system

Abstract

We provide the system. [Solution] A means of collecting ingredient information entered by the user, A means of acquiring and analyzing purchase history, A method for analyzing recipe information, including necessary ingredients and steps, and comparing it with the ingredients you have on hand, A method using a generative model that generates and proposes alternatives based on the lack of materials, A means of providing notifications to encourage the purchase of shortages of food ingredients, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 cooking, flavoring failures due to forgetting to buy ingredients or overlooking cooking procedures are common problems, especially for novice cooks. This type of failure not only leads to waste of food ingredients but also causes damage to the successful cooking experience. Therefore, there is a need for a method to successfully cook by maximizing the utilization of the ingredients the user has and supplementing the lack of ingredients.

Means for Solving the Problems

[0005] This invention collects ingredient information entered by the user, analyzes purchase history to identify missing ingredients, and provides a system that analyzes recipe information including necessary ingredients and steps and compares it with the user's available ingredients. Furthermore, it uses an AI generative model to generate alternatives for missing ingredients and proposes them to the user. In addition, it provides notifications to encourage the purchase of missing ingredients. The invention also aims to support cooking success by collecting feedback and using it to improve the generative model.

[0006] A "user" is the entity that uses the system to receive instructions and suggestions during the cooking process.

[0007] "Ingredient information" refers to data provided by users, including the types and quantities of food and seasonings used.

[0008] "Purchase history" refers to data that shows information about food ingredients and seasonings that a user has purchased and recorded in the past.

[0009] "Analysis" refers to the process of extracting specific information, organizing it, and examining it in detail in order to understand it.

[0010] "Recipe information" refers to information that includes the ingredients and steps necessary to make a specific dish.

[0011] A "generative model" refers to an algorithm or program that uses AI technology to generate new proposals or alternatives.

[0012] An "alternative" refers to a proposal to supplement or replace the original materials or conditions when they are lacking.

[0013] A "notification" is a message sent by a system to inform a user of information or a suggestion.

[0014] "Feedback" refers to information that includes suggestions from users, as well as their evaluations and impressions of the system's operation. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is an AI system for successful cooking, providing a means for effectively utilizing the ingredients and seasonings a user possesses. The system analyzes information about the ingredients and seasonings the user has at home, along with past purchase history. As a result, it identifies missing ingredients and provides alternative suggestions using an AI-generated model. It also provides notifications to encourage the purchase of missing ingredients.

[0037] Specifically, users input information about the ingredients and condiments they have at home through an application or web interface. This includes a function to record what's in their refrigerator and cupboards. The terminal sends the entered data to a server, which stores it in a database.

[0038] Next, the server retrieves the user's purchase history via an API. This allows for the collection of history, including previously purchased food items, in a digital format. Through this, the server identifies seasonings used in the past and frequently purchased ingredients, and analyzes the user's purchasing patterns.

[0039] The user selects a recipe for a dish they want to make, and this information is sent to the server. The server analyzes the required ingredients for the recipe and compares them to the ingredients the user has on hand. At this point, any missing ingredients are identified, and an AI generation model is activated. This model devises alternatives to supplement the missing ingredients and displays them as suggestions on the device.

[0040] For example, if a user chooses to make teriyaki chicken but is short on soy sauce, a key seasoning, the AI ​​model will generate a suggestion such as "substitute with mirin and salt to adjust the flavor." Furthermore, it will notify the user to add soy sauce to their shopping list so that it will be helpful when they purchase ingredients next time.

[0041] Thus, this system aims to help users succeed in cooking without making mistakes and to reduce food waste. It also allows for further improvement of the user experience through a feedback function, enabling the system to continuously learn and improve.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users input information about the ingredients and seasonings they have at home using an application or web interface. Once the user has finished inputting the information, the device sends this information to the server. The server stores the received information in a database and records it as the user's available ingredients.

[0045] Step 2:

[0046] The server accesses an external purchase history API to retrieve purchase history. Here, it obtains the user's past purchase history and collects purchase data related to ingredients and seasonings. The server analyzes this purchase history data to identify what the user frequently buys, which ingredients are currently out of stock, and stores this information in a database.

[0047] Step 3:

[0048] The user selects a recipe for the dish they want to make within the application. The selection information is sent from the terminal to the server. The server retrieves the details of the selected recipe from its database and analyzes the ingredients and steps required for the dish. A list of required ingredients is created and compared with the user's available ingredients.

[0049] Step 4:

[0050] The server compares the ingredients the user has with the ingredients required for the recipe and identifies any missing ingredients. Based on the identified missing ingredients, an AI generation model is activated to generate alternatives for modifying the recipe using the other ingredients the user has.

[0051] Step 5:

[0052] The generated alternatives are sent to the device and displayed to the user. The user reviews this information and uses it as a guide to proceed with cooking. For example, if soy sauce is in short supply, the AI ​​model might suggest using mirin and salt as substitutes.

[0053] Step 6:

[0054] The server generates purchase suggestions for missing materials. Using the user's purchase history and current material shortage information, the server generates a notification of materials to be purchased in the next shopping trip. The terminal sends this notification to the user in real time, encouraging them to use it as a shopping list.

[0055] Thus, the system's primary focus is on supporting the user's cooking experience and preventing mistakes.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern households, it is often difficult to effectively utilize the ingredients available when cooking, leading to frequent shortages of ingredients and hindering smooth cooking planning. Furthermore, reducing food waste and providing appropriate alternatives for efficient cooking are crucial challenges. Additionally, there is a need to address the lack of opportunities to create ingredient shopping lists based on user purchasing trends and to learn about the proper use of ingredients.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for collecting food information entered by the user, means for acquiring and analyzing purchase history, and means for analyzing cooking information including necessary ingredients and preparation processes, and comparing it with the ingredients on hand. This makes it possible to identify missing ingredients and generate alternative suggestions for effectively utilizing ingredients using a generation system. Furthermore, by analyzing the user's purchasing trends and adding missing ingredients to the purchase list, it is possible to provide information that will be useful for future purchases.

[0061] "Food information" refers to data entered by users about ingredients and seasonings they have at home, and is recorded through barcodes or manual input.

[0062] "Purchase history" refers to a record of products and food items that a user has purchased in the past, and is digital data obtained through online platforms and integration with loyalty cards.

[0063] "Cooking information" refers to data that includes the necessary ingredients and steps for the dish you want to make, and describes the details of a recipe that can be searched or selected within the application.

[0064] A "generation system" refers to a program that utilizes AI technology to provide alternative options for insufficient materials, presenting users with alternative solutions.

[0065] An "alternative" refers to an alternative material or means suggested to compensate for materials that the user does not have on hand, and is a choice provided by the generation system.

[0066] The "information display section" refers to an interface for instantly displaying suggestions and notifications to the user, and can be an application or web screen.

[0067] A description of the embodiment for carrying out the invention will be provided.

[0068] This invention is a system that enables users to effectively utilize information about ingredients and seasonings they have at home to ensure successful cooking. Users input food information via devices such as smartphones and personal computers. Specifically, they use barcode scanners or manual input to record ingredients and seasonings in their refrigerators and cupboards as a digital list.

[0069] The terminal transmits the entered food information to the server in real time. The server stores this information in a database and manages it as food information organized for each user.

[0070] The server uses an API to retrieve the user's purchase history. This history data includes online shopping history and loyalty card purchase history, which the server analyzes to understand the user's purchasing patterns. For example, it can deduce trends in frequently purchased food items and condiments used in the past.

[0071] The user selects a recipe for the dish they want to make within the application. This recipe contains the necessary ingredients and cooking steps, and the server analyzes this information based on the user's selection.

[0072] The server compares the ingredients of the analyzed recipe with the user's available ingredients to identify any missing ingredients. It then activates a generative AI model to generate alternatives for the missing ingredients. For example, if a user is making teriyaki chicken and lacks soy sauce, the AI ​​might suggest substituting mirin and salt to adjust the flavor.

[0073] Furthermore, the server generates a notification to add any missing ingredients to the next shopping list. This notification is displayed on the device to help the user remember to buy the ingredients on their next shopping trip.

[0074] As a concrete example, a user can input the following prompt into the AI ​​model to obtain more specific suggestions:

[0075] "What can we make for dinner tonight using the ingredients we have in our refrigerator?"

[0076] In this way, this system helps users effectively utilize the ingredients they have, reduces food waste, and supports them in successfully preparing meals.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users input information about food items they have at home. Using their smartphones or PCs, users enter information about ingredients and condiments in their refrigerators and cupboards into the app. Barcode scanning and text forms are used for input. The entered information includes data such as "ingredient name," "quantity," and "expiration date."

[0080] Step 2:

[0081] The terminal sends the entered food information to the server. The terminal uses a network connection to send the entered data to the server in real time. The server analyzes the received data and records it in a food information database. This organizes the food information for each user.

[0082] Step 3:

[0083] The server retrieves purchase history data. Through an API, the server collects the user's past purchase history data. This data, obtained from online platforms and loyalty card systems, includes information such as "purchase date," "item name," and "price." The server analyzes this data to model the user's purchasing patterns.

[0084] Step 4:

[0085] The user selects the recipe they want to make. The user chooses a dish of interest from a list of recipes provided within the app. The selected recipe is sent to the server along with the necessary ingredients and cooking instructions. The recipe data includes "ingredient names," "quantities," and "instructions."

[0086] Step 5:

[0087] The server compares the recipe with the food information on hand. The server analyzes the required ingredients in the recipe and compares them with the food information entered by the user. Here, the server identifies any missing ingredients and lists them. As a result, a list of missing ingredients is output.

[0088] Step 6:

[0089] The server activates a generative AI model to generate alternatives. The server passes a list of missing ingredients to the generative AI model, which then devises alternatives to address the shortages. For example, it might generate a suggestion such as, "If soy sauce is unavailable, substitute with mirin and salt." This suggestion includes the alternative ingredients and how to use them.

[0090] Step 7:

[0091] The server sends suggestions and notifications to the device. The generated alternatives and lists of missing ingredients are delivered to the device. The device displays the suggestions to the user on the screen and notifies them to add any missing ingredients to their shopping list. The user can review this and use it to help with their next shopping trip or cooking.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Modern consumers are busy, yet there is a growing demand for efficient meal preparation at home. However, a lack of knowledge about what ingredients are available, the effort required to purchase missing ingredients, and alternative ingredients can increase the time and effort involved in cooking. This can result in food waste, cooking failures, and wasted time.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for collecting food information entered by the user, means for acquiring and analyzing purchase history, and means including a communication device that allows the user to pre-order any missing food items. This allows the user to efficiently understand what ingredients they have on hand and easily obtain alternatives for missing ingredients, thereby reducing the effort and potential failures involved in cooking.

