system

Abstract

The system according to this embodiment aims to suggest an appropriate recipe based on the user's requests and to order the necessary ingredients in the correct quantities. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a decision unit, and an order unit. The reception unit receives requests from users. The proposal unit proposes a recipe based on the requests received by the reception unit. The decision unit determines the amount of ingredients needed based on the recipe proposed by the proposal unit. The order unit orders any missing ingredients based on the amount of ingredients needed determined by the decision 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 a chatbot 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 is a problem that it is difficult to propose an appropriate recipe based on the user's desires and order the necessary ingredients without excess or deficiency.

[0005] The system according to the embodiment aims to propose an appropriate recipe based on the user's desires and order the necessary ingredients without excess or deficiency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a decision unit, and an order unit. The reception unit receives requests from users. The proposal unit proposes recipes based on the requests received by the reception unit. The decision unit determines the amount of ingredients needed based on the recipe proposed by the proposal unit. The order unit orders any missing ingredients based on the amount of ingredients needed determined by the decision unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an appropriate recipe based on the user's requests and order the necessary ingredients in the correct quantities. [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 a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

[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 recipe suggestion agent according to an embodiment of the present invention is a system that suggests an appropriate recipe based on the user's requests, determines the amount of ingredients needed, and even orders any missing ingredients on behalf of the user. This recipe suggestion agent takes user requests such as what they want to eat, the time required, ingredients, and cooking utensils, and the AI ​​uses Kurashiru's recipe database as a RAG (Retrieval-Augmented Generation) to suggest a recipe that matches the user's requests. Furthermore, based on the suggested recipe, it determines the amount of ingredients needed, considers the user's inventory information and expiration dates, and orders any missing ingredients on behalf of the user. This mechanism allows users to use all their ingredients without excess or shortage. For example, the user inputs requests such as what they want to eat, the time required, ingredients, and cooking utensils. At this time, the user can input specific dish names, types of ingredients, cooking times, etc. For example, they might input requests such as "I want to make curry," "a dish that can be made in under 30 minutes," or "I want to use chicken." Next, the AI ​​uses Kurashiru's recipe database as a RAG to suggest a recipe that matches the user's requests. The AI ​​analyzes the user's requests, searches for an appropriate recipe from Kurashiru's recipe database, and suggests it. For example, if a user enters "I want to make curry," the system searches for curry recipes in Kurashiru's recipe database and suggests recipes. Furthermore, it determines the amount of ingredients needed based on the suggested recipe. The AI ​​calculates the amount of ingredients needed, taking into account the user's inventory information and expiration dates. For example, if a user enters "I want to use chicken," the system checks the user's inventory information and calculates the amount of chicken needed. Finally, if any ingredients are missing, the system places an order on their behalf. The AI ​​checks the user's inventory information, and if any ingredients are missing, it places an order using an online shopping site. For example, if a user enters "I don't have enough chicken," the AI ​​will order chicken using an online shopping site. This system ensures that users can use all their ingredients without any surplus or shortage. By informing users of their ingredient inventory and expiration dates, this information is reflected in subsequent suggestions, ensuring that ingredients are used without waste. For example, if a user enters "The chicken is nearing its expiration date," the AI ​​takes this information into consideration and prioritizes suggesting recipes that use chicken.This allows the recipe suggestion agent to propose appropriate recipes based on the user's requests, determine the necessary quantities of ingredients, and even order any missing ingredients on their behalf.

[0029] The recipe suggestion agent according to this embodiment comprises a reception unit, a suggestion unit, a decision unit, and an order unit. The reception unit receives user requests. User requests include, but are not limited to, what the user wants to eat, the time required, ingredients, and cooking utensils. For example, the user can input requests such as "I want to make curry," "a dish that can be made in 30 minutes or less," and "I want to use chicken" into the reception unit. The suggestion unit proposes a recipe based on the requests received by the reception unit. For example, the suggestion unit uses AI to utilize Kurashiru's recipe database as a RAG and proposes a recipe that matches the user's requests. For example, if the user inputs "I want to make curry," the suggestion unit searches for a curry recipe from Kurashiru's recipe database and proposes it. The decision unit determines the amount of ingredients needed based on the recipe proposed by the suggestion unit. For example, the decision unit calculates the amount of ingredients needed by considering the user's inventory information and expiration dates. For example, if the user inputs "I want to use chicken," the decision unit checks the user's inventory information and calculates the amount of chicken needed. The ordering unit orders any missing ingredients based on the quantities of ingredients determined by the decision unit. For example, the ordering unit orders the missing ingredients using an online shopping site. For example, if the user enters "We don't have enough chicken," the ordering unit orders chicken using an online shopping site. As a result, the recipe suggestion agent according to the embodiment can suggest an appropriate recipe based on the user's request, determine the quantities of ingredients needed, and even place orders for any missing ingredients.

[0030] The reception desk receives user requests. User requests include, but are not limited to, what they want to eat, the time required, ingredients, and cooking utensils. For example, users can input requests such as "I want to make curry," "A dish that can be made in under 30 minutes," or "I want to use chicken." Specifically, the reception desk provides forms and options through the user interface, allowing users to freely input their requests. Users can communicate their requests using text input or voice input, and in the case of voice input, speech recognition technology is used to convert it into text. Furthermore, the reception desk can record the user's past requests and history, and learn the user's preferences and tendencies to provide more personalized suggestions. For example, if a user has previously preferred "healthy dishes," the reception desk will take that information into consideration and prioritize suggesting healthy recipes. The reception desk also has a function to register the user's allergy information and dietary restrictions (e.g., vegetarian, gluten-free, etc.), and can suggest appropriate recipes based on this information. In this way, the reception desk can flexibly respond to the diverse requests of users and suggest recipes that meet individual needs.

[0031] The Proposal Department proposes recipes based on requests received by the Reception Department. For example, the Proposal Department uses AI to utilize Kurashiru's recipe database as a RAG (Recipe Aggregation) to suggest recipes that match the user's requests. Specifically, the Proposal Department uses natural language processing technology to analyze the user's requests and extract keywords and conditions included in the requests. For example, if a user enters "I want to make curry," the Proposal Department extracts the keyword "curry" and searches for curry recipes in Kurashiru's recipe database. Furthermore, the Proposal Department narrows down the recipes by considering conditions such as the time required, ingredients used, and cooking utensils, according to the user's requests. For example, if there is a request for "a dish that can be made in 30 minutes or less," the Proposal Department prioritizes searching for recipes that take 30 minutes or less. Also, if there is a request for "I want to use chicken," it searches for recipes that use chicken. The Proposal Department uses AI to analyze recipe ratings and reviews and proposes the most suitable recipe for the user. For example, it prioritizes suggesting recipes that have received high ratings from other users or recipes that have been rated as "easy to make" and "delicious" in reviews. This allows the proposal department to quickly and accurately suggest recipes that meet the user's needs.

[0032] The decision unit determines the amount of ingredients needed based on the recipe proposed by the suggestion unit. The decision unit calculates the amount of ingredients needed, for example, by considering the user's inventory information and expiration dates. Specifically, the decision unit refers to the inventory information registered by the user and checks whether the ingredients needed for the proposed recipe are in stock. For example, if the user enters "I want to use chicken," the decision unit checks the user's inventory information and the amount of chicken in stock. If the stock is insufficient, it calculates the additional amount needed. The decision unit also considers the expiration dates of the ingredients and calculates to use older ingredients first. For example, if the expiration date of the chicken in stock is approaching, it will suggest using that chicken first. Furthermore, the decision unit adjusts the amount of ingredients needed according to the amount of food and the number of people in the user's meal. For example, if the user enters "I want to make curry for 4 people," the decision unit calculates and suggests the amount of ingredients needed for 4 people. In this way, the decision unit can accurately determine the amount of ingredients needed according to the user's inventory information, expiration dates, and meal size.

