consider the relevance of main dishes and side dishes and analyze them simultaneously. Furthermore, the analysis unit can consider the relevance of desserts and appetizers and adjust the order of analysis. In addition, the analysis unit can consider the relevance of dishes that use the same ingredients and perform analysis efficiently. As a result, the analysis unit can perform more efficient analysis by adjusting the order of analysis based on the relevance of dishes.

[0087] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. The suggestion unit is implemented using emotion estimation functionality, such as an emotion engine or generative AI. Generative AI, for example, takes the user's emotions as input and outputs the estimated emotion. The suggestion unit needs to clearly define specific methods and criteria for adjusting the way suggestions are presented. These include, for example, visual and textual displays. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions, including background information on the dishes. If the user is in a hurry, the suggestion unit can provide concise suggestions, displaying only the essentials. Furthermore, if the user is excited, the suggestion unit can use visually appealing graphs and charts to present suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way suggestions are presented according to the user's emotions.

[0088] The proposal department can adjust the level of detail in a proposal based on the importance of the specially blended rice. For example, the proposal department can adjust the level of detail in a proposal based on the importance of the specially blended rice. The proposal department needs to clarify the specific criteria and evaluation methods for the importance of the specially blended rice. For example, this could include sales figures and customer reviews. The proposal department needs to clarify the specific methods and criteria for adjusting the level of detail in a proposal. For example, this could include the depth of the proposal content and the number of proposal items. The proposal department can use AI to evaluate the importance of the specially blended rice. For example, the AI ​​takes the importance of the specially blended rice as input and outputs the evaluation result. For example, in the case of a main dish, the proposal department can make a detailed proposal, explaining in detail the type of rice to use and the cooking method. Also, in the case of a side dish, the proposal department can make a concise proposal, providing only basic information. Furthermore, in the case of a dessert, the proposal department can make a proposal that emphasizes the type of rice to use and the balance of sweetness. As a result, the proposal department can make more appropriate proposals by adjusting the level of detail in a proposal based on the importance of the specially blended rice.

[0089] The proposal unit can apply different proposal algorithms depending on the category of the special blended rice when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the category of the special blended rice when making a proposal. The proposal unit needs to clarify the specific classification method and criteria for the category of special blended rice. For example, this includes the type of rice used and the blending ratio. The proposal unit needs to clarify the specific type and application method of the proposal algorithm. For example, this includes recommendation systems and machine learning algorithms. The proposal unit can use AI to apply a proposal algorithm according to the category of the special blended rice. For example, the AI ​​takes the category of the special blended rice as input and outputs the proposal algorithm to apply. For example, in the case of Japanese cuisine, the proposal unit can apply a proposal algorithm based on traditional cooking methods and the type of rice used. In the case of Western cuisine, the proposal unit can apply a proposal algorithm based on cooking time and the combination of ingredients used. Furthermore, in the case of Chinese cuisine, the proposal unit can apply a proposal algorithm based on seasonings and cooking methods. As a result, the proposal unit can make more appropriate proposals by applying different proposal algorithms depending on the category of the special blended rice.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, the suggestion unit estimates the user's emotions and adjusts the length of the suggestion based on those emotions. The suggestion unit is implemented using emotion estimation functionality, such as an emotion engine or generative AI. Generative AI, for example, takes the user's emotions as input and outputs the emotion estimation result. The suggestion unit needs to clearly define the specific methods and criteria for adjusting the length of the suggestion. These include, for example, the volume of the suggestion content and the suggestion duration. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions.

[0091] The proposal department can prioritize proposals based on the submission timing of the special blend rice. For example, the proposal department can prioritize proposals based on the submission timing of the special blend rice. The proposal department needs to clarify the specific criteria and methods for determining the submission timing. For example, this could include seasons, events, etc. The proposal department also needs to clarify the specific methods and criteria for determining priority. For example, this could include submission deadlines, importance, etc. The proposal department can use AI to evaluate the submission timing of the special blend rice. For example, the AI ​​takes the submission timing as input and outputs evaluation results. For example, the proposal department can prioritize proposals for special blend rice that will be provided soon and provide results quickly. The proposal department can also prioritize proposals for special blend rice that will be provided regularly and maintain consistent quality. Furthermore, the proposal department can prioritize proposals for seasonal special blend rice and make proposals that utilize seasonal ingredients. As a result, the proposal department can make more appropriate proposals by prioritizing proposals based on the submission timing of the special blend rice.