[0097] A "user" is someone who uses the system to input food information and receives necessary ingredients or alternative suggestions.

[0098] "Food information" refers to data about ingredients and seasonings owned by the user, and is an important element in cooking preparation.

[0099] "Purchase history" refers to a record of food and condiments that a user has purchased in the past, and is information used to analyze the user's purchasing patterns.

[0100] "Processing information" refers to detailed data about the ingredients and steps required to prepare a dish.

[0101] A "generative model" is a system that utilizes artificial intelligence technology to create alternative solutions for missing materials and present them to the user.

[0102] A "communication device" is a device that enables the transmission and reception of data via an external network, and is used for processing orders and sending notifications.

[0103] A "communication screen" is an interface used to display suggestions and notifications to the user in real time.

[0104] This invention is a system that efficiently supports cooking preparation based on food information input by the user. The server collects food information, retrieves and analyzes purchase history. This allows the system to compare the food items the user owns with the required ingredients based on the selected recipe and identify any missing ingredients.

[0105] The server uses a generative model to create alternatives for the food items that are in short supply. This generative AI model presents multiple usable alternatives for the missing ingredients. Users can review these alternatives via their terminal and, if necessary, order the missing food items in advance through a communication device. Furthermore, a notification system can be used to send real-time notifications to users regarding food shortages, prompting them to procure the items.

[0106] This invention is programmed using Python and the requests library. Through a series of processes including data collection and analysis, generation of alternatives using generative models, and transmission and reception of data using communication devices, users can easily gather the necessary materials.

[0107] For example, if a user wants to make a pizza and has cheese and bell peppers in the refrigerator but no tomato sauce, the system can suggest alternatives such as "substitute with ketchup and basil." An example of a prompt message could be in the format of "Current ingredient list: cheese, bell peppers. What ingredients are missing to make the pizza?" This enables efficient ingredient management and purchasing simply by entering the necessary information.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user uses a device to input information about the food items they have on hand.

[0111] The entered information includes the names and quantities of ingredients and seasonings. The terminal sends this information to the server, which stores it in a database. The input in this step is the food information provided by the user, and the output is the data stored on the server.

[0112] Step 2:

[0113] The server retrieves the user's purchase history from an external database.

[0114] The acquired purchase history data includes information about previously purchased foods and their frequency. The server analyzes this information to identify the user's purchasing patterns. The input for this step is the purchase history retrieved from an external database, and the output is the analyzed purchasing pattern information.

[0115] Step 3:

[0116] The user selects the recipe for the dish they want to make on their device.

[0117] The recipe information is sent to the server, which performs analysis. The ingredients required for the recipe are compared with the user's available food information, and any missing ingredients are identified. The input for this step is the recipe information selected by the user, and the output is a list of missing ingredients.

[0118] Step 4:

[0119] The server uses a generative AI model to generate alternative solutions for any missing materials.

[0120] The generative model calculates available alternative ingredients based on the input information about the missing ingredients. The generated alternatives are sent to the terminal and displayed to the user. The input for this step is a list of missing ingredients, and the output is a list of alternatives.

[0121] Step 5:

[0122] The device sends a notification to the user prompting them to acquire any missing materials.

[0123] The notification includes information on alternatives and options for ordering online. This gives users an easy way to supplement missing ingredients. The input for this step is the generated alternatives, and the output is the notification information for the user.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention relates to an AI system equipped with emotion recognition capabilities to support the cooking process. It measures the user's emotions and provides personalized suggestions and notifications accordingly, offering a more personalized cooking experience. The system analyzes recipes based on user-inputted ingredient information and purchase history, and uses an AI-generated model to suggest optimal alternatives. Furthermore, by combining this with an emotion engine, it recognizes the user's emotional state and provides suggestions tailored to those emotions.

[0126] Specifically, users input information about the ingredients and seasonings they have at home through the application. The device sends this data to a server, which stores it in a database. The server uses the collected data to retrieve the user's purchase history via an API and compares it with the ingredients stored in the database.

[0127] When a user selects a dish they want to make, the server analyzes the necessary ingredients and steps for that recipe. It identifies any missing ingredients, and an AI generative model generates alternatives. In addition, an emotion engine recognizes the user's current emotional state from their facial expressions and voice. Based on this information, the server adjusts the suggestions and sends alternatives that take the user's emotions into consideration to the device. For example, if the recognized emotion is "anxiety," a simplified suggestion will be provided.

[0128] Furthermore, a feature has been added that adjusts the content and timing of notifications according to the user's emotional state. This adjustment optimizes the timing of purchasing necessary ingredients based on emotions; for example, if the user is feeling stressed, it will send purchase suggestions that take that situation into consideration.

[0129] By taking user emotions into consideration in this way, it becomes possible to provide a richer and more successful cooking experience and reduce food waste.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] Users input the ingredients and seasonings they have at home using an application or web interface. The entered information is sent from the terminal to the server, which stores this data in a database.

[0133] Step 2:

[0134] The server retrieves the user's purchase history via an API. By collecting past purchase data, it understands the user's purchasing patterns. This allows for the identification of frequently purchased food items and condiments. The server stores this data in a database.

[0135] Step 3:

[0136] The user selects a recipe for the dish they want to make. The device sends this information to the server, which retrieves the selected recipe from its database and analyzes the necessary ingredients and steps. It then compares this information with the user's available ingredients to identify any missing ingredients.

[0137] Step 4:

[0138] The server compares the ingredients it has on hand with the ingredients required for the recipe and activates an AI generation model based on the missing ingredients. The generation model devises alternatives to compensate for the missing ingredients and prepares these suggestions.

[0139] Step 5:

[0140] The emotion engine analyzes the user's facial expressions and voice on the device to recognize their current emotional state. The recognized emotional information is sent to a server and used to adjust the suggestions. The server generates emotionally sensitive suggestions and sends them to the device. For example, if the user is feeling anxious, a simplified suggestion will be provided.

[0141] Step 6:

[0142] The device displays suggestions to the user. The user then proceeds with cooking based on these suggestions. The suggestions are optimized for the user's situation and emotions.

[0143] Step 7:

[0144] The server adjusts the content and timing of purchase suggestion notifications based on the user's emotional state. The device provides these notifications to the user in real time, encouraging them to use them to help with their next grocery purchase. For example, purchase suggestions are adjusted to appear during times when the user is calm.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Conventional meal preparation support systems fail to consider the user's emotional state when making suggestions, making it difficult to provide advice optimized for the user's needs in specific situations. Furthermore, suggestions based on available ingredients and purchase history are not adequately integrated, making efficient resource utilization difficult and resulting in waste.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for collecting user resource information, means for acquiring and analyzing purchase history, means for analyzing necessary materials and comparing them with existing materials, means for recognizing the user's emotional state and adjusting the suggested content accordingly, and means for providing notifications to encourage the purchase of insufficient resources. This enables personalized suggestions that take the user's emotions into consideration, resulting in efficient resource utilization and an improved user experience.

[0150] "Resource information" refers to detailed information about ingredients and seasonings owned by the user, including attributes such as quantity and expiration date.

[0151] "Purchase history" refers to information about materials and items that a user has purchased in the past, and consists of data such as the date and time of purchase and the quantity.

[0152] An "alternative" is another option proposed to fill in the missing materials for the user, and it is optimized by a generative model.

[0153] A "generative model" is an algorithm that uses artificial intelligence technology to analyze data and automatically generate optimal suggestions and solutions.

[0154] "Emotional state" refers to the user's current psychological state, which is recognized in real time from facial expressions, voice, and other factors.

[0155] A "notification" is a message that conveys information or suggestions to a user, and its timing and content are adjusted to meet the user's needs.

[0156] This invention is a system for providing users with personalized cooking suggestions that take their emotions into consideration. This system combines ingredient information and emotion recognition data, and uses a generative AI model to present the optimal alternative. The system includes the following elements:

[0157] Users input information about ingredients and seasonings they have at home through the application. Specifically, they can use a smartphone or tablet to enter details such as type, quantity, and expiration date. The device then transmits this data to the server via the internet.

[0158] The server stores the received material information in a database and retrieves the user's purchase history via an API based on that information. As a software platform, an open-source database management system can be used for server-side data analysis. This data will be used for subsequent analysis.

[0159] When a user selects a dish they want to make, the server analyzes the plan and identifies key ingredients and processes. If necessary, it passes missing ingredients to a generative AI model, which then generates the optimal alternative. This generative AI model can utilize machine learning libraries developed in, for example, Python, and calculates the optimal solution in real time.

[0160] Furthermore, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and the server processes this data using an emotion engine to recognize the user's psychological state. Based on this information, the server generates suggestions tailored to the user's emotions and sends them to the device.

[0161] A concrete example of a prompt statement is, "I don't have much time today, but I want to make a proper dinner." In response to this prompt, the system can suggest a simple recipe that can be prepared in a short amount of time.

[0162] These features enable the system to offer suggestions that resonate with the user's emotions, improving both the efficiency of meal preparation and the user experience.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users enter information about ingredients and seasonings they have at home into a dedicated application on their device. This includes detailed information such as type, quantity, and expiration date. The entered data is transmitted from the device to a server via the internet. The server receives this data and records it in a database. This ensures that the system stores the most up-to-date information about the ingredients the user has on hand.

[0166] Step 2:

[0167] The server retrieves purchase history via an API based on the user's material information. The retrieved purchase history data is stored in a database and serves as foundational data for understanding the user's past purchasing patterns. The server compares the purchase history with the current material information to identify which materials are lacking.

[0168] Step 3:

[0169] The user selects the dish they want to make within the application. The server analyzes this selected recipe information to identify the necessary ingredients and steps. Based on this analysis, any missing ingredients are identified and passed to a generative AI model. This model calculates the optimal alternative ingredients based on past data and the current situation, and generates suggestions.

[0170] Step 4:

[0171] The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. The captured data is sent to a server, where an emotion engine analyzes it. The server understands the user's emotional state in real time and initiates a process to adjust the suggestions based on that information.

[0172] Step 5:

[0173] Based on the sentiment analysis results, the server adjusts the alternatives generated by the generative AI model to create suggestions that best suit the user's emotions. For example, if the user is expressing anxiety, a simplified recipe will be suggested. The adjusted suggestions are sent to the device, and the user can receive the information through the application.