[0033] The ordering department orders any missing ingredients based on the quantities determined by the decision department. For example, the ordering department might use an online shopping site to order the missing ingredients. Specifically, based on the list of necessary ingredients calculated by the decision department, the ordering department identifies any ingredients the user is lacking in their inventory and places an order through an online shopping site. For example, if the user enters "We're short on chicken," the ordering department will order chicken through an online shopping site. The ordering department selects the most suitable product considering the user's preferences and past purchase history. For example, if the user has previously purchased a specific brand of chicken, the ordering department will prioritize ordering that brand of chicken. The ordering department also suggests the best purchase options considering the user's budget and delivery time. For example, if the user enters "I want to buy within my budget," the ordering department will search for and suggest products that can be purchased within that budget. Furthermore, the ordering department can compare multiple online shopping sites and select the cheapest and fastest delivery option. This allows the ordering department to quickly and efficiently order any missing ingredients according to the user's needs.

[0034] The suggestion unit can propose recipes using a specific recipe database as a RAG (Recipe Aggregation). For example, the suggestion unit can use the Kurashiru recipe database as a RAG to propose recipes that meet the user's needs. For example, if the user inputs "I want to make curry," the suggestion unit will search the Kurashiru recipe database for curry recipes and propose them. For example, if the user inputs "a dish that can be made in under 30 minutes," the suggestion unit will search the Kurashiru recipe database for recipes that can be made in under 30 minutes and propose them. For example, if the user inputs "I want to use chicken," the suggestion unit will search the Kurashiru recipe database for recipes using chicken and propose them. In this way, by using Kurashiru recipes as a RAG, it is possible to propose recipes that meet the user's needs. Specific recipe databases include, but are not limited to, Kurashiru and Cookpad. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion section can propose recipes using a generative AI model that takes user requests as input and outputs recipes.

[0035] The decision unit can determine the amount of ingredients needed based on the user's inventory information and expiration dates. For example, the decision unit checks the user's inventory information and calculates the amount of ingredients needed. For example, if the user inputs "I want to use chicken," the decision unit checks the user's inventory information and calculates the amount of chicken needed. The decision unit can also calculate the amount of ingredients needed by considering the user's expiration dates. For example, if the user inputs "The chicken is nearing its expiration date," the decision unit considers this information and calculates the amount of chicken needed. In this way, by considering the user's inventory information and expiration dates, the amount of ingredients needed can be appropriately determined. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can determine the amount of ingredients using an AI model that takes the user's inventory information and expiration dates as input and outputs the amount of ingredients needed.

[0036] The ordering unit can order missing ingredients using an online shopping site. For example, the ordering unit checks the user's inventory information and orders the missing ingredients from an online shopping site. For example, if the user enters "We're short on chicken," the ordering unit will order chicken using an online shopping site. For example, if the user enters "We're short on vegetables," the ordering unit will order vegetables using an online shopping site. This allows for quick ordering of missing ingredients by using an online shopping site. Some or all of the above processing in the ordering unit may be performed using AI, for example, or without AI. For example, the ordering unit can order ingredients using an AI model that takes the user's inventory information as input and outputs the missing ingredients.

[0037] The suggestion unit can search for and suggest recipes based on user requests. For example, the suggestion unit can analyze user requests, search for appropriate recipes from the recipe database, and suggest them. For example, if a user inputs "I want to make curry," the suggestion unit will search for curry recipes from the recipe database and suggest them. For example, if a user inputs "a dish that can be made in under 30 minutes," the suggestion unit will search for recipes that can be made in under 30 minutes from the recipe database and suggest them. For example, if a user inputs "I want to use chicken," the suggestion unit will search for recipes using chicken from the recipe database and suggest them. In this way, by searching for and suggesting recipes based on user requests, it is possible to provide recipes that meet the user's needs. Specific methods for searching for recipes include, but are not limited to, keyword searches and filtering conditions. Some or all of the above-described processes in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can search for and suggest recipes using an AI model that takes user requests as input and outputs recipes.

[0038] The decision unit can calculate the amount of ingredients needed based on the proposed recipe. For example, the decision unit analyzes the contents of the proposed recipe and calculates the amount of ingredients needed. For example, if the user inputs "I want to make curry," the decision unit calculates the amount of ingredients needed based on the proposed curry recipe. For example, if the user inputs "a dish that can be made in under 30 minutes," the decision unit calculates the amount of ingredients needed based on the proposed recipe. For example, if the user inputs "I want to use chicken," the decision unit calculates the amount of chicken needed based on the proposed recipe. In this way, by calculating the amount of ingredients needed based on the proposed recipe, the appropriate amount of ingredients can be prepared. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can calculate the amount of ingredients using an AI model that takes a proposed recipe as input and outputs the amount of ingredients needed.

[0039] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history. Past request history includes, but is not limited to, past order history and survey results. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can select a reception method using an AI model that takes the user's past request history as input and outputs the optimal reception method.

[0040] The reception desk can filter requests based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the reception desk will prioritize suggesting health-conscious recipes. If the user is busy, the reception desk will prioritize suggesting recipes that can be prepared in a short time. If the user is interested in a particular ingredient, the reception desk will prioritize suggesting recipes that use that ingredient. By filtering based on the user's current lifestyle and areas of interest, the reception desk can receive more appropriate requests. Current lifestyle includes, but is not limited to, family structure and frequency of meals. Areas of interest include, but is not limited to, hobbies and preferred cooking genres. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can perform filtering using an AI model that takes the user's current lifestyle and areas of interest as input and outputs filtering results.

[0041] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize suggesting recipes using ingredients available in that region. If the user is traveling, the reception desk will prioritize suggesting recipes using ingredients available at their travel destination. If the user is at home, the reception desk will prioritize suggesting recipes that can be easily prepared at home. This allows the reception desk to prioritize requests based on their relevance by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can prioritize requests using an AI model that takes the user's geographical location as input and outputs requests based on their relevance.

[0042] The reception desk can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can suggest relevant recipes based on recipes the user has shared on social media. For example, the reception desk can suggest recipes using ingredients the user has shown interest in on social media. For example, the reception desk can suggest recipes from cooking experts the user follows on social media. In this way, relevant requests can be accepted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can accept requests using an AI model that takes the user's social media activity as input and outputs relevant requests.

[0043] The suggestion function can adjust the level of detail in its suggestions based on the importance of the recipe. For example, for important recipes, the suggestion function will provide suggestions that include detailed steps and precautions. For simple recipes, the suggestion function will provide suggestions that include concise steps. For recipes for special events, the suggestion function will also include suggestions for decoration and presentation. By adjusting the level of detail in suggestions based on the importance of the recipe, it becomes possible to provide the most suitable suggestions for the user. Recipe importance includes, but is not limited to, nutritional value and popularity. Some or all of the above processing in the suggestion function may be performed using, for example, AI, or not. For example, the suggestion function can adjust the level of detail using an AI model that takes the importance of the recipe as input and outputs the level of detail of the suggestion.