[0092] The proposal unit can adjust the order of proposals based on the relationships between specially blended rice dishes when making proposals. For example, the proposal unit can adjust the order of proposals based on the relationships between specially blended rice dishes when making proposals. The proposal unit needs to clarify specific criteria and evaluation methods for the relationships between specially blended rice dishes. For example, this includes the type of rice used and the blending ratio. The proposal unit needs to clarify specific methods and criteria for adjusting the order of proposals. For example, this includes sorting by relevance and sorting by importance. The proposal unit can use AI to evaluate the relationships between specially blended rice dishes. For example, the AI ​​takes the relationships between specially blended rice dishes as input and outputs the evaluation results. For example, the proposal unit can consider the relationships between main dishes and side dishes and make proposals simultaneously. The proposal unit can also consider the relationships between desserts and appetizers and adjust the order when making proposals. Furthermore, the proposal unit can consider the relationships between specially blended rice dishes that use the same ingredients and make proposals efficiently. As a result, the proposal unit can make more efficient proposals by adjusting the order of proposals based on the relationships between specially blended rice dishes.

[0093] The management department can estimate the user's emotions and adjust inventory management methods based on those estimated emotions. For example, the management department can estimate the user's emotions and adjust inventory management methods based on those estimated emotions. This is implemented using emotion estimation functionality, such as an emotion engine or generative AI. Generative AI, for example, takes the user's emotions as input and outputs the estimated emotion. The management department needs to clarify specific adjustment methods and criteria for inventory management. These include, for example, inventory turnover rate and ordering frequency. For example, if the user is stressed, the management department can provide a simple inventory management method to reduce the effort involved. Conversely, if the user is relaxed, the management department can provide a detailed inventory management method that allows for fine-tuning. Furthermore, if the user is in a hurry, the management department can provide a way to quickly check inventory status. This allows the management department to achieve more appropriate inventory management by adjusting inventory management methods according to the user's emotions.

[0094] The management department can analyze users' past consumption behavior to select the optimal inventory management method during inventory management. For example, the management department can analyze users' past consumption behavior to select the optimal inventory management method. The management department needs to clarify the specific content and analysis methods of past consumption behavior. For example, this includes purchase history and consumption patterns. The management department needs to clarify the specific criteria and selection methods for the optimal inventory management method. For example, this includes demand forecasting and consumption volume. The management department can use AI to analyze users' past consumption behavior. For example, the AI ​​can take past consumption behavior as input and output the optimal inventory management method. For example, the management department can analyze users' past consumption patterns and prioritize the management of inventory of frequently used ingredients. In addition, the management department can predict and manage the inventory of ingredients needed at specific times based on users' consumption behavior. Furthermore, the management department can propose an inventory management method that minimizes waste based on users' past consumption history. In this way, the management department can select the optimal inventory management method by analyzing past consumption behavior.

[0095] The management department can customize inventory management methods based on the user's current lifestyle. For example, the management department can customize inventory management methods based on the user's current lifestyle. The management department needs to clarify the specific details and methods of obtaining the user's current lifestyle. For example, this may include family structure and lifestyle patterns. The management department also needs to clarify the specific methods and criteria for customizing inventory management methods. For example, this may include inventory type, quantity, and storage method. The management department can use AI to analyze the user's current lifestyle. For example, the AI ​​can take the user's current lifestyle as input and output customized inventory management methods. For example, the management department can provide a way for users to easily check inventory status when they are busy. Furthermore, if a user is traveling, the management department can automate inventory management during their trip. In addition, if a user prioritizes health management, the management department can provide health-conscious inventory management methods. As a result, the management department can enable more appropriate inventory management by customizing inventory management methods based on the user's current lifestyle.