[0174] Step 6:

[0175] The server optimizes not only suggestions but also the content and timing of notifications based on the user's emotions. If a user is feeling stressed, it adjusts product suggestions and notifications to better promote relaxation. The device receives this information and presents it to the user at the appropriate time.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In today's busy daily lives, there is a lack of cooking support tailored to individual emotional states, and cooking often becomes a source of stress. Therefore, it is necessary to provide a personalized cooking experience that responds to the user's emotions, thereby improving the success rate of cooking and reducing the purchase and use of unnecessary ingredients.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for acquiring information about ingredients entered by the user, means for acquiring and analyzing purchase history, means for analyzing information about recipes including necessary ingredients and processes and comparing them with the ingredients owned, means for using a generative model to generate and suggest alternatives based on missing ingredients, means for reading the user's emotional state and utilizing emotion recognition to make adjustment suggestions, and means for providing music and visuals based on the learner's current emotional state. This makes it possible to provide optimal cooking support according to the user's emotional state.

[0181] "Means for acquiring ingredient information" refers to a function that collects data about the ingredients used in the dishes that the user prepares.

[0182] "Means for acquiring and analyzing purchase history" refers to a function that acquires and analyzes a user's past purchase data.

[0183] "A means of analyzing information about a recipe and comparing it to the ingredients you have" refers to a function that analyzes the details of a provided recipe and compares them to the ingredients you have on hand.

[0184] "Means of using generative models to generate and propose alternatives" refers to a function that uses AI models to generate alternative options for the necessary materials and proposes them to the user.

[0185] "A means of utilizing emotion recognition to read emotional states and make adjustment suggestions" refers to a function that analyzes the user's facial expressions and voice to recognize emotions and then makes suggestions based on those emotions.

[0186] "A means of providing music and visuals based on the participant's current emotional state" refers to a function that provides users with appropriate music and visual support based on the results of emotional analysis.

[0187] To realize this invention, the system is configured as a user terminal and a cloud server. The user terminal is a device such as a smartphone or smart glasses, which collects data through its camera and microphone and analyzes emotions in real time using an emotion recognition API. Microsoft's Azure® Face API and Google® Cloud Speech-to-Text API are suitable tools for emotion recognition.

[0188] The server processes large amounts of data and stores the ingredient information entered by the user in a database. It also retrieves purchase history and compares the ingredients the user owns with the provided recipes. An AI generative model (such as OpenAI's GPT-3) is used to generate alternatives for missing ingredients.

[0189] If the user's emotional state is "specific," the server recognizes that state and sends a customized meal suggestion to the terminal. Furthermore, it provides multimedia content such as music and visual support; for example, if the user's emotion is anxiety, it plays relaxing music.

[0190] As a concrete example, let's say a user is working on a project to bake bread. If the user is feeling anxious, the system can provide support by simplifying suggestions and displaying a specific music playlist on their device, thus creating a sense of reassurance. In this way, the probability of success in the cooking process can be significantly increased.

[0191] An example of a prompt message is: "If the user is anxious, generate support suggestions to simplify the task and play relaxing music."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The user inputs ingredient information into the terminal. The terminal sends this information to the cloud server. The input includes ingredient name, quantity, type, etc., and this information is formatted and output as data to be recorded in the database.

[0195] Step 2:

[0196] The server retrieves the user's purchase history data via an API. The retrieved data includes past purchase dates and items, which are then analyzed and compiled into a list to compare with the user's current food inventory.

[0197] Step 3:

[0198] The server uses the entered ingredient information and purchase history to check the ingredients required for the recipe selected by the user and compares them with the user's owned ingredients. This comparison reveals any missing ingredients, which are then compiled into a list.

[0199] Step 4:

[0200] The server generates alternatives using a generative AI model for missing ingredients. The input is a list of missing ingredients, and the alternatives are output as substitutes for ingredients or modified recipes. The generation process is set up using prompts.

[0201] Step 5:

[0202] The device collects the user's emotional state through its camera and microphone and analyzes it using an emotion recognition API. Based on this analysis, the device sends the user's emotional data to the server. The output is data on the emotional state.

[0203] Step 6:

[0204] The server considers emotional data and alternatives, and sends recipe suggestions and simplified procedures tailored to the user's emotions to the device. Relaxing music and visual materials are also added as support information, depending on the emotional state.

[0205] 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.

[0206] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0207] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0212] 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.

[0213] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0214] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0215] 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.

[0216] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0217] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0218] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0219] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0220] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0221] This invention is an AI system for successful cooking, providing a means for effectively utilizing the ingredients and seasonings a user possesses. The system analyzes information about the ingredients and seasonings the user has at home, along with past purchase history. As a result, it identifies missing ingredients and provides alternative suggestions using an AI-generated model. It also provides notifications to encourage the purchase of missing ingredients.

[0222] Specifically, users input information about the ingredients and condiments they have at home through an application or web interface. This includes a function to record what's in their refrigerator and cupboards. The terminal sends the entered data to a server, which stores it in a database.

[0223] Next, the server retrieves the user's purchase history via an API. This allows for the collection of history, including previously purchased food items, in a digital format. Through this, the server identifies seasonings used in the past and frequently purchased ingredients, and analyzes the user's purchasing patterns.

[0224] The user selects a recipe for a dish they want to make, and this information is sent to the server. The server analyzes the required ingredients for the recipe and compares them to the ingredients the user has on hand. At this point, any missing ingredients are identified, and an AI generation model is activated. This model devises alternatives to supplement the missing ingredients and displays them as suggestions on the device.

[0225] For example, if a user chooses to make teriyaki chicken but is short on soy sauce, a key seasoning, the AI ​​model will generate a suggestion such as "substitute with mirin and salt to adjust the flavor." Furthermore, it will notify the user to add soy sauce to their shopping list so that it will be helpful when they purchase ingredients next time.

[0226] Thus, this system aims to help users succeed in cooking without making mistakes and to reduce food waste. It also allows for further improvement of the user experience through a feedback function, enabling the system to continuously learn and improve.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] Users input information about the ingredients and seasonings they have at home using an application or web interface. Once the user has finished inputting the information, the device sends this information to the server. The server stores the received information in a database and records it as the user's available ingredients.

[0230] Step 2:

[0231] The server accesses an external purchase history API to retrieve purchase history. Here, it obtains the user's past purchase history and collects purchase data related to ingredients and seasonings. The server analyzes this purchase history data to identify what the user frequently buys, which ingredients are currently out of stock, and stores this information in a database.

[0232] Step 3:

[0233] The user selects a recipe for the dish they want to make within the application. The selection information is sent from the terminal to the server. The server retrieves the details of the selected recipe from its database and analyzes the ingredients and steps required for the dish. A list of required ingredients is created and compared with the user's available ingredients.

[0234] Step 4:

[0235] The server compares the ingredients the user has with the ingredients required for the recipe and identifies any missing ingredients. Based on the identified missing ingredients, an AI generation model is activated to generate alternatives for modifying the recipe using the other ingredients the user has.

[0236] Step 5:

[0237] The generated alternatives are sent to the device and displayed to the user. The user reviews this information and uses it as a guide to proceed with cooking. For example, if soy sauce is in short supply, the AI ​​model might suggest using mirin and salt as substitutes.

[0238] Step 6:

[0239] The server generates purchase suggestions for missing materials. Using the user's purchase history and current material shortage information, the server generates a notification of materials to be purchased in the next shopping trip. The terminal sends this notification to the user in real time, encouraging them to use it as a shopping list.

[0240] Thus, the system's primary focus is on supporting the user's cooking experience and preventing mistakes.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0243] In modern households, it is often difficult to effectively utilize the ingredients available when cooking, leading to frequent shortages of ingredients and hindering smooth cooking planning. Furthermore, reducing food waste and providing appropriate alternatives for efficient cooking are crucial challenges. Additionally, there is a need to address the lack of opportunities to create ingredient shopping lists based on user purchasing trends and to learn about the proper use of ingredients.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for collecting food information entered by the user, means for acquiring and analyzing purchase history, and means for analyzing cooking information including necessary ingredients and preparation processes, and comparing it with the ingredients on hand. This makes it possible to identify missing ingredients and generate alternative suggestions for effectively utilizing ingredients using a generation system. Furthermore, by analyzing the user's purchasing trends and adding missing ingredients to the purchase list, it is possible to provide information that will be useful for future purchases.

[0246] "Food information" refers to data entered by users about ingredients and seasonings they have at home, and is recorded through barcodes or manual input.

[0247] "Purchase history" refers to a record of products and food items that a user has purchased in the past, and is digital data obtained through online platforms and integration with loyalty cards.

[0248] "Cooking information" refers to data that includes the necessary ingredients and steps for the dish you want to make, and describes the details of a recipe that can be searched or selected within the application.

[0249] A "generation system" refers to a program that utilizes AI technology to provide alternative options for insufficient materials, presenting users with alternative solutions.

[0250] An "alternative" refers to an alternative material or means suggested to compensate for materials that the user does not have on hand, and is a choice provided by the generation system.

[0251] The "information display section" refers to an interface for instantly displaying suggestions and notifications to the user, and can be an application or web screen.

[0252] A description of the embodiment for carrying out the invention will be provided.

[0253] This invention is a system that enables users to effectively utilize information about ingredients and seasonings they have at home to ensure successful cooking. Users input food information via devices such as smartphones and personal computers. Specifically, they use barcode scanners or manual input to record ingredients and seasonings in their refrigerators and cupboards as a digital list.

[0254] The terminal transmits the entered food information to the server in real time. The server stores this information in a database and manages it as food information organized for each user.

[0255] The server uses an API to retrieve the user's purchase history. This history data includes online shopping history and loyalty card purchase history, which the server analyzes to understand the user's purchasing patterns. For example, it can deduce trends in frequently purchased food items and condiments used in the past.

[0256] The user selects a recipe for the dish they want to make within the application. This recipe contains the necessary ingredients and cooking steps, and the server analyzes this information based on the user's selection.

[0257] The server compares the ingredients of the analyzed recipe with the user's available ingredients to identify any missing ingredients. It then activates a generative AI model to generate alternatives for the missing ingredients. For example, if a user is making teriyaki chicken and lacks soy sauce, the AI ​​might suggest substituting mirin and salt to adjust the flavor.

[0258] Furthermore, the server generates a notification to add any missing ingredients to the next shopping list. This notification is displayed on the device to help the user remember to buy the ingredients on their next shopping trip.