[0044] The suggestion unit can apply different suggestion algorithms depending on the recipe category when making suggestions. For example, in the case of a dessert recipe, the suggestion unit will make suggestions that include how to adjust the sweetness. For example, in the case of a main dish recipe, the suggestion unit will make suggestions that take into account volume and nutritional balance. For example, in the case of a snack recipe, the suggestion unit will suggest an easy way to make it. By applying different suggestion algorithms depending on the recipe category, more appropriate suggestions can be made. Recipe categories include, but are not limited to, Japanese food, Western food, and desserts. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can apply an algorithm using an AI model that takes the recipe category as input and outputs a suggestion algorithm.

[0045] The suggestion department can prioritize suggestions based on the timing of recipe submission. For example, if the user is in a hurry, the suggestion department will prioritize suggesting recipes that can be cooked quickly. If the user has ample time, the suggestion department will prioritize suggesting more elaborate recipes. If the user is preparing for a specific event, the suggestion department will prioritize suggesting recipes suitable for that event. This allows for optimal suggestions for the user by prioritizing suggestions based on the timing of recipe submission. The timing of recipe submission includes, but is not limited to, the submission date and season. Some or all of the above processing in the suggestion department may be performed using, for example, AI, or not. For example, the suggestion department can determine priorities using an AI model that takes the timing of recipe submission as input and outputs the priority of suggestions.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the recipes. For example, the suggestion unit might suggest the recipe most relevant to the user's needs first. For example, the suggestion unit might prioritize suggesting recipes that are highly relevant based on the user's past preferences. For example, the suggestion unit might suggest recipes that are highly relevant based on the user's current living situation. By adjusting the order of suggestions based on the relevance of the recipes, it becomes possible to provide the most suitable suggestions for the user. Recipe relevance includes, but is not limited to, commonality of ingredients or similarity of cooking methods. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can adjust the order using an AI model that takes recipe relevance as input and outputs the order of suggestions.

[0047] The decision unit can analyze the user's past consumption behavior to select an appropriate amount of food at the time of decision. For example, the decision unit can suggest the optimal amount based on the amount the user has consumed in the past. For example, the decision unit can analyze the user's past consumption patterns to suggest an amount that minimizes waste. For example, the decision unit can predict and suggest the amount of a specific food to be consumed based on the user's past consumption history. In this way, by analyzing the user's past consumption behavior, it is possible to select an optimal amount of food that minimizes waste. Past consumption behavior includes, but is not limited to, purchase history and consumption patterns. Some or all of the above processing in the decision unit may be performed using, for example, AI, or not using AI. For example, the decision unit can select an amount using an AI model that takes the user's past consumption behavior as input and outputs an appropriate amount of food.

[0048] The decision-making unit can customize the amount of ingredients based on the user's current living situation when making a decision. For example, if the user lives alone, the decision-making unit will suggest a small amount of ingredients. If the user lives with family, the decision-making unit will suggest an amount that will satisfy everyone in the family. If the user has a specific event planned, the decision-making unit will suggest an amount appropriate for that event. In this way, by customizing the amount of ingredients based on the user's current living situation, it is possible to suggest a more appropriate amount of ingredients. Current living situation includes, but is not limited to, family structure and frequency of meals. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can customize the amount using an AI model that takes the user's current living situation as input and outputs the amount of ingredients.

[0049] The decision-making unit can select an appropriate amount of ingredients by considering the user's geographical location information during the decision-making process. For example, if the user is in a specific region, the decision-making unit will prioritize suggesting ingredients available in that region. For example, if the user is traveling, the decision-making unit will suggest ingredients that can be procured at the travel destination. For example, if the user is at home, the decision-making unit will suggest ingredients that can be easily cooked at home. In this way, the optimal amount of ingredients can be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can select the amount using an AI model that takes the user's geographical location information as input and outputs an appropriate amount of ingredients.

[0050] The decision-making unit can analyze the user's social media activity and adjust the amount of ingredients at the time of decision-making. For example, the decision-making unit may suggest the amount of relevant ingredients based on recipes shared by the user on social media. For example, the decision-making unit may suggest recipes using ingredients that the user has shown interest in on social media. For example, the decision-making unit may suggest recipes from cooking experts that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to suggest a more appropriate amount of ingredients. Social media activity includes, but is not limited to, posts and the number of followers. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can adjust the amount using an AI model that takes the user's social media activity as input and outputs the amount of ingredients.

[0051] The ordering system can select an appropriate ordering method by referring to the user's past order history when an order is placed. For example, the ordering system can automatically display ingredients that the user has frequently ordered in the past as suggestions. For example, the ordering system can prioritize suggesting ordering methods that the user has used in the past (online, telephone, etc.). For example, the ordering system can predict and suggest ordering methods to be used at a specific time of day based on the user's past order history. This allows the system to select the optimal ordering method by referring to the user's past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select a method using an AI model that takes the user's past order history as input and outputs an appropriate ordering method.

[0052] The ordering system can customize the ordering process based on the user's current lifestyle. For example, if the user is busy, the ordering system can provide a way to complete the order quickly. If the user is relaxed, the ordering system can provide detailed ordering options. If the user has a specific event planned, the ordering system can suggest an ordering method suitable for that event. This allows for more appropriate ordering by customizing the ordering process based on the user's current lifestyle. Current lifestyle includes, but is not limited to, family structure and meal frequency. Some or all of the above processing in the ordering system may be performed using AI, for example, or not. For example, the ordering system can customize the ordering process using an AI model that takes the user's current lifestyle as input and outputs ordering methods.

[0053] The ordering system can select an appropriate ordering method when an order is placed, taking into account the user's geographical location. For example, if the user is in a specific region, the ordering system will prioritize ordering ingredients available in that region. If the user is traveling, the ordering system will order ingredients available at the travel destination. If the user is at home, the ordering system will suggest an ordering method that can be easily received at home. This allows the system to select the optimal ordering method by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select an ordering method using an AI model that takes the user's geographical location as input and outputs an appropriate ordering method.

[0054] The ordering system can analyze a user's social media activity and suggest ordering methods when an order is placed. For example, the ordering system can order relevant ingredients based on ingredients the user has shared on social media. For example, the ordering system can suggest recipes using ingredients the user has shown interest in on social media. For example, the ordering system can suggest recipes from cooking experts the user follows on social media. This allows for more appropriate orders by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the above processing in the ordering system may be performed using AI, for example, or without AI. For example, the ordering system can suggest ordering methods using an AI model that takes the user's social media activity as input and outputs ordering methods.

[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 suggestion unit can analyze the user's past request history and propose the most suitable recipe. For example, the suggestion unit may prioritize suggesting recipes that the user has frequently selected in the past. For example, the suggestion unit may suggest recipes that use ingredients that the user has liked in the past. For example, the suggestion unit may suggest recipes suitable for a specific time of day based on the user's past request history. In this way, the optimal recipe can be suggested by analyzing the user's past request history. Past request history includes, but is not limited to, past order history and survey results. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can suggest recipes using an AI model that takes the user's past request history as input and outputs the optimal recipe.