[0096] The management department can estimate the user's emotions and determine inventory management priorities based on those estimated emotions. For example, the management department can estimate the user's emotions and determine inventory management priorities based on those estimated emotions. This is implemented using an emotion estimation function, such as an emotion engine or generative AI. Generative AI, for example, takes the user's emotions as input and outputs the emotion estimation result. The management department needs to clearly define the specific methods and criteria for determining priorities. These include, for example, the importance of the inventory and the frequency of consumption. For example, if the user is stressed, the management department can prioritize the inventory management of important ingredients. Furthermore, if the user is relaxed, the management department can perform overall inventory management and make fine adjustments. Additionally, if the user is in a hurry, the management department can prioritize the inventory management of ingredients that are needed immediately. This allows the management department to perform more appropriate inventory management by determining inventory management priorities according to the user's emotions.

[0097] The management department can select the optimal inventory management method when managing inventory, taking into account the user's geographical location information. For example, the management department can select the optimal inventory management method when managing inventory, taking into account the user's geographical location information. The management department needs to clarify the specific methods for acquiring and using geographical location information. For example, this includes GPS data and location information services. The management department needs to clarify the specific criteria and selection methods for the optimal inventory management method. For example, this includes demand forecasting and consumption volume. The management department can use AI to analyze the user's geographical location information. For example, the AI ​​can take geographical location information as input and output the optimal inventory management method. For example, if the user is in a specific region, the management department can prioritize inventory management of local specialty products. Also, if the user is traveling, the management department can manage inventory considering ingredients available at the travel destination. Furthermore, if the user is at home, the management department can prioritize inventory management of ingredients available at nearby supermarkets. In this way, the management department can select the optimal inventory management method by taking geographical location information into account.

[0098] The management department can analyze users' social media activity and propose inventory management methods during inventory management. For example, the management department can analyze users' social media activity and propose inventory management methods during inventory management. The management department needs to clarify the specific content and analysis methods of social media activity. For example, this includes the content of posts and the number of likes. The management department needs to clarify the specific methods and criteria for proposing inventory management methods. For example, this includes the type of inventory, quantity, and storage method. The management department can use AI to analyze users' social media activity. For example, the AI ​​can take social media activity as input and output inventory management methods. For example, the management department can analyze photos of dishes shared by users on social media and manage the inventory of related ingredients. Furthermore, the management department can analyze posts from cooking accounts that users follow and manage the inventory of related ingredients. In addition, the management department can analyze posts of dishes that users "like" on social media and manage the inventory of related ingredients. As a result, the management department can propose the optimal inventory management method by analyzing social media activity.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data acquisition unit can acquire the user's dietary preferences and allergy information and filter menu information based on this information. For example, if a user is allergic to a specific ingredient, menu information containing that ingredient can be excluded. Also, if a user likes a particular ingredient, menu information containing that ingredient can be prioritized. Furthermore, the data acquisition unit can update the user's dietary preferences and allergy information in real time and acquire menu information based on the latest information. As a result, the data acquisition unit can provide menu information that takes into account the user's health and preferences.

[0101] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results, including background information and nutritional value of the dishes. If the user is in a hurry, it can provide concise analysis results, displaying only the essentials. Furthermore, if the user is excited, it can display the analysis results using visually appealing graphs and charts. This allows the analysis unit to select the optimal display method according to the user's emotions.

[0102] The suggestion unit can analyze a user's past menu selection history and suggest a special blend of rice based on this analysis. For example, it can analyze the trends in menus a user has selected in the past and suggest a special blend of rice that would suit similar menus. It can also predict the menus a user will select at a specific time of day and suggest a special blend of rice that would suit that time. Furthermore, based on the user's past selection history, it can suggest a special blend of rice that would suit the menus selected on specific days of the week. In this way, the suggestion unit can provide a special blend of rice tailored to the user's preferences.