[0259] As a concrete example, a user can input the following prompt into the AI ​​model to obtain more specific suggestions:

[0260] "What can we make for dinner tonight using the ingredients we have in our refrigerator?"

[0261] In this way, this system helps users effectively utilize the ingredients they have, reduces food waste, and supports them in successfully preparing meals.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] Users input information about food items they have at home. Using their smartphones or PCs, users enter information about ingredients and condiments in their refrigerators and cupboards into the app. Barcode scanning and text forms are used for input. The entered information includes data such as "ingredient name," "quantity," and "expiration date."

[0265] Step 2:

[0266] The terminal sends the entered food information to the server. The terminal uses a network connection to send the entered data to the server in real time. The server analyzes the received data and records it in a food information database. This organizes the food information for each user.

[0267] Step 3:

[0268] The server retrieves purchase history data. Through an API, the server collects the user's past purchase history data. This data, obtained from online platforms and loyalty card systems, includes information such as "purchase date," "item name," and "price." The server analyzes this data to model the user's purchasing patterns.

[0269] Step 4:

[0270] The user selects the recipe they want to make. The user chooses a dish of interest from a list of recipes provided within the app. The selected recipe is sent to the server along with the necessary ingredients and cooking instructions. The recipe data includes "ingredient names," "quantities," and "instructions."

[0271] Step 5:

[0272] The server compares the recipe with the food information on hand. The server analyzes the required ingredients in the recipe and compares them with the food information entered by the user. Here, the server identifies any missing ingredients and lists them. As a result, a list of missing ingredients is output.

[0273] Step 6:

[0274] The server activates a generative AI model to generate alternatives. The server passes a list of missing ingredients to the generative AI model, which then devises alternatives to address the shortages. For example, it might generate a suggestion such as, "If soy sauce is unavailable, substitute with mirin and salt." This suggestion includes the alternative ingredients and how to use them.

[0275] Step 7:

[0276] The server sends suggestions and notifications to the device. The generated alternatives and lists of missing ingredients are delivered to the device. The device displays the suggestions to the user on the screen and notifies them to add any missing ingredients to their shopping list. The user can review this and use it to help with their next shopping trip or cooking.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] Modern consumers are busy, yet there is a growing demand for efficient meal preparation at home. However, a lack of knowledge about what ingredients are available, the effort required to purchase missing ingredients, and alternative ingredients can increase the time and effort involved in cooking. This can result in food waste, cooking failures, and wasted time.

[0280] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for collecting food information input by the user, means for obtaining and analyzing the purchase history, and means including a communication device capable of pre-ordering insufficient food. As a result, the user can efficiently grasp the materials at hand and easily obtain alternatives for the insufficient materials, reducing the labor and failure of cooking.

[0282] The "user" is a person who inputs food information using the system and receives the necessary materials and alternatives.

[0283] The "food information" is data related to the ingredients and seasonings owned by the user and is an important element in preparing a dish.

[0284] The "purchase history" is a record of the foods and seasonings purchased by the user in the past and is information used to analyze the user's purchase pattern.

[0285] The "processing information" is detailed data on the materials and processes necessary to create a dish.

[0286] The "generation model" is a system that utilizes artificial intelligence technology to create alternatives for the lacking materials and present them to the user.

[0287] The "communication device" is a device capable of transmitting and receiving data via an external network and is used to process orders and send notifications.

[0288] The "communication screen" is an interface for displaying proposals and notifications to the user in real time.

[0289] This invention is a system that inputs the food information held by the user and efficiently supports the preparation of a dish based on that information. The server collects the food information, obtains and analyzes the purchase history. Thereby, the user's available foods at hand are compared with the necessary materials based on the selected recipe, and the lacking foods are identified.

[0290] The server uses a generative model to create alternatives for the food items that are in short supply. This generative AI model presents multiple usable alternatives for the missing ingredients. Users can review these alternatives via their terminal and, if necessary, order the missing food items in advance through a communication device. Furthermore, a notification system can be used to send real-time notifications to users regarding food shortages, prompting them to procure the items.

[0291] This invention is programmed using Python and the requests library. Through a series of processes including data collection and analysis, generation of alternatives using generative models, and transmission and reception of data using communication devices, users can easily gather the necessary materials.

[0292] For example, if a user wants to make a pizza and has cheese and bell peppers in the refrigerator but no tomato sauce, the system can suggest alternatives such as "substitute with ketchup and basil." An example of a prompt message could be in the format of "Current ingredient list: cheese, bell peppers. What ingredients are missing to make the pizza?" This enables efficient ingredient management and purchasing simply by entering the necessary information.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The user uses a device to input information about the food items they have on hand.

[0296] The entered information includes the names and quantities of ingredients and seasonings. The terminal sends this information to the server, which stores it in a database. The input in this step is the food information provided by the user, and the output is the data stored on the server.

[0297] Step 2:

[0298] The server retrieves the user's purchase history from an external database.

[0299] The acquired purchase history data includes information on foods purchased in the past and their frequencies. The server analyzes this information to identify the user's purchase patterns. The input for this step is the purchase history obtained from the external database, and the output is the information on the analyzed purchase patterns.

[0300] Step 3:

[0301] The user selects a recipe for the dish they want to cook on the terminal.

[0302] The recipe information is sent to the server, and the server performs an analysis. The ingredients required for the recipe are compared with the user's available food information, and the missing ingredients are identified. The input for this step is the recipe information selected by the user, and the output is a list of missing ingredients.

[0303] Step 4:

[0304] The server generates alternatives for the missing ingredients using a generation AI model.

[0305] The generation model calculates available alternative ingredients based on the input information of the missing ingredients. The generated alternatives are sent to the terminal and displayed to the user. The input for this step is the list of missing ingredients, and the output is a list of alternatives.

[0306] Step 5:

[0307] The terminal sends a notification to prompt the user to obtain the missing ingredients.

[0308] The notification includes information on the alternatives and options for online ordering. This enables the user to have options to easily replenish the missing ingredients. The input for this step is the generated alternatives, and the output is the notification information to the user.

[0309] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0310] This invention relates to an AI system equipped with emotion recognition capabilities to support the cooking process. It measures the user's emotions and provides personalized suggestions and notifications accordingly, offering a more personalized cooking experience. The system analyzes recipes based on user-inputted ingredient information and purchase history, and uses an AI-generated model to suggest optimal alternatives. Furthermore, by combining this with an emotion engine, it recognizes the user's emotional state and provides suggestions tailored to those emotions.

[0311] Specifically, users input information about the ingredients and seasonings they have at home through the application. The device sends this data to a server, which stores it in a database. The server uses the collected data to retrieve the user's purchase history via an API and compares it with the ingredients stored in the database.

[0312] When a user selects a dish they want to make, the server analyzes the necessary ingredients and steps for that recipe. It identifies any missing ingredients, and an AI generative model generates alternatives. In addition, an emotion engine recognizes the user's current emotional state from their facial expressions and voice. Based on this information, the server adjusts the suggestions and sends an alternative that takes the user's emotions into consideration to the device. For example, if the recognized emotion is "anxiety," a simplified suggestion will be provided.

[0313] Furthermore, a feature has been added that adjusts the content and timing of notifications according to the user's emotional state. This adjustment optimizes the timing of purchasing necessary ingredients based on emotions; for example, if the user is feeling stressed, it will send purchase suggestions that take that situation into consideration.

[0314] By taking user emotions into consideration in this way, it becomes possible to provide a richer and more successful cooking experience and reduce food waste.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] Users input the ingredients and seasonings they have at home using an application or web interface. The entered information is sent from the terminal to the server, which stores this data in a database.

[0318] Step 2:

[0319] The server retrieves the user's purchase history via an API. By collecting past purchase data, it understands the user's purchasing patterns. This allows for the identification of frequently purchased food items and condiments. The server stores this data in a database.

[0320] Step 3:

[0321] The user selects a recipe for the dish they want to make. The device sends this information to the server, which retrieves the selected recipe from its database and analyzes the necessary ingredients and steps. It then compares this information with the user's available ingredients to identify any missing ingredients.

[0322] Step 4:

[0323] The server compares the ingredients it has on hand with the ingredients required for the recipe and activates an AI generation model based on the missing ingredients. The generation model devises alternatives to compensate for the missing ingredients and prepares these suggestions.

[0324] Step 5:

[0325] The emotion engine analyzes the user's facial expressions and voice on the device to recognize their current emotional state. The recognized emotional information is sent to a server and used to adjust the suggestions. The server generates emotionally sensitive suggestions and sends them to the device. For example, if the user is feeling anxious, a simplified suggestion will be provided.

[0326] Step 6:

[0327] The device displays suggestions to the user. The user then proceeds with cooking based on these suggestions. The suggestions are optimized for the user's situation and emotions.

[0328] Step 7:

[0329] The server adjusts the content and timing of purchase suggestion notifications based on the user's emotional state. The device provides these notifications to the user in real time, encouraging them to use them to help with their next grocery purchase. For example, purchase suggestions are adjusted to appear during times when the user is calm.

[0330] (Example 2)

[0331] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0332] Conventional meal preparation support systems fail to consider the user's emotional state when making suggestions, making it difficult to provide advice optimized for the user's needs in specific situations. Furthermore, suggestions based on available ingredients and purchase history are not adequately integrated, making efficient resource utilization difficult and resulting in waste.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0334] In this invention, the server includes means for collecting user resource information, means for acquiring and analyzing purchase history, means for analyzing necessary materials and comparing them with existing materials, means for recognizing the user's emotional state and adjusting the suggested content accordingly, and means for providing notifications to encourage the purchase of insufficient resources. This enables personalized suggestions that take the user's emotions into consideration, resulting in efficient resource utilization and an improved user experience.

[0335] "Resource information" refers to detailed information about ingredients and seasonings owned by the user, including attributes such as quantity and expiration date.

[0336] "Purchase history" refers to information about materials and items that a user has purchased in the past, and consists of data such as the date and time of purchase and the quantity.

[0337] An "alternative" is another option proposed to fill in the missing materials for the user, and it is optimized by a generative model.

[0338] A "generative model" is an algorithm that uses artificial intelligence technology to analyze data and automatically generate optimal suggestions and solutions.

[0339] "Emotional state" refers to the user's current psychological state, which is recognized in real time from facial expressions, voice, and other factors.

[0340] A "notification" is a message that conveys information or suggestions to a user, and its timing and content are adjusted to meet the user's needs.