[0057] The decision unit can analyze the user's past consumption behavior to select an appropriate amount of food at the time of decision. For example, the decision unit can suggest the optimal amount based on the amount the user has consumed in the past. For example, the decision unit can analyze the user's past consumption patterns to suggest an amount that minimizes waste. For example, the decision unit can predict and suggest the amount of a specific food to be consumed based on the user's past consumption history. In this way, by analyzing the user's past consumption behavior, it is possible to select an optimal amount of food that minimizes waste. Past consumption behavior includes, but is not limited to, purchase history and consumption patterns. Some or all of the above processing in the decision unit may be performed using, for example, AI, or not using AI. For example, the decision unit can select an amount using an AI model that takes the user's past consumption behavior as input and outputs an appropriate amount of food.

[0058] The ordering system can select an appropriate ordering method by referring to the user's past order history when an order is placed. For example, the ordering system can automatically display ingredients that the user has frequently ordered in the past as suggestions. For example, the ordering system can prioritize suggesting ordering methods that the user has used in the past (online, telephone, etc.). For example, the ordering system can predict and suggest ordering methods to be used at a specific time of day based on the user's past order history. This allows the system to select the optimal ordering method by referring to the user's past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select a method using an AI model that takes the user's past order history as input and outputs an appropriate ordering method.

[0059] The suggestion unit can apply different suggestion algorithms depending on the recipe category when making suggestions. For example, in the case of a dessert recipe, the suggestion unit will make suggestions that include how to adjust the sweetness. For example, in the case of a main dish recipe, the suggestion unit will make suggestions that take into account volume and nutritional balance. For example, in the case of a snack recipe, the suggestion unit will suggest an easy way to make it. By applying different suggestion algorithms depending on the recipe category, more appropriate suggestions can be made. Recipe categories include, but are not limited to, Japanese food, Western food, and desserts. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can apply an algorithm using an AI model that takes the recipe category as input and outputs a suggestion algorithm.

[0060] The ordering system can select an appropriate ordering method when an order is placed, taking into account the user's geographical location. For example, if the user is in a specific region, the ordering system will prioritize ordering ingredients available in that region. If the user is traveling, the ordering system will order ingredients available at the travel destination. If the user is at home, the ordering system will suggest an ordering method that can be easily received at home. This allows the system to select the optimal ordering method by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select an ordering method using an AI model that takes the user's geographical location as input and outputs an appropriate ordering method.

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

[0062] Step 1: The reception desk receives user requests. User requests include what they want to eat, the time required, ingredients, cooking equipment, etc. For example, a user can enter requests such as "I want to make curry," "a dish that can be made in 30 minutes or less," or "I want to use chicken." Step 2: The Proposal Department proposes recipes based on the requests received by the Reception Department. The Proposal Department uses AI to utilize Kurashiru's recipe database as a RAG (Recipe Aggregation) and proposes recipes that match the user's requests. For example, if the user enters "I want to make curry," the Proposal Department searches for curry recipes in Kurashiru's recipe database and proposes them. Step 3: The decision unit determines the amount of ingredients needed based on the recipe proposed by the suggestion unit. The decision unit calculates the amount of ingredients needed, taking into account the user's inventory information and expiration dates. For example, if the user enters "I want to use chicken," the decision unit checks the user's inventory information and calculates the amount of chicken needed. Step 4: The ordering department orders any missing ingredients based on the quantities determined by the decision-making department. The ordering department uses an online shopping site to order the missing ingredients. For example, if the user enters "We are short on chicken," the ordering department will use an online shopping site to order chicken.

[0063] (Example of form 2) The recipe suggestion agent according to an embodiment of the present invention is a system that suggests an appropriate recipe based on the user's requests, determines the amount of ingredients needed, and even orders any missing ingredients on behalf of the user. This recipe suggestion agent takes user requests such as what they want to eat, the time required, ingredients, and cooking utensils, and the AI ​​uses Kurashiru's recipe database as a RAG (Retrieval-Augmented Generation) to suggest a recipe that matches the user's requests. Furthermore, based on the suggested recipe, it determines the amount of ingredients needed, considers the user's inventory information and expiration dates, and orders any missing ingredients on behalf of the user. This mechanism allows users to use all their ingredients without excess or shortage. For example, the user inputs requests such as what they want to eat, the time required, ingredients, and cooking utensils. At this time, the user can input specific dish names, types of ingredients, cooking times, etc. For example, they might input requests such as "I want to make curry," "a dish that can be made in under 30 minutes," or "I want to use chicken." Next, the AI ​​uses Kurashiru's recipe database as a RAG to suggest a recipe that matches the user's requests. The AI ​​analyzes the user's requests, searches for an appropriate recipe from Kurashiru's recipe database, and suggests it. For example, if a user enters "I want to make curry," the system searches for curry recipes in Kurashiru's recipe database and suggests recipes. Furthermore, it determines the amount of ingredients needed based on the suggested recipe. The AI ​​calculates the amount of ingredients needed, taking into account the user's inventory information and expiration dates. For example, if a user enters "I want to use chicken," the system checks the user's inventory information and calculates the amount of chicken needed. Finally, if any ingredients are missing, the system places an order on their behalf. The AI ​​checks the user's inventory information, and if any ingredients are missing, it places an order using an online shopping site. For example, if a user enters "I don't have enough chicken," the AI ​​will order chicken using an online shopping site. This system ensures that users can use all their ingredients without any surplus or shortage. By informing users of their ingredient inventory and expiration dates, this information is reflected in subsequent suggestions, ensuring that ingredients are used without waste. For example, if a user enters "The chicken is nearing its expiration date," the AI ​​takes this information into consideration and prioritizes suggesting recipes that use chicken.This allows the recipe suggestion agent to propose appropriate recipes based on the user's requests, determine the necessary quantities of ingredients, and even order any missing ingredients on their behalf.

[0064] The recipe suggestion agent according to this embodiment comprises a reception unit, a suggestion unit, a decision unit, and an order unit. The reception unit receives user requests. User requests include, but are not limited to, what the user wants to eat, the time required, ingredients, and cooking utensils. For example, the user can input requests such as "I want to make curry," "a dish that can be made in 30 minutes or less," and "I want to use chicken" into the reception unit. The suggestion unit proposes a recipe based on the requests received by the reception unit. For example, the suggestion unit uses AI to utilize Kurashiru's recipe database as a RAG and proposes a recipe that matches the user's requests. For example, if the user inputs "I want to make curry," the suggestion unit searches for a curry recipe from Kurashiru's recipe database and proposes it. The decision unit determines the amount of ingredients needed based on the recipe proposed by the suggestion unit. For example, the decision unit calculates the amount of ingredients needed by considering the user's inventory information and expiration dates. For example, if the user inputs "I want to use chicken," the decision unit checks the user's inventory information and calculates the amount of chicken needed. The ordering unit orders any missing ingredients based on the quantities of ingredients determined by the decision unit. For example, the ordering unit orders the missing ingredients using an online shopping site. For example, if the user enters "We don't have enough chicken," the ordering unit orders chicken using an online shopping site. As a result, the recipe suggestion agent according to the embodiment can suggest an appropriate recipe based on the user's request, determine the quantities of ingredients needed, and even place orders for any missing ingredients.

[0065] The reception desk receives user requests. User requests include, but are not limited to, what they want to eat, the time required, ingredients, and cooking utensils. For example, users can input requests such as "I want to make curry," "A dish that can be made in under 30 minutes," or "I want to use chicken." Specifically, the reception desk provides forms and options through the user interface, allowing users to freely input their requests. Users can communicate their requests using text input or voice input, and in the case of voice input, speech recognition technology is used to convert it into text. Furthermore, the reception desk can record the user's past requests and history, and learn the user's preferences and tendencies to provide more personalized suggestions. For example, if a user has previously preferred "healthy dishes," the reception desk will take that information into consideration and prioritize suggesting healthy recipes. The reception desk also has a function to register the user's allergy information and dietary restrictions (e.g., vegetarian, gluten-free, etc.), and can suggest appropriate recipes based on this information. In this way, the reception desk can flexibly respond to the diverse requests of users and suggest recipes that meet individual needs.