[0103] The management department can estimate user emotions and adjust inventory management methods based on those estimates. For example, if a user is stressed, a simple inventory management method can be provided to reduce their workload. If a user is relaxed, a detailed inventory management method can be provided, allowing for fine-tuning. Furthermore, if a user is in a hurry, a method for quickly checking inventory status can be provided. In this way, the management department can achieve more appropriate inventory management by adjusting inventory management methods according to user emotions.

[0104] The data acquisition unit can prioritize the acquisition of highly relevant menu information by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the acquisition of menu information using local specialties from that region. Also, if the user is traveling, it can prioritize the acquisition of menu information featuring local cuisine from their travel destination. Furthermore, if the user is at home, it can prioritize the acquisition of menu information using ingredients available at nearby supermarkets. In this way, the data acquisition unit can provide the user with highly relevant menu information by considering their geographical location.

[0105] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions, including background information about the dishes. If the user is in a hurry, it can provide concise suggestions, showing only the essentials. Furthermore, if the user is excited, it can use visually appealing graphs and charts to present suggestions. In this way, the suggestion function can provide more appropriate suggestions by adjusting the presentation of suggestions according to the user's emotions.

[0106] The management department can analyze users' past consumption behavior to select the optimal inventory management method. For example, by analyzing users' past consumption patterns, they can prioritize the management of inventory for frequently used ingredients. They can also predict and manage the inventory of ingredients needed at specific times based on users' consumption behavior. Furthermore, they can propose inventory management methods that minimize waste based on users' past consumption history. In this way, the management department can select the optimal inventory management method by analyzing past consumption behavior.

[0107] The analysis department can adjust the level of detail in its analysis based on the importance of each dish. For example, for a main dish, a detailed analysis can be performed, providing a detailed explanation of the type of rice used and the cooking method. For a side dish, a concise analysis can be performed, providing only basic information. Furthermore, for desserts, an analysis can be performed that emphasizes the type of rice used and the balance of sweetness. In this way, the analysis department can provide more appropriate analysis results by adjusting the level of detail in its analysis based on the importance of each dish.

[0108] The acquisition unit can analyze a user's social media activity and obtain relevant menu information. For example, it can analyze photos of dishes a user has shared on social media and obtain similar menu information. It can also analyze posts from food accounts a user follows and obtain relevant menu information. Furthermore, it can analyze food posts a user has "liked" on social media and obtain relevant menu information. In this way, the acquisition unit can provide menu information relevant to the user by analyzing their social media activity.