[0341] This invention is a system for providing users with personalized cooking suggestions that take their emotions into consideration. This system combines ingredient information and emotion recognition data, and uses a generative AI model to present the optimal alternative. The system includes the following elements:

[0342] Users input information about ingredients and seasonings they have at home through the application. Specifically, they can use a smartphone or tablet to enter details such as type, quantity, and expiration date. The device then transmits this data to the server via the internet.

[0343] The server stores the received material information in a database and retrieves the user's purchase history via an API based on that information. As a software platform, an open-source database management system can be used for server-side data analysis. This data will be used for subsequent analysis.

[0344] When a user selects a dish they want to make, the server analyzes the plan and identifies key ingredients and processes. If necessary, it passes missing ingredients to a generative AI model, which then generates the optimal alternative. This generative AI model can utilize machine learning libraries developed in, for example, Python, and calculates the optimal solution in real time.

[0345] Furthermore, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and the server processes this data using an emotion engine to recognize the user's psychological state. Based on this information, the server generates suggestions tailored to the user's emotions and sends them to the device.

[0346] A concrete example of a prompt statement is, "I don't have much time today, but I want to make a proper dinner." In response to this prompt, the system can suggest a simple recipe that can be prepared in a short amount of time.

[0347] These features enable the system to offer suggestions that resonate with the user's emotions, improving both the efficiency of meal preparation and the user experience.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] Users enter information about ingredients and seasonings they have at home into a dedicated application on their device. This includes detailed information such as type, quantity, and expiration date. The entered data is transmitted from the device to a server via the internet. The server receives this data and records it in a database. This ensures that the system stores the most up-to-date information about the ingredients the user has on hand.

[0351] Step 2:

[0352] The server retrieves purchase history via an API based on the user's material information. The retrieved purchase history data is stored in a database and serves as foundational data for understanding the user's past purchasing patterns. The server compares the purchase history with the current material information to identify which materials are lacking.

[0353] Step 3:

[0354] The user selects the dish they want to make within the application. The server analyzes this selected recipe information to identify the necessary ingredients and steps. Based on this analysis, any missing ingredients are identified and passed to a generative AI model. This model calculates the optimal alternative ingredients based on past data and the current situation, and generates suggestions.

[0355] Step 4:

[0356] The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. The captured data is sent to a server, where an emotion engine analyzes it. The server understands the user's emotional state in real time and initiates a process to adjust the suggestions based on that information.

[0357] Step 5:

[0358] Based on the sentiment analysis results, the server adjusts the alternatives generated by the generative AI model to create suggestions that best suit the user's emotions. For example, if the user is expressing anxiety, a simplified recipe will be suggested. The adjusted suggestions are sent to the device, and the user can receive the information through the application.

[0359] Step 6:

[0360] The server optimizes not only suggestions but also the content and timing of notifications based on the user's emotions. If a user is feeling stressed, it adjusts product suggestions and notifications to better promote relaxation. The device receives this information and presents it to the user at the appropriate time.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] In today's busy daily lives, there is a lack of cooking support tailored to individual emotional states, and cooking often becomes a source of stress. Therefore, it is necessary to provide a personalized cooking experience that responds to the user's emotions, thereby improving the success rate of cooking and reducing the purchase and use of unnecessary ingredients.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for acquiring information about ingredients entered by the user, means for acquiring and analyzing purchase history, means for analyzing information about recipes including necessary ingredients and processes and comparing them with the ingredients owned, means for using a generative model to generate and suggest alternatives based on missing ingredients, means for reading the user's emotional state and utilizing emotion recognition to make adjustment suggestions, and means for providing music and visuals based on the learner's current emotional state. This makes it possible to provide optimal cooking support according to the user's emotional state.

[0366] "Means for acquiring ingredient information" refers to a function that collects data about the ingredients used in the dishes that the user prepares.

[0367] "Means for acquiring and analyzing purchase history" refers to a function that acquires and analyzes a user's past purchase data.

[0368] "A means of analyzing information about a recipe and comparing it to the ingredients you have" refers to a function that analyzes the details of a provided recipe and compares them to the ingredients you have on hand.

[0369] "Means of using generative models to generate and propose alternatives" refers to a function that uses AI models to generate alternative options for the necessary materials and proposes them to the user.

[0370] "A means of utilizing emotion recognition to read emotional states and make adjustment suggestions" refers to a function that analyzes the user's facial expressions and voice to recognize emotions and then makes suggestions based on those emotions.

[0371] "A means of providing music and visuals based on the participant's current emotional state" refers to a function that provides users with appropriate music and visual support based on the results of emotional analysis.

[0372] To realize this invention, the system is configured as a user terminal and a cloud server. The user terminal is a device such as a smartphone or smart glasses, which collects data through its camera and microphone and analyzes emotions in real time using an emotion recognition API. Microsoft's Azure Face API and Google Cloud Speech-to-Text API are suitable tools for emotion recognition.

[0373] The server processes large amounts of data and stores the ingredient information entered by the user in a database. It also retrieves purchase history and compares the ingredients the user owns with the provided recipes. Using an AI generative model (such as OpenAI's GPT-3), it generates alternatives for missing ingredients.

[0374] If the user's emotional state is "specific," the server recognizes that state and sends a customized meal suggestion to the terminal. Furthermore, it provides multimedia content such as music and visual support; for example, if the user's emotion is anxiety, it plays relaxing music.

[0375] As a concrete example, let's say a user is working on a project to bake bread. If the user is feeling anxious, the system can provide support by simplifying suggestions and displaying a specific music playlist on their device, thus creating a sense of reassurance. In this way, the probability of success in the cooking process can be significantly increased.

[0376] An example of a prompt message is: "If the user is anxious, generate support suggestions to simplify the task and play relaxing music."

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The user inputs ingredient information into the terminal. The terminal sends this information to the cloud server. The input includes ingredient name, quantity, type, etc., and this information is formatted and output as data to be recorded in the database.

[0380] Step 2:

[0381] The server retrieves the user's purchase history data via an API. The retrieved data includes past purchase dates and items, which are then analyzed and compiled into a list to compare with the user's current food inventory.

[0382] Step 3:

[0383] The server uses the entered ingredient information and purchase history to check the ingredients required for the recipe selected by the user and compares them with the user's owned ingredients. This comparison reveals any missing ingredients, which are then compiled into a list.

[0384] Step 4:

[0385] The server generates alternatives using a generative AI model for missing ingredients. The input is a list of missing ingredients, and the alternatives are output as substitutes for ingredients or modified recipes. The generation process is set up using prompts.

[0386] Step 5:

[0387] The device collects the user's emotional state through its camera and microphone and analyzes it using an emotion recognition API. Based on this analysis, the device sends the user's emotional data to the server. The output is data on the emotional state.

[0388] Step 6:

[0389] The server considers emotional data and alternatives, and sends recipe suggestions and simplified procedures tailored to the user's emotions to the device. Relaxing music and visual materials are also added as support information, depending on the emotional state.

[0390] 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.

[0391] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0397] 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.

[0398] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0399] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0400] 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.

[0401] 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.

[0402] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0403] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0404] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0405] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0406] This invention is an AI system for successful cooking, providing a means for effectively utilizing the ingredients and seasonings a user possesses. The system analyzes information about the ingredients and seasonings the user has at home, along with past purchase history. As a result, it identifies missing ingredients and provides alternative suggestions using an AI-generated model. It also provides notifications to encourage the purchase of missing ingredients.

[0407] Specifically, users input information about the ingredients and condiments they have at home through an application or web interface. This includes a function to record what's in their refrigerator and cupboards. The terminal sends the entered data to a server, which stores it in a database.

[0408] Next, the server retrieves the user's purchase history via an API. This allows for the collection of history, including previously purchased food items, in a digital format. Through this, the server identifies seasonings used in the past and frequently purchased ingredients, and analyzes the user's purchasing patterns.

[0409] The user selects a recipe for a dish they want to make, and this information is sent to the server. The server analyzes the required ingredients for the recipe and compares them to the ingredients the user has on hand. At this point, any missing ingredients are identified, and an AI generation model is activated. This model devises alternatives to supplement the missing ingredients and displays them as suggestions on the device.

[0410] For example, if a user chooses to make teriyaki chicken but is short on soy sauce, a key seasoning, the AI ​​model will generate a suggestion such as "substitute with mirin and salt to adjust the flavor." Furthermore, it will notify the user to add soy sauce to their shopping list so that it will be helpful when they purchase ingredients next time.

[0411] Thus, this system aims to help users succeed in cooking without making mistakes and to reduce food waste. It also allows for further improvement of the user experience through a feedback function, enabling the system to continuously learn and improve.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] Users input information about the ingredients and seasonings they have at home using an application or web interface. Once the user has finished inputting the information, the device sends this information to the server. The server stores the received information in a database and records it as the user's available ingredients.

[0415] Step 2:

[0416] The server accesses an external purchase history API to retrieve purchase history. Here, it obtains the user's past purchase history and collects purchase data related to ingredients and seasonings. The server analyzes this purchase history data to identify what the user frequently buys, which ingredients are currently out of stock, and stores this information in a database.

[0417] Step 3:

[0418] The user selects a recipe for the dish they want to make within the application. The selection information is sent from the terminal to the server. The server retrieves the details of the selected recipe from its database and analyzes the ingredients and steps required for the dish. A list of required ingredients is created and compared with the user's available ingredients.

[0419] Step 4:

[0420] The server compares the ingredients the user has with the ingredients required for the recipe and identifies any missing ingredients. Based on the identified missing ingredients, an AI generation model is activated to generate alternatives for modifying the recipe using the other ingredients the user has.

[0421] Step 5:

[0422] The generated alternatives are sent to the device and displayed to the user. The user reviews this information and uses it as a guide to proceed with cooking. For example, if soy sauce is in short supply, the AI ​​model might suggest using mirin and salt as substitutes.

[0423] Step 6:

[0424] The server generates purchase suggestions for missing materials. Using the user's purchase history and current material shortage information, the server generates a notification of materials to be purchased in the next shopping trip. The terminal sends this notification to the user in real time, encouraging them to use it as a shopping list.

[0425] Thus, the system's primary focus is on supporting the user's cooking experience and preventing mistakes.

[0426] (Example 1)

[0427] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0428] In modern households, it is often difficult to effectively utilize the ingredients available when cooking, leading to frequent shortages of ingredients and hindering smooth cooking planning. Furthermore, reducing food waste and providing appropriate alternatives for efficient cooking are crucial challenges. Additionally, there is a need to address the lack of opportunities to create ingredient shopping lists based on user purchasing trends and to learn about the proper use of ingredients.