[0066] The Proposal Department proposes recipes based on requests received by the Reception Department. For example, the Proposal Department uses AI to utilize Kurashiru's recipe database as a RAG (Recipe Aggregation) to suggest recipes that match the user's requests. Specifically, the Proposal Department uses natural language processing technology to analyze the user's requests and extract keywords and conditions included in the requests. For example, if a user enters "I want to make curry," the Proposal Department extracts the keyword "curry" and searches for curry recipes in Kurashiru's recipe database. Furthermore, the Proposal Department narrows down the recipes by considering conditions such as the time required, ingredients used, and cooking utensils, according to the user's requests. For example, if there is a request for "a dish that can be made in 30 minutes or less," the Proposal Department prioritizes searching for recipes that take 30 minutes or less. Also, if there is a request for "I want to use chicken," it searches for recipes that use chicken. The Proposal Department uses AI to analyze recipe ratings and reviews and proposes the most suitable recipe for the user. For example, it prioritizes suggesting recipes that have received high ratings from other users or recipes that have been rated as "easy to make" and "delicious" in reviews. This allows the proposal department to quickly and accurately suggest recipes that meet the user's needs.

[0067] The decision unit determines the amount of ingredients needed based on the recipe proposed by the suggestion unit. The decision unit calculates the amount of ingredients needed, for example, by considering the user's inventory information and expiration dates. Specifically, the decision unit refers to the inventory information registered by the user and checks whether the ingredients needed for the proposed recipe are in stock. For example, if the user enters "I want to use chicken," the decision unit checks the user's inventory information and the amount of chicken in stock. If the stock is insufficient, it calculates the additional amount needed. The decision unit also considers the expiration dates of the ingredients and calculates to use older ingredients first. For example, if the expiration date of the chicken in stock is approaching, it will suggest using that chicken first. Furthermore, the decision unit adjusts the amount of ingredients needed according to the amount of food and the number of people in the user's meal. For example, if the user enters "I want to make curry for 4 people," the decision unit calculates and suggests the amount of ingredients needed for 4 people. In this way, the decision unit can accurately determine the amount of ingredients needed according to the user's inventory information, expiration dates, and meal size.

[0068] The ordering department orders any missing ingredients based on the quantities determined by the decision department. For example, the ordering department might use an online shopping site to order the missing ingredients. Specifically, based on the list of necessary ingredients calculated by the decision department, the ordering department identifies any ingredients the user is lacking in their inventory and places an order through an online shopping site. For example, if the user enters "We're short on chicken," the ordering department will order chicken through an online shopping site. The ordering department selects the most suitable product considering the user's preferences and past purchase history. For example, if the user has previously purchased a specific brand of chicken, the ordering department will prioritize ordering that brand of chicken. The ordering department also suggests the best purchase options considering the user's budget and delivery time. For example, if the user enters "I want to buy within my budget," the ordering department will search for and suggest products that can be purchased within that budget. Furthermore, the ordering department can compare multiple online shopping sites and select the cheapest and fastest delivery option. This allows the ordering department to quickly and efficiently order any missing ingredients according to the user's needs.

[0069] The suggestion unit can propose recipes using a specific recipe database as a RAG (Recipe Aggregation). For example, the suggestion unit can use the Kurashiru recipe database as a RAG to propose recipes that meet the user's needs. For example, if the user inputs "I want to make curry," the suggestion unit will search the Kurashiru recipe database for curry recipes and propose them. For example, if the user inputs "a dish that can be made in under 30 minutes," the suggestion unit will search the Kurashiru recipe database for recipes that can be made in under 30 minutes and propose them. For example, if the user inputs "I want to use chicken," the suggestion unit will search the Kurashiru recipe database for recipes using chicken and propose them. In this way, by using Kurashiru recipes as a RAG, it is possible to propose recipes that meet the user's needs. Specific recipe databases include, but are not limited to, Kurashiru and Cookpad. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion section can propose recipes using a generative AI model that takes user requests as input and outputs recipes.

[0070] The decision unit can determine the amount of ingredients needed based on the user's inventory information and expiration dates. For example, the decision unit checks the user's inventory information and calculates the amount of ingredients needed. For example, if the user inputs "I want to use chicken," the decision unit checks the user's inventory information and calculates the amount of chicken needed. The decision unit can also calculate the amount of ingredients needed by considering the user's expiration dates. For example, if the user inputs "The chicken is nearing its expiration date," the decision unit considers this information and calculates the amount of chicken needed. In this way, by considering the user's inventory information and expiration dates, the amount of ingredients needed can be appropriately determined. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can determine the amount of ingredients using an AI model that takes the user's inventory information and expiration dates as input and outputs the amount of ingredients needed.

[0071] The ordering unit can order missing ingredients using an online shopping site. For example, the ordering unit checks the user's inventory information and orders the missing ingredients from an online shopping site. For example, if the user enters "We're short on chicken," the ordering unit will order chicken using an online shopping site. For example, if the user enters "We're short on vegetables," the ordering unit will order vegetables using an online shopping site. This allows for quick ordering of missing ingredients by using an online shopping site. Some or all of the above processing in the ordering unit may be performed using AI, for example, or without AI. For example, the ordering unit can order ingredients using an AI model that takes the user's inventory information as input and outputs the missing ingredients.

[0072] The suggestion unit can search for and suggest recipes based on user requests. For example, the suggestion unit can analyze user requests, search for appropriate recipes from the recipe database, and suggest them. For example, if a user inputs "I want to make curry," the suggestion unit will search for curry recipes from the recipe database and suggest them. For example, if a user inputs "a dish that can be made in under 30 minutes," the suggestion unit will search for recipes that can be made in under 30 minutes from the recipe database and suggest them. For example, if a user inputs "I want to use chicken," the suggestion unit will search for recipes using chicken from the recipe database and suggest them. In this way, by searching for and suggesting recipes based on user requests, it is possible to provide recipes that meet the user's needs. Specific methods for searching for recipes include, but are not limited to, keyword searches and filtering conditions. Some or all of the above-described processes in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can search for and suggest recipes using an AI model that takes user requests as input and outputs recipes.

[0073] The decision unit can calculate the amount of ingredients needed based on the proposed recipe. For example, the decision unit analyzes the contents of the proposed recipe and calculates the amount of ingredients needed. For example, if the user inputs "I want to make curry," the decision unit calculates the amount of ingredients needed based on the proposed curry recipe. For example, if the user inputs "a dish that can be made in under 30 minutes," the decision unit calculates the amount of ingredients needed based on the proposed recipe. For example, if the user inputs "I want to use chicken," the decision unit calculates the amount of chicken needed based on the proposed recipe. In this way, by calculating the amount of ingredients needed based on the proposed recipe, the appropriate amount of ingredients can be prepared. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can calculate the amount of ingredients using an AI model that takes a proposed recipe as input and outputs the amount of ingredients needed.

[0074] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is stressed, the reception desk may provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk may provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk may prioritize voice input to allow for quick request entry. This allows for a response tailored to the user's needs by adjusting the request processing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using AI or not. For example, the reception desk may adjust the processing method using an AI model that takes user emotion data as input and outputs a request processing method.