[0109] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on that estimation. For example, if the user is in a hurry, it can provide a short, concise suggestion. If the user is relaxed, it can provide a longer suggestion with more detailed explanations. Furthermore, if the user is excited, it can provide a suggestion with visually stimulating effects. In this way, the suggestion function can provide more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The acquisition unit acquires menu information. The acquisition unit can acquire menu information by, for example, linking with existing recipe management software. The acquisition unit needs to clarify the specific content and format of the menu information. For example, this may include the dish name, ingredients, and cooking method. Step 2: The analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit. The analysis unit, for example, analyzes the recipe information for each dish in detail to identify which type of rice is best. For example, sticky rice is suitable for sushi, and firmer rice is suitable for curry. The analysis unit needs to clarify specific criteria and selection methods for the recommended type of rice for each dish. For example, these may include taste, texture, and nutritional value. Step 3: The Proposal Department proposes a special blend of rice based on the information analyzed by the Analysis Department. The Proposal Department uses a generating AI to propose a special blend of rice. The Proposal Department's generating AI proposes the optimal blend of rice in response to requests such as wanting to use a specific brand of rice or wanting to adjust the ratio of new rice to old rice. The Proposal Department needs to clarify the specific contents and creation method of the special blend of rice. This includes, for example, the types of rice to be used and the blending ratio. Step 4: The management department manages the inventory and ordering of the special blend rice proposed by the proposal department. For example, the management department monitors the restaurant's inventory status in real time and orders the optimal amount of rice at the necessary time. The management department needs to clarify the specific contents and management methods of the inventory. For example, this includes the type of inventory, quantity, and storage method. The management department needs to clarify the specific methods and criteria for ordering. For example, this includes the timing of orders, order quantity, and supplier.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires menu information in cooperation with existing recipe management software. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the recommended type of rice for each dish based on the acquired menu information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a special blend of rice using generating AI. The management unit is implemented by the control unit 46A of the smart device 14 and manages the inventory and ordering of the proposed special blend of rice. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires menu information in cooperation with existing recipe management software. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the recommended type of rice for each dish based on the acquired menu information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a special blend of rice using generating AI. The management unit is implemented by the control unit 46A of the smart glasses 214 and manages the inventory and ordering of the proposed special blend of rice. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires menu information in cooperation with existing recipe management software. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the recommended type of rice for each dish based on the acquired menu information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a special blend of rice using generating AI. The management unit is implemented by the control unit 46A of the headset terminal 314 and manages the inventory and ordering of the proposed special blend of rice. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires menu information in cooperation with existing recipe management software. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the recommended type of rice for each dish based on the acquired menu information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a special blend of rice using generating AI. The management unit is implemented by, for example, the control unit 46A of the robot 414 and manages the inventory and ordering of the proposed special blend of rice. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A unit for obtaining menu information, An analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes a special blend of rice, The system includes a management unit that manages the inventory and ordering of the special blend rice proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose a special blend of rice using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the recipe information for each dish in detail to determine which type of rice is best suited for it. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, The system monitors the restaurant's inventory in real time and orders the optimal amount of rice at the right time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Reflecting the restaurant's wishes and incorporating the rice shop's expertise, we propose a special blend of rice, adjusting the ratio of new to old rice to match specific rice varieties. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Reduce unnecessary stock and achieve efficient distribution. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of menu information acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past menu selection history and select the optimal method for retrieving it. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving menu information, filtering is performed based on the user's current ingredient inventory status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and determines the priority of menu information to retrieve based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving menu information, the system prioritizes retrieving menu information that is highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When retrieving menu information, the system analyzes the user's social media activity and retrieves relevant menu information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of each dish. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the dish. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, the priority of the analysis will be determined based on when the dishes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the dishes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the specially blended rice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, a different proposal algorithm is applied depending on the category of the special blended rice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the timing of submission of the special blend rice. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the special blend rice. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates user sentiment and adjusts inventory management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing inventory, analyze users' past purchasing behavior to select the optimal inventory management method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When managing inventory, customize the inventory management method based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, The system estimates user sentiment and prioritizes inventory management based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing inventory, the optimal inventory management method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing inventory, we analyze users' social media activity and propose inventory management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 unit for obtaining menu information, An analysis unit analyzes the recommended type of rice for each dish based on the menu information acquired by the acquisition unit, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes a special blend of rice, The system includes a management unit that manages the inventory and ordering of the special blend rice proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, We propose a special blend of rice using AI generation. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the recipe information for each dish in detail to determine which type of rice is best suited for it. The system according to feature 1.

4. The aforementioned management department, The system monitors the restaurant's inventory in real time and orders the optimal amount of rice at the right time. The system according to feature 1.

5. The aforementioned proposal section is, Reflecting the restaurant's wishes and incorporating the rice shop's expertise, we propose a special blend of rice, adjusting the ratio of new to old rice to match specific rice varieties. The system according to feature 1.

6. The aforementioned management department, Reduce unnecessary stock and achieve efficient distribution. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of menu information acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the user's past menu selection history and select the optimal method for retrieving it. The system according to feature 1.

9. The acquisition unit is, When retrieving menu information, filtering is performed based on the user's current ingredient inventory status. The system according to feature 1.

10. The acquisition unit is, The system estimates the user's emotions and determines the priority of menu information to retrieve based on those estimated emotions. The system according to feature 1.

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