[0429] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0430] In this invention, the server includes means for collecting food information entered by the user, means for acquiring and analyzing purchase history, and means for analyzing cooking information including necessary ingredients and preparation processes, and comparing it with the ingredients on hand. This makes it possible to identify missing ingredients and generate alternative suggestions for effectively utilizing ingredients using a generation system. Furthermore, by analyzing the user's purchasing trends and adding missing ingredients to the purchase list, it is possible to provide information that will be useful for future purchases.

[0431] "Food information" refers to data entered by users about ingredients and seasonings they have at home, and is recorded through barcodes or manual input.

[0432] "Purchase history" refers to a record of products and food items that a user has purchased in the past, and is digital data obtained through online platforms and integration with loyalty cards.

[0433] "Cooking information" refers to data that includes the necessary ingredients and steps for the dish you want to make, and describes the details of a recipe that can be searched or selected within the application.

[0434] A "generation system" refers to a program that utilizes AI technology to provide alternative options for insufficient materials, presenting users with alternative solutions.

[0435] An "alternative" refers to an alternative material or means suggested to compensate for materials that the user does not have on hand, and is a choice provided by the generation system.

[0436] The "information display section" refers to an interface for instantly displaying suggestions and notifications to the user, and can be an application or web screen.

[0437] A description of the embodiment for carrying out the invention will be provided.

[0438] This invention is a system that enables users to effectively utilize information about ingredients and seasonings they have at home to ensure successful cooking. Users input food information via devices such as smartphones and personal computers. Specifically, they use barcode scanners or manual input to record ingredients and seasonings in their refrigerators and cupboards as a digital list.

[0439] The terminal transmits the entered food information to the server in real time. The server stores this information in a database and manages it as food information organized for each user.

[0440] The server uses an API to retrieve the user's purchase history. This history data includes online shopping history and loyalty card purchase history, which the server analyzes to understand the user's purchasing patterns. For example, it can deduce trends in frequently purchased food items and condiments used in the past.

[0441] The user selects a recipe for the dish they want to make within the application. This recipe contains the necessary ingredients and cooking steps, and the server analyzes this information based on the user's selection.

[0442] The server compares the ingredients of the analyzed recipe with the user's available ingredients to identify any missing ingredients. It then activates a generative AI model to generate alternatives for the missing ingredients. For example, if a user is making teriyaki chicken and lacks soy sauce, the AI ​​might suggest substituting mirin and salt to adjust the flavor.

[0443] Furthermore, the server generates a notification to add any missing ingredients to the next shopping list. This notification is displayed on the device to help the user remember to buy the ingredients on their next shopping trip.

[0444] As a concrete example, a user can input the following prompt into the AI ​​model to obtain more specific suggestions:

[0445] "What can we make for dinner tonight using the ingredients we have in our refrigerator?"

[0446] In this way, this system helps users effectively utilize the ingredients they have, reduces food waste, and supports them in successfully preparing meals.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] Users input information about food items they have at home. Using their smartphones or PCs, users enter information about ingredients and condiments in their refrigerators and cupboards into the app. Barcode scanning and text forms are used for input. The entered information includes data such as "ingredient name," "quantity," and "expiration date."

[0450] Step 2:

[0451] The terminal sends the entered food information to the server. The terminal uses a network connection to send the entered data to the server in real time. The server analyzes the received data and records it in a food information database. This organizes the food information for each user.

[0452] Step 3:

[0453] The server retrieves purchase history data. Through an API, the server collects the user's past purchase history data. This data, obtained from online platforms and loyalty card systems, includes information such as "purchase date," "item name," and "price." The server analyzes this data to model the user's purchasing patterns.

[0454] Step 4:

[0455] The user selects the recipe they want to make. The user chooses a dish of interest from a list of recipes provided within the app. The selected recipe is sent to the server along with the necessary ingredients and cooking instructions. The recipe data includes "ingredient names," "quantities," and "instructions."

[0456] Step 5:

[0457] The server compares the recipe with the food information on hand. The server analyzes the required ingredients in the recipe and compares them with the food information entered by the user. Here, the server identifies any missing ingredients and lists them. As a result, a list of missing ingredients is output.

[0458] Step 6:

[0459] The server activates a generative AI model to generate alternatives. The server passes a list of missing ingredients to the generative AI model, which then devises alternatives to address the shortages. For example, it might generate a suggestion such as, "If soy sauce is unavailable, substitute with mirin and salt." This suggestion includes the alternative ingredients and how to use them.

[0460] Step 7:

[0461] The server sends suggestions and notifications to the device. The generated alternatives and lists of missing ingredients are delivered to the device. The device displays the suggestions to the user on the screen and notifies them to add any missing ingredients to their shopping list. The user can review this and use it to help with their next shopping trip or cooking.

[0462] (Application Example 1)

[0463] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] Modern consumers are busy, yet there is a growing demand for efficient meal preparation at home. However, a lack of knowledge about what ingredients are available, the effort required to purchase missing ingredients, and alternative ingredients can increase the time and effort involved in cooking. This can result in food waste, cooking failures, and wasted time.

[0465] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0466] In this invention, the server includes means for collecting food information entered by the user, means for acquiring and analyzing purchase history, and means including a communication device that allows the user to pre-order any missing food items. This allows the user to efficiently understand what ingredients they have on hand and easily obtain alternatives for missing ingredients, thereby reducing the effort and potential failures involved in cooking.

[0467] A "user" is someone who uses the system to input food information and receives necessary ingredients or alternative suggestions.

[0468] "Food information" refers to data about ingredients and seasonings owned by the user, and is an important element in cooking preparation.

[0469] "Purchase history" refers to a record of food and condiments that a user has purchased in the past, and is information used to analyze the user's purchasing patterns.

[0470] "Processing information" refers to detailed data about the ingredients and steps required to prepare a dish.

[0471] A "generative model" is a system that utilizes artificial intelligence technology to create alternative solutions for missing materials and present them to the user.

[0472] A "communication device" is a device that enables the transmission and reception of data via an external network, and is used for processing orders and sending notifications.

[0473] A "communication screen" is an interface used to display suggestions and notifications to the user in real time.

[0474] This invention is a system that efficiently supports cooking preparation based on food information input by the user. The server collects food information, retrieves and analyzes purchase history. This allows the system to compare the food items the user owns with the required ingredients based on the selected recipe and identify any missing ingredients.

[0475] The server uses a generative model to create alternatives for the food items that are in short supply. This generative AI model presents multiple usable alternatives for the missing ingredients. Users can review these alternatives via their terminal and, if necessary, order the missing food items in advance through a communication device. Furthermore, a notification system can be used to send real-time notifications to users regarding food shortages, prompting them to procure the items.

[0476] This invention is programmed using Python and the requests library. Through a series of processes including data collection and analysis, generation of alternatives using generative models, and transmission and reception of data using communication devices, users can easily gather the necessary materials.

[0477] For example, if a user wants to make a pizza and has cheese and bell peppers in the refrigerator but no tomato sauce, the system can suggest alternatives such as "substitute with ketchup and basil." An example of a prompt message could be in the format of "Current ingredient list: cheese, bell peppers. What ingredients are missing to make the pizza?" This enables efficient ingredient management and purchasing simply by entering the necessary information.

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The user uses a device to input information about the food items they have on hand.

[0481] The entered information includes the names and quantities of ingredients and seasonings. The terminal sends this information to the server, which stores it in a database. The input in this step is the food information provided by the user, and the output is the data stored on the server.

[0482] Step 2:

[0483] The server retrieves the user's purchase history from an external database.

[0484] The acquired purchase history data includes information about previously purchased foods and their frequency. The server analyzes this information to identify the user's purchasing patterns. The input for this step is the purchase history retrieved from an external database, and the output is the analyzed purchasing pattern information.

[0485] Step 3:

[0486] The user selects the recipe for the dish they want to make on their device.

[0487] The recipe information is sent to the server, which performs analysis. The ingredients required for the recipe are compared with the user's available food information, and any missing ingredients are identified. The input for this step is the recipe information selected by the user, and the output is a list of missing ingredients.

[0488] Step 4:

[0489] The server uses a generative AI model to generate alternative solutions for any missing materials.

[0490] The generative model calculates available alternative ingredients based on the input information about the missing ingredients. The generated alternatives are sent to the terminal and displayed to the user. The input for this step is a list of missing ingredients, and the output is a list of alternatives.

[0491] Step 5:

[0492] The device sends a notification to the user prompting them to acquire any missing materials.

[0493] The notification includes information on alternatives and options for ordering online. This gives users an easy way to supplement missing ingredients. The input for this step is the generated alternatives, and the output is the notification information for the user.

[0494] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0495] This invention relates to an AI system equipped with emotion recognition capabilities to support the cooking process. It measures the user's emotions and provides personalized suggestions and notifications accordingly, offering a more personalized cooking experience. The system analyzes recipes based on user-inputted ingredient information and purchase history, and uses an AI-generated model to suggest optimal alternatives. Furthermore, by combining this with an emotion engine, it recognizes the user's emotional state and provides suggestions tailored to those emotions.

[0496] Specifically, users input information about the ingredients and seasonings they have at home through the application. The device sends this data to a server, which stores it in a database. The server uses the collected data to retrieve the user's purchase history via an API and compares it with the ingredients stored in the database.

[0497] When a user selects a dish they want to make, the server analyzes the necessary ingredients and steps for that recipe. It identifies any missing ingredients, and an AI generative model generates alternatives. In addition, an emotion engine recognizes the user's current emotional state from their facial expressions and voice. Based on this information, the server adjusts the suggestions and sends an alternative that takes the user's emotions into consideration to the device. For example, if the recognized emotion is "anxiety," a simplified suggestion will be provided.

[0498] Furthermore, a feature has been added that adjusts the content and timing of notifications according to the user's emotional state. This adjustment optimizes the timing of purchasing necessary ingredients based on emotions; for example, if the user is feeling stressed, it will send purchase suggestions that take that situation into consideration.

[0499] By taking user emotions into consideration in this way, it becomes possible to provide a richer and more successful cooking experience and reduce food waste.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] Users input the ingredients and seasonings they have at home using an application or web interface. The entered information is sent from the terminal to the server, which stores this data in a database.