[0075] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history. Past request history includes, but is not limited to, past order history and survey results. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can select a reception method using an AI model that takes the user's past request history as input and outputs the optimal reception method.

[0076] The reception desk can filter requests based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the reception desk will prioritize suggesting health-conscious recipes. If the user is busy, the reception desk will prioritize suggesting recipes that can be prepared in a short time. If the user is interested in a particular ingredient, the reception desk will prioritize suggesting recipes that use that ingredient. By filtering based on the user's current lifestyle and areas of interest, the reception desk can receive more appropriate requests. Current lifestyle includes, but is not limited to, family structure and frequency of meals. Areas of interest include, but is not limited to, hobbies and preferred cooking genres. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can perform filtering using an AI model that takes the user's current lifestyle and areas of interest as input and outputs filtering results.

[0077] The reception desk can estimate the user's emotions and determine the priority of requests based on the estimated emotions. For example, if the user is in a hurry, the reception desk will prioritize requests that require a quick response. For example, if the user is relaxed, the reception desk will prioritize detailed requests. For example, if the user is stressed, the reception desk will prioritize simple requests. This allows for more appropriate responses by prioritizing requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can determine priorities using an AI model that takes user emotion data as input and outputs a priority order for requests.

[0078] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize suggesting recipes using ingredients available in that region. If the user is traveling, the reception desk will prioritize suggesting recipes using ingredients available at their travel destination. If the user is at home, the reception desk will prioritize suggesting recipes that can be easily prepared at home. This allows the reception desk to prioritize requests based on their relevance by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can prioritize requests using an AI model that takes the user's geographical location as input and outputs requests based on their relevance.

[0079] The reception desk can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can suggest relevant recipes based on recipes the user has shared on social media. For example, the reception desk can suggest recipes using ingredients the user has shown interest in on social media. For example, the reception desk can suggest recipes from cooking experts the user follows on social media. In this way, relevant requests can be accepted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can accept requests using an AI model that takes the user's social media activity as input and outputs relevant requests.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is excited, the suggestion unit will provide visually appealing suggestions. By adjusting the way it presents suggestions according to the user's emotions, it becomes possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can adjust the presentation using an AI model that takes user emotion data as input and outputs a way to present suggestions.

[0081] The suggestion function can adjust the level of detail in its suggestions based on the importance of the recipe. For example, for important recipes, the suggestion function will provide suggestions that include detailed steps and precautions. For simple recipes, the suggestion function will provide suggestions that include concise steps. For recipes for special events, the suggestion function will also include suggestions for decoration and presentation. By adjusting the level of detail in suggestions based on the importance of the recipe, it becomes possible to provide the most suitable suggestions for the user. Recipe importance includes, but is not limited to, nutritional value and popularity. Some or all of the above processing in the suggestion function may be performed using, for example, AI, or not. For example, the suggestion function can adjust the level of detail using an AI model that takes the importance of the recipe as input and outputs the level of detail of the suggestion.

[0082] The suggestion unit can apply different suggestion algorithms depending on the recipe category when making suggestions. For example, in the case of a dessert recipe, the suggestion unit will make suggestions that include how to adjust the sweetness. For example, in the case of a main dish recipe, the suggestion unit will make suggestions that take into account volume and nutritional balance. For example, in the case of a snack recipe, the suggestion unit will suggest an easy way to make it. By applying different suggestion algorithms depending on the recipe category, more appropriate suggestions can be made. Recipe categories include, but are not limited to, Japanese food, Western food, and desserts. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can apply an algorithm using an AI model that takes the recipe category as input and outputs a suggestion algorithm.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit will provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit will provide a visually stimulating suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can adjust the length using an AI model that takes user emotion data as input and outputs the length of the suggestion.

[0084] The suggestion department can prioritize suggestions based on the timing of recipe submission. For example, if the user is in a hurry, the suggestion department will prioritize suggesting recipes that can be cooked quickly. If the user has ample time, the suggestion department will prioritize suggesting more elaborate recipes. If the user is preparing for a specific event, the suggestion department will prioritize suggesting recipes suitable for that event. This allows for optimal suggestions for the user by prioritizing suggestions based on the timing of recipe submission. The timing of recipe submission includes, but is not limited to, the submission date and season. Some or all of the above processing in the suggestion department may be performed using, for example, AI, or not. For example, the suggestion department can determine priorities using an AI model that takes the timing of recipe submission as input and outputs the priority of suggestions.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of the recipes. For example, the suggestion unit might suggest the recipe most relevant to the user's needs first. For example, the suggestion unit might prioritize suggesting recipes that are highly relevant based on the user's past preferences. For example, the suggestion unit might suggest recipes that are highly relevant based on the user's current living situation. By adjusting the order of suggestions based on the relevance of the recipes, it becomes possible to provide the most suitable suggestions for the user. Recipe relevance includes, but is not limited to, commonality of ingredients or similarity of cooking methods. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can adjust the order using an AI model that takes recipe relevance as input and outputs the order of suggestions.

[0086] The decision unit can estimate the user's emotions and adjust the amount of ingredients needed based on the estimated emotions. For example, if the user is stressed, the decision unit will suggest an amount that is easy to cook. For example, if the user is relaxed, the decision unit will suggest a generous amount. For example, if the user is in a hurry, the decision unit will suggest a minimum amount. In this way, by adjusting the amount of ingredients needed according to the user's emotions, it is possible to suggest a more appropriate amount of ingredients. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can adjust the amount using an AI model that takes user emotion data as input and outputs the amount of ingredients needed.

[0087] The decision unit can analyze the user's past consumption behavior to select an appropriate amount of food at the time of decision. For example, the decision unit can suggest the optimal amount based on the amount the user has consumed in the past. For example, the decision unit can analyze the user's past consumption patterns to suggest an amount that minimizes waste. For example, the decision unit can predict and suggest the amount of a specific food to be consumed based on the user's past consumption history. In this way, by analyzing the user's past consumption behavior, it is possible to select an optimal amount of food that minimizes waste. Past consumption behavior includes, but is not limited to, purchase history and consumption patterns. Some or all of the above processing in the decision unit may be performed using, for example, AI, or not using AI. For example, the decision unit can select an amount using an AI model that takes the user's past consumption behavior as input and outputs an appropriate amount of food.

[0088] The decision-making unit can customize the amount of ingredients based on the user's current living situation when making a decision. For example, if the user lives alone, the decision-making unit will suggest a small amount of ingredients. If the user lives with family, the decision-making unit will suggest an amount that will satisfy everyone in the family. If the user has a specific event planned, the decision-making unit will suggest an amount appropriate for that event. In this way, by customizing the amount of ingredients based on the user's current living situation, it is possible to suggest a more appropriate amount of ingredients. Current living situation includes, but is not limited to, family structure and frequency of meals. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can customize the amount using an AI model that takes the user's current living situation as input and outputs the amount of ingredients.

[0089] The decision unit can estimate the user's emotions and determine the priority of necessary ingredients based on the estimated emotions. For example, if the user is in a hurry, the decision unit will prioritize suggesting ingredients that can be cooked quickly. For example, if the user is relaxed, the decision unit will also suggest ingredients that take time to cook. For example, if the user is stressed, the decision unit will prioritize suggesting ingredients that can be cooked easily. In this way, by determining the priority of necessary ingredients according to the user's emotions, it is possible to suggest more appropriate ingredients. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can determine the priority using an AI model that takes user emotion data as input and outputs the priority of necessary ingredients.