[0503] Step 2:

[0504] The server retrieves the user's purchase history via an API. By collecting past purchase data, it understands the user's purchasing patterns. This allows for the identification of frequently purchased food items and condiments. The server stores this data in a database.

[0505] Step 3:

[0506] The user selects a recipe for the dish they want to make. The device sends this information to the server, which retrieves the selected recipe from its database and analyzes the necessary ingredients and steps. It then compares this information with the user's available ingredients to identify any missing ingredients.

[0507] Step 4:

[0508] The server compares the ingredients it has on hand with the ingredients required for the recipe and activates an AI generation model based on the missing ingredients. The generation model devises alternatives to compensate for the missing ingredients and prepares these suggestions.

[0509] Step 5:

[0510] The emotion engine analyzes the user's facial expressions and voice on the device to recognize their current emotional state. The recognized emotional information is sent to a server and used to adjust the suggestions. The server generates emotionally sensitive suggestions and sends them to the device. For example, if the user is feeling anxious, a simplified suggestion will be provided.

[0511] Step 6:

[0512] The device displays suggestions to the user. The user then proceeds with cooking based on these suggestions. The suggestions are optimized for the user's situation and emotions.

[0513] Step 7:

[0514] The server adjusts the content and timing of purchase suggestion notifications based on the user's emotional state. The device provides these notifications to the user in real time, encouraging them to use them to help with their next grocery purchase. For example, purchase suggestions are adjusted to appear during times when the user is calm.

[0515] (Example 2)

[0516] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0517] Conventional meal preparation support systems fail to consider the user's emotional state when making suggestions, making it difficult to provide advice optimized for the user's needs in specific situations. Furthermore, suggestions based on available ingredients and purchase history are not adequately integrated, making efficient resource utilization difficult and resulting in waste.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0519] In this invention, the server includes means for collecting user resource information, means for acquiring and analyzing purchase history, means for analyzing necessary materials and comparing them with existing materials, means for recognizing the user's emotional state and adjusting the suggested content accordingly, and means for providing notifications to encourage the purchase of insufficient resources. This enables personalized suggestions that take the user's emotions into consideration, resulting in efficient resource utilization and an improved user experience.

[0520] "Resource information" refers to detailed information about ingredients and seasonings owned by the user, including attributes such as quantity and expiration date.

[0521] "Purchase history" refers to information about materials and items that a user has purchased in the past, and consists of data such as the date and time of purchase and the quantity.

[0522] An "alternative" is another option proposed to fill in the missing materials for the user, and it is optimized by a generative model.

[0523] A "generative model" is an algorithm that uses artificial intelligence technology to analyze data and automatically generate optimal suggestions and solutions.

[0524] "Emotional state" refers to the user's current psychological state, which is recognized in real time from facial expressions, voice, and other factors.

[0525] A "notification" is a message that conveys information or suggestions to a user, and its timing and content are adjusted to meet the user's needs.

[0526] This invention is a system for providing users with personalized cooking suggestions that take their emotions into consideration. This system combines ingredient information and emotion recognition data, and uses a generative AI model to present the optimal alternative. The system includes the following elements:

[0527] Users input information about ingredients and seasonings they have at home through the application. Specifically, they can use a smartphone or tablet to enter details such as type, quantity, and expiration date. The device then transmits this data to the server via the internet.

[0528] The server stores the received material information in a database and retrieves the user's purchase history via an API based on that information. As a software platform, an open-source database management system can be used for server-side data analysis. This data will be used for subsequent analysis.

[0529] When a user selects a dish they want to make, the server analyzes the plan and identifies key ingredients and processes. If necessary, it passes missing ingredients to a generative AI model, which then generates the optimal alternative. This generative AI model can utilize machine learning libraries developed in, for example, Python, and calculates the optimal solution in real time.

[0530] Furthermore, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and the server processes this data using an emotion engine to recognize the user's psychological state. Based on this information, the server generates suggestions tailored to the user's emotions and sends them to the device.

[0531] A concrete example of a prompt statement is, "I don't have much time today, but I want to make a proper dinner." In response to this prompt, the system can suggest a simple recipe that can be prepared in a short amount of time.

[0532] These features enable the system to offer suggestions that resonate with the user's emotions, improving both the efficiency of meal preparation and the user experience.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] Users enter information about ingredients and seasonings they have at home into a dedicated application on their device. This includes detailed information such as type, quantity, and expiration date. The entered data is transmitted from the device to a server via the internet. The server receives this data and records it in a database. This ensures that the system stores the most up-to-date information about the ingredients the user has on hand.

[0536] Step 2:

[0537] The server retrieves purchase history via an API based on the user's material information. The retrieved purchase history data is stored in a database and serves as foundational data for understanding the user's past purchasing patterns. The server compares the purchase history with the current material information to identify which materials are lacking.

[0538] Step 3:

[0539] The user selects the dish they want to make within the application. The server analyzes this selected recipe information to identify the necessary ingredients and steps. Based on this analysis, any missing ingredients are identified and passed to a generative AI model. This model calculates the optimal alternative ingredients based on past data and the current situation, and generates suggestions.

[0540] Step 4:

[0541] The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. The captured data is sent to a server, where an emotion engine analyzes it. The server understands the user's emotional state in real time and initiates a process to adjust the suggestions based on that information.

[0542] Step 5:

[0543] Based on the sentiment analysis results, the server adjusts the alternatives generated by the generative AI model to create suggestions that best suit the user's emotions. For example, if the user is expressing anxiety, a simplified recipe will be suggested. The adjusted suggestions are sent to the device, and the user can receive the information through the application.

[0544] Step 6:

[0545] The server optimizes not only suggestions but also the content and timing of notifications based on the user's emotions. If a user is feeling stressed, it adjusts product suggestions and notifications to better promote relaxation. The device receives this information and presents it to the user at the appropriate time.

[0546] (Application Example 2)

[0547] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] In today's busy daily lives, there is a lack of cooking support tailored to individual emotional states, and cooking often becomes a source of stress. Therefore, it is necessary to provide a personalized cooking experience that responds to the user's emotions, thereby improving the success rate of cooking and reducing the purchase and use of unnecessary ingredients.

[0549] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0550] In this invention, the server includes means for acquiring information about ingredients entered by the user, means for acquiring and analyzing purchase history, means for analyzing information about recipes including necessary ingredients and processes and comparing them with the ingredients owned, means for using a generative model to generate and suggest alternatives based on missing ingredients, means for reading the user's emotional state and utilizing emotion recognition to make adjustment suggestions, and means for providing music and visuals based on the learner's current emotional state. This makes it possible to provide optimal cooking support according to the user's emotional state.

[0551] "Means for acquiring ingredient information" refers to a function that collects data about the ingredients used in the dishes that the user prepares.

[0552] "Means for acquiring and analyzing purchase history" refers to a function that acquires and analyzes a user's past purchase data.

[0553] "A means of analyzing information about a recipe and comparing it to the ingredients you have" refers to a function that analyzes the details of a provided recipe and compares them to the ingredients you have on hand.

[0554] "Means of using generative models to generate and propose alternatives" refers to a function that uses AI models to generate alternative options for the necessary materials and proposes them to the user.

[0555] "A means of utilizing emotion recognition to read emotional states and make adjustment suggestions" refers to a function that analyzes the user's facial expressions and voice to recognize emotions and then makes suggestions based on those emotions.

[0556] "A means of providing music and visuals based on the participant's current emotional state" refers to a function that provides users with appropriate music and visual support based on the results of emotional analysis.

[0557] To realize this invention, the system is configured as a user terminal and a cloud server. The user terminal is a device such as a smartphone or smart glasses, which collects data through its camera and microphone and analyzes emotions in real time using an emotion recognition API. Microsoft's Azure Face API and Google Cloud Speech-to-Text API are suitable tools for emotion recognition.

[0558] The server processes large amounts of data and stores the ingredient information entered by the user in a database. It also retrieves purchase history and compares the ingredients the user owns with the provided recipes. Using an AI generative model (such as OpenAI's GPT-3), it generates alternatives for missing ingredients.

[0559] If the user's emotional state is "specific," the server recognizes that state and sends a customized meal suggestion to the terminal. Furthermore, it provides multimedia content such as music and visual support; for example, if the user's emotion is anxiety, it plays relaxing music.

[0560] As a concrete example, let's say a user is working on a project to bake bread. If the user is feeling anxious, the system can provide support by simplifying suggestions and displaying a specific music playlist on their device, thus creating a sense of reassurance. In this way, the probability of success in the cooking process can be significantly increased.

[0561] An example of a prompt message is: "If the user is anxious, generate support suggestions to simplify the task and play relaxing music."

[0562] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0563] Step 1:

[0564] The user inputs ingredient information into the terminal. The terminal sends this information to the cloud server. The input includes ingredient name, quantity, type, etc., and this information is formatted and output as data to be recorded in the database.

[0565] Step 2:

[0566] The server retrieves the user's purchase history data via an API. The retrieved data includes past purchase dates and items, which are then analyzed and compiled into a list to compare with the user's current food inventory.

[0567] Step 3:

[0568] The server uses the entered ingredient information and purchase history to check the ingredients required for the recipe selected by the user and compares them with the user's owned ingredients. This comparison reveals any missing ingredients, which are then compiled into a list.

[0569] Step 4:

[0570] The server generates alternatives using a generative AI model for missing ingredients. The input is a list of missing ingredients, and the alternatives are output as substitutes for ingredients or modified recipes. The generation process is set up using prompts.

[0571] Step 5:

[0572] The device collects the user's emotional state through its camera and microphone and analyzes it using an emotion recognition API. Based on this analysis, the device sends the user's emotional data to the server. The output is data on the emotional state.

[0573] Step 6:

[0574] The server considers emotional data and alternatives, and sends recipe suggestions and simplified procedures tailored to the user's emotions to the device. Relaxing music and visual materials are also added as support information, depending on the emotional state.

[0575] 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.

[0576] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0577] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0578] [Fourth Embodiment]

[0579] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0580] 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.

[0581] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0582] 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.

[0583] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0584] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0585] 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.

[0586] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0587] 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.

[0588] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0589] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0590] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0591] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0592] This invention is an AI system for successful cooking, providing a means for effectively utilizing the ingredients and seasonings a user possesses. The system analyzes information about the ingredients and seasonings the user has at home, along with past purchase history. As a result, it identifies missing ingredients and provides alternative suggestions using an AI-generated model. It also provides notifications to encourage the purchase of missing ingredients.