[0090] The decision-making unit can select an appropriate amount of ingredients by considering the user's geographical location information during the decision-making process. For example, if the user is in a specific region, the decision-making unit will prioritize suggesting ingredients available in that region. For example, if the user is traveling, the decision-making unit will suggest ingredients that can be procured at the travel destination. For example, if the user is at home, the decision-making unit will suggest ingredients that can be easily cooked at home. In this way, the optimal amount of ingredients can be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can select the amount using an AI model that takes the user's geographical location information as input and outputs an appropriate amount of ingredients.

[0091] The decision-making unit can analyze the user's social media activity and adjust the amount of ingredients at the time of decision-making. For example, the decision-making unit may suggest the amount of relevant ingredients based on recipes shared by the user on social media. For example, the decision-making unit may suggest recipes using ingredients that the user has shown interest in on social media. For example, the decision-making unit may suggest recipes from cooking experts that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to suggest a more appropriate amount of ingredients. Social media activity includes, but is not limited to, posts and the number of followers. Some or all of the above processing in the decision-making unit may be performed using, for example, AI, or not using AI. For example, the decision-making unit can adjust the amount using an AI model that takes the user's social media activity as input and outputs the amount of ingredients.

[0092] The ordering system can estimate the user's emotions and adjust the ordering process based on those emotions. For example, if the user is stressed, the ordering system can provide a simple ordering procedure. If the user is relaxed, the ordering system can provide detailed ordering options. If the user is in a hurry, the ordering system can provide a way to complete the order quickly. By adjusting the ordering process according to the user's emotions, more appropriate orders can be placed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can adjust the process using an AI model that takes user emotion data as input and outputs ordering methods.

[0093] The ordering system can select an appropriate ordering method by referring to the user's past order history when an order is placed. For example, the ordering system can automatically display ingredients that the user has frequently ordered in the past as suggestions. For example, the ordering system can prioritize suggesting ordering methods that the user has used in the past (online, telephone, etc.). For example, the ordering system can predict and suggest ordering methods to be used at a specific time of day based on the user's past order history. This allows the system to select the optimal ordering method by referring to the user's past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select a method using an AI model that takes the user's past order history as input and outputs an appropriate ordering method.

[0094] The ordering system can customize the ordering process based on the user's current lifestyle. For example, if the user is busy, the ordering system can provide a way to complete the order quickly. If the user is relaxed, the ordering system can provide detailed ordering options. If the user has a specific event planned, the ordering system can suggest an ordering method suitable for that event. This allows for more appropriate ordering by customizing the ordering process based on the user's current lifestyle. Current lifestyle includes, but is not limited to, family structure and meal frequency. Some or all of the above processing in the ordering system may be performed using AI, for example, or not. For example, the ordering system can customize the ordering process using an AI model that takes the user's current lifestyle as input and outputs ordering methods.

[0095] The order processing unit can estimate the user's emotions and determine order priorities based on those emotions. For example, if the user is in a hurry, the order processing unit will prioritize orders that require quick attention. For example, if the user is relaxed, the order processing unit will prioritize detailed orders. For example, if the user is stressed, the order processing unit will prioritize simple orders. This allows for more appropriate orders by prioritizing orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the order processing unit may be performed using AI or not. For example, the order processing unit can determine priorities using an AI model that takes user emotion data as input and outputs order priorities.

[0096] The ordering system can select an appropriate ordering method when an order is placed, taking into account the user's geographical location. For example, if the user is in a specific region, the ordering system will prioritize ordering ingredients available in that region. If the user is traveling, the ordering system will order ingredients available at the travel destination. If the user is at home, the ordering system will suggest an ordering method that can be easily received at home. This allows the system to select the optimal ordering method by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select an ordering method using an AI model that takes the user's geographical location as input and outputs an appropriate ordering method.

[0097] The ordering system can analyze a user's social media activity and suggest ordering methods when an order is placed. For example, the ordering system can order relevant ingredients based on ingredients the user has shared on social media. For example, the ordering system can suggest recipes using ingredients the user has shown interest in on social media. For example, the ordering system can suggest recipes from cooking experts the user follows on social media. This allows for more appropriate orders by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower counts. Some or all of the above processing in the ordering system may be performed using AI, for example, or without AI. For example, the ordering system can suggest ordering methods using an AI model that takes the user's social media activity as input and outputs ordering methods.

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

[0099] The suggestion unit can estimate the user's emotions and suggest recipes based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting easy and quick recipes. If the user is relaxed, the suggestion unit will suggest recipes that can be enjoyed over time. If the user is in a hurry, the suggestion unit will suggest recipes that can be cooked quickly. By suggesting recipes according to the user's emotions, it is possible to provide more appropriate recipes. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can suggest recipes using an AI model that takes user emotion data as input and outputs recipes.

[0100] The decision unit can estimate the user's emotions and adjust the amount of ingredients needed based on the estimated emotions. For example, if the user is stressed, the decision unit will suggest an amount that is easy to cook. For example, if the user is relaxed, the decision unit will suggest a generous amount. For example, if the user is in a hurry, the decision unit will suggest a minimum amount. In this way, by adjusting the amount of ingredients needed according to the user's emotions, it is possible to suggest a more appropriate amount of ingredients. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can adjust the amount using an AI model that takes user emotion data as input and outputs the amount of ingredients needed.

[0101] The ordering system can estimate the user's emotions and adjust the ordering process based on those emotions. For example, if the user is stressed, the ordering system can provide a simple ordering procedure. If the user is relaxed, the ordering system can provide detailed ordering options. If the user is in a hurry, the ordering system can provide a way to complete the order quickly. By adjusting the ordering process according to the user's emotions, more appropriate orders can be placed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering system may be performed using AI or not. For example, the ordering system can adjust the process using an AI model that takes user emotion data as input and outputs ordering methods.

[0102] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions with detailed explanations. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is excited, the suggestion unit will provide visually appealing suggestions. By adjusting the way it presents suggestions according to the user's emotions, it becomes possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can adjust the presentation using an AI model that takes user emotion data as input and outputs a way to present suggestions.

[0103] The decision unit can estimate the user's emotions and determine the priority of necessary ingredients based on the estimated emotions. For example, if the user is in a hurry, the decision unit will prioritize suggesting ingredients that can be cooked quickly. For example, if the user is relaxed, the decision unit will also suggest ingredients that take time to cook. For example, if the user is stressed, the decision unit will prioritize suggesting ingredients that can be cooked easily. In this way, by determining the priority of necessary ingredients according to the user's emotions, it is possible to suggest more appropriate ingredients. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can determine the priority using an AI model that takes user emotion data as input and outputs the priority of necessary ingredients.

[0104] The suggestion unit can analyze the user's past request history and propose the most suitable recipe. For example, the suggestion unit may prioritize suggesting recipes that the user has frequently selected in the past. For example, the suggestion unit may suggest recipes that use ingredients that the user has liked in the past. For example, the suggestion unit may suggest recipes suitable for a specific time of day based on the user's past request history. In this way, the optimal recipe can be suggested by analyzing the user's past request history. Past request history includes, but is not limited to, past order history and survey results. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can suggest recipes using an AI model that takes the user's past request history as input and outputs the optimal recipe.

[0105] The decision unit can analyze the user's past consumption behavior to select an appropriate amount of food at the time of decision. For example, the decision unit can suggest the optimal amount based on the amount the user has consumed in the past. For example, the decision unit can analyze the user's past consumption patterns to suggest an amount that minimizes waste. For example, the decision unit can predict and suggest the amount of a specific food to be consumed based on the user's past consumption history. In this way, by analyzing the user's past consumption behavior, it is possible to select an optimal amount of food that minimizes waste. Past consumption behavior includes, but is not limited to, purchase history and consumption patterns. Some or all of the above processing in the decision unit may be performed using, for example, AI, or not using AI. For example, the decision unit can select an amount using an AI model that takes the user's past consumption behavior as input and outputs an appropriate amount of food.

[0106] The ordering system can select an appropriate ordering method by referring to the user's past order history when an order is placed. For example, the ordering system can automatically display ingredients that the user has frequently ordered in the past as suggestions. For example, the ordering system can prioritize suggesting ordering methods that the user has used in the past (online, telephone, etc.). For example, the ordering system can predict and suggest ordering methods to be used at a specific time of day based on the user's past order history. This allows the system to select the optimal ordering method by referring to the user's past order history. Past order history includes, but is not limited to, order date and time and order details. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select a method using an AI model that takes the user's past order history as input and outputs an appropriate ordering method.

[0107] The suggestion unit can apply different suggestion algorithms depending on the recipe category when making suggestions. For example, in the case of a dessert recipe, the suggestion unit will make suggestions that include how to adjust the sweetness. For example, in the case of a main dish recipe, the suggestion unit will make suggestions that take into account volume and nutritional balance. For example, in the case of a snack recipe, the suggestion unit will suggest an easy way to make it. By applying different suggestion algorithms depending on the recipe category, more appropriate suggestions can be made. Recipe categories include, but are not limited to, Japanese food, Western food, and desserts. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can apply an algorithm using an AI model that takes the recipe category as input and outputs a suggestion algorithm.

[0108] The ordering system can select an appropriate ordering method when an order is placed, taking into account the user's geographical location. For example, if the user is in a specific region, the ordering system will prioritize ordering ingredients available in that region. If the user is traveling, the ordering system will order ingredients available at the travel destination. If the user is at home, the ordering system will suggest an ordering method that can be easily received at home. This allows the system to select the optimal ordering method by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the ordering system may be performed using, for example, AI, or not. For example, the ordering system can select an ordering method using an AI model that takes the user's geographical location as input and outputs an appropriate ordering method.

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

[0110] Step 1: The reception desk receives user requests. User requests include what they want to eat, the time required, ingredients, cooking equipment, etc. For example, a user can enter requests such as "I want to make curry," "a dish that can be made in 30 minutes or less," or "I want to use chicken." Step 2: The Proposal Department proposes recipes based on the requests received by the Reception Department. The Proposal Department uses AI to utilize Kurashiru's recipe database as a RAG (Recipe Aggregation) and proposes recipes that match the user's requests. For example, if the user enters "I want to make curry," the Proposal Department searches for curry recipes in Kurashiru's recipe database and proposes them. Step 3: The decision unit determines the amount of ingredients needed based on the recipe proposed by the suggestion unit. The decision unit calculates the amount of ingredients needed, taking into account the user's inventory information and expiration dates. For example, if the user enters "I want to use chicken," the decision unit checks the user's inventory information and calculates the amount of chicken needed. Step 4: The ordering department orders any missing ingredients based on the quantities determined by the decision-making department. The ordering department uses an online shopping site to order the missing ingredients. For example, if the user enters "We are short on chicken," the ordering department will use an online shopping site to order chicken.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, proposal unit, decision unit, and order unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's request. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a recipe that suits the user's request using Kurashiru's recipes as RAG. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and determines the amount of ingredients needed based on the proposed recipe. The order unit is implemented by, for example, the control unit 46A of the smart device 14 and orders any missing ingredients from an online shopping site. 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.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, proposal unit, decision unit, and order unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's request. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a recipe that suits the user's request using Kurashiru's recipes as RAG. The decision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and determines the amount of ingredients needed based on the proposed recipe. The order unit is implemented, for example, by the control unit 46A of the smart glasses 214 and orders any missing ingredients from an online shopping site. 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.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, proposal unit, decision unit, and order unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's request. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a recipe that suits the user's request using Kurashiru's recipes as RAG. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the amount of ingredients needed based on the proposed recipe. The order unit is implemented by the control unit 46A of the headset terminal 314 and orders any missing ingredients from an online shopping site. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, proposal unit, decision unit, and order unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's request. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a recipe that suits the user's request using Kurashiru's recipes as RAG. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and determines the amount of ingredients needed based on the proposed recipe. The order unit is implemented by, for example, the control unit 46A of the robot 414 and orders any missing ingredients from an online shopping site. 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.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A reception desk that handles user requests, Based on the requests received by the aforementioned reception department, the proposal department proposes recipes, A determination unit that determines the amount of ingredients needed based on the recipe proposed by the aforementioned proposal unit, The system includes an ordering unit that orders any missing ingredients based on the amount of ingredients needed determined by the determination unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Suggest recipes using a specific recipe database as a RAG (Recipe Aggregation). The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned determination unit, The system determines the required amount of ingredients based on the user's inventory information and expiration dates. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned ordering section is, I use an online shopping site to order any missing ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Search for and suggest recipes based on user requests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned determination unit, Calculate the amount of ingredients needed based on the proposed recipe. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past request history and select the appropriate method for receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the recipe. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the recipe category. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the recipes were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the recipes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned determination unit, It estimates the user's emotions and adjusts the amount of ingredients needed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned determination unit, When making a decision, the system analyzes the user's past consumption behavior to select the appropriate amount of ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned determination unit, When making a decision, the amount of ingredients is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned determination unit, It estimates the user's emotions and determines the priority of necessary ingredients based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned determination unit, When making a decision, the system selects the appropriate amount of ingredients, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned determination unit, When making a decision, we analyze the user's social media activity and adjust the amount of ingredients accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ordering section is, It estimates the user's emotions and adjusts the ordering process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering section is, When an order is placed, the system will refer to the user's past order history to select the appropriate ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering section is, When placing an order, the ordering method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering section is, It estimates the user's emotions and determines order priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering section is, When an order is placed, the system will select the appropriate ordering method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned ordering section is, When an order is placed, we analyze the user's social media activity and suggest ways to place the order. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles user requests, Based on the requests received by the aforementioned reception department, the proposal department proposes recipes, A determination unit that determines the amount of ingredients needed based on the recipe proposed by the aforementioned proposal unit, The system includes an ordering unit that orders any missing ingredients based on the amount of ingredients needed determined by the determination unit. A system characterized by the following features.

2. The aforementioned proposal section is, Suggest recipes using a specific recipe database as a RAG (Recipe Aggregation). The system according to feature 1.

3. The aforementioned determination unit, The system determines the required amount of ingredients based on the user's inventory information and expiration dates. The system according to feature 1.

4. The aforementioned ordering section is, I use an online shopping site to order any missing ingredients. The system according to feature 1.

5. The aforementioned proposal section is, Search for and suggest recipes based on user requests. The system according to feature 1.

6. The aforementioned determination unit, Calculate the amount of ingredients needed based on the proposed recipe. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past request history and select the appropriate method for receiving requests. The system according to feature 1.

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