[0593] Specifically, users input information about the ingredients and condiments they have at home through an application or web interface. This includes a function to record what's in their refrigerator and cupboards. The terminal sends the entered data to a server, which stores it in a database.

[0594] Next, the server retrieves the user's purchase history via an API. This allows for the collection of history, including previously purchased food items, in a digital format. Through this, the server identifies seasonings used in the past and frequently purchased ingredients, and analyzes the user's purchasing patterns.

[0595] The user selects a recipe for a dish they want to make, and this information is sent to the server. The server analyzes the required ingredients for the recipe and compares them to the ingredients the user has on hand. At this point, any missing ingredients are identified, and an AI generation model is activated. This model devises alternatives to supplement the missing ingredients and displays them as suggestions on the device.

[0596] For example, if a user chooses to make teriyaki chicken but is short on soy sauce, a key seasoning, the AI ​​model will generate a suggestion such as "substitute with mirin and salt to adjust the flavor." Furthermore, it will notify the user to add soy sauce to their shopping list so that it will be helpful when they purchase ingredients next time.

[0597] Thus, this system aims to help users succeed in cooking without making mistakes and to reduce food waste. It also allows for further improvement of the user experience through a feedback function, enabling the system to continuously learn and improve.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] Users input information about the ingredients and seasonings they have at home using an application or web interface. Once the user has finished inputting, the device sends this information to the server. The server stores the received information in a database and records it as the user's available ingredients.

[0601] Step 2:

[0602] The server accesses an external purchase history API to retrieve purchase history. Here, it obtains the user's past purchase history and collects purchase data related to ingredients and seasonings. The server analyzes this purchase history data to identify what the user frequently buys, which ingredients are currently out of stock, and stores this information in a database.

[0603] Step 3:

[0604] The user selects a recipe for the dish they want to make within the application. The selection information is sent from the terminal to the server. The server retrieves the details of the selected recipe from its database and analyzes the ingredients and steps required for the dish. A list of required ingredients is created and compared with the user's available ingredients.

[0605] Step 4:

[0606] The server compares the ingredients the user has with the ingredients required for the recipe and identifies any missing ingredients. Based on the identified missing ingredients, an AI generation model is activated to generate alternatives for modifying the recipe using the other ingredients the user has.

[0607] Step 5:

[0608] The generated alternatives are sent to the device and displayed to the user. The user reviews this information and uses it as a guide to proceed with cooking. For example, if soy sauce is in short supply, the AI ​​model might suggest using mirin and salt as substitutes.

[0609] Step 6:

[0610] The server generates purchase suggestions for missing materials. Using the user's purchase history and current material shortage information, the server generates a notification of materials to be purchased in the next shopping trip. The terminal sends this notification to the user in real time, encouraging them to use it as a shopping list.

[0611] Thus, the system's primary focus is on supporting the user's cooking experience and preventing mistakes.

[0612] (Example 1)

[0613] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0614] In modern households, it is often difficult to effectively utilize the ingredients available when cooking, leading to frequent shortages of ingredients and hindering smooth cooking planning. Furthermore, reducing food waste and providing appropriate alternatives for efficient cooking are crucial challenges. Additionally, there is a need to address the lack of opportunities to create ingredient shopping lists based on user purchasing trends and to learn about the proper use of ingredients.

[0615] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0616] In this invention, the server includes means for collecting food information entered by the user, means for acquiring and analyzing purchase history, and means for analyzing cooking information including necessary ingredients and preparation processes, and comparing it with the ingredients on hand. This makes it possible to identify missing ingredients and generate alternative suggestions for effectively utilizing ingredients using a generation system. Furthermore, by analyzing the user's purchasing trends and adding missing ingredients to the purchase list, it is possible to provide information that will be useful for future purchases.

[0617] "Food information" refers to data entered by users about ingredients and seasonings they have at home, and is recorded through barcodes or manual input.

[0618] "Purchase history" refers to a record of products and food items that a user has purchased in the past, and is digital data obtained through online platforms and integration with loyalty cards.

[0619] "Cooking information" refers to data that includes the necessary ingredients and steps for the dish you want to make, and describes the details of a recipe that can be searched or selected within the application.

[0620] A "generation system" refers to a program that utilizes AI technology to provide alternative options for insufficient materials, presenting users with alternative solutions.

[0621] An "alternative" refers to an alternative material or means suggested to compensate for materials that the user does not have on hand, and is a choice provided by the generation system.

[0622] The "information display section" refers to an interface for instantly displaying suggestions and notifications to the user, and can be an application or web screen.

[0623] A description of the embodiment for carrying out the invention will be provided.

[0624] This invention is a system that enables users to effectively utilize information about ingredients and seasonings they have at home to ensure successful cooking. Users input food information via devices such as smartphones and personal computers. Specifically, they use barcode scanners or manual input to record ingredients and seasonings in their refrigerators and cupboards as a digital list.

[0625] The terminal transmits the entered food information to the server in real time. The server stores this information in a database and manages it as food information organized for each user.

[0626] The server uses an API to retrieve the user's purchase history. This history data includes online shopping history and loyalty card purchase history, which the server analyzes to understand the user's purchasing patterns. For example, it can deduce trends in frequently purchased food items and condiments used in the past.

[0627] The user selects a recipe for the dish they want to make within the application. This recipe contains the necessary ingredients and cooking steps, and the server analyzes this information based on the user's selection.

[0628] The server compares the ingredients of the analyzed recipe with the user's available ingredients to identify any missing ingredients. It then activates a generative AI model to generate alternatives for the missing ingredients. For example, if a user is making teriyaki chicken and lacks soy sauce, the AI ​​might suggest substituting mirin and salt to adjust the flavor.

[0629] Furthermore, the server generates a notification to add any missing ingredients to the next shopping list. This notification is displayed on the device to help the user remember to buy the ingredients on their next shopping trip.

[0630] As a concrete example, a user can input the following prompt into the AI ​​model to obtain more specific suggestions:

[0631] "What can we make for dinner tonight using the ingredients we have in our refrigerator?"

[0632] In this way, this system helps users effectively utilize the ingredients they have, reduces food waste, and supports them in successfully preparing meals.

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] Users input information about food items they have at home. Using their smartphones or PCs, users enter information about ingredients and condiments in their refrigerators and cupboards into the app. Barcode scanning and text forms are used for input. The entered information includes data such as "ingredient name," "quantity," and "expiration date."

[0636] Step 2:

[0637] The terminal sends the entered food information to the server. The terminal uses a network connection to send the entered data to the server in real time. The server analyzes the received data and records it in a food information database. This organizes the food information for each user.

[0638] Step 3:

[0639] The server retrieves purchase history data. Through an API, the server collects the user's past purchase history data. This data, obtained from online platforms and loyalty card systems, includes information such as "purchase date," "item name," and "price." The server analyzes this data to model the user's purchasing patterns.

[0640] Step 4:

[0641] The user selects the recipe they want to make. The user chooses a dish of interest from a list of recipes provided within the app. The selected recipe is sent to the server along with the necessary ingredients and cooking instructions. The recipe data includes "ingredient names," "quantities," and "instructions."

[0642] Step 5:

[0643] The server compares the recipe with the food information on hand. The server analyzes the required ingredients in the recipe and compares them with the food information entered by the user. Here, the server identifies any missing ingredients and lists them. As a result, a list of missing ingredients is output.

[0644] Step 6:

[0645] The server activates a generative AI model to generate alternatives. The server passes a list of missing ingredients to the generative AI model, which then devises alternatives to address the shortages. For example, it might generate a suggestion such as, "If soy sauce is unavailable, substitute with mirin and salt." This suggestion includes the alternative ingredients and how to use them.

[0646] Step 7:

[0647] The server sends suggestions and notifications to the device. The generated alternatives and lists of missing ingredients are delivered to the device. The device displays the suggestions to the user on the screen and notifies them to add any missing ingredients to their shopping list. The user can review this and use it to help with their next shopping trip or cooking.

[0648] (Application Example 1)

[0649] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] Modern consumers are busy, yet there is a growing demand for efficient meal preparation at home. However, a lack of knowledge about what ingredients are available, the effort required to purchase missing ingredients, and alternative ingredients can increase the time and effort involved in cooking. This can result in food waste, cooking failures, and wasted time.

[0651] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0652] In this invention, the server includes means for collecting food information entered by the user, means for acquiring and analyzing purchase history, and means including a communication device that allows the user to pre-order any missing food items. This allows the user to efficiently understand what ingredients they have on hand and easily obtain alternatives for missing ingredients, thereby reducing the effort and potential failures involved in cooking.

[0653] A "user" is someone who uses the system to input food information and receives necessary ingredients or alternative suggestions.

[0654] "Food information" refers to data about ingredients and seasonings owned by the user, and is an important element in cooking preparation.

[0655] "Purchase history" refers to a record of food and condiments that a user has purchased in the past, and is information used to analyze the user's purchasing patterns.

[0656] "Processing information" refers to detailed data about the ingredients and steps required to prepare a dish.

[0657] A "generative model" is a system that utilizes artificial intelligence technology to create alternative solutions for missing materials and present them to the user.

[0658] A "communication device" is a device that enables the transmission and reception of data via an external network, and is used for processing orders and sending notifications.

[0659] A "communication screen" is an interface used to display suggestions and notifications to the user in real time.

[0660] This invention is a system that efficiently supports cooking preparation based on food information input by the user. The server collects food information, retrieves and analyzes purchase history. This allows the system to compare the food items the user owns with the required ingredients based on the selected recipe and identify any missing ingredients.

[0661] The server uses a generative model to create alternatives for the food items that are in short supply. This generative AI model presents multiple usable alternatives for the missing ingredients. Users can review these alternatives via their terminal and, if necessary, order the missing food items in advance through a communication device. Furthermore, a notification system can be used to send real-time notifications to users regarding food shortages, prompting them to procure the items.

[0662] This invention is programmed using Python and the requests library. Through a series of processes including data collection and analysis, generation of alternatives using generative models, and transmission and reception of data using communication devices, users can easily gather the necessary materials.

[0663] For example, if a user wants to make a pizza and has cheese and bell peppers in the refrigerator but no tomato sauce, the system can suggest alternatives such as "substitute with ketchup and basil." An example of a prompt message could be in the format of "Current ingredient list: cheese, bell peppers. What ingredients are missing to make the pizza?" This enables efficient ingredient management and purchasing simply by entering the necessary information.

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] The user uses a device to input information about the food items they have on hand.

[0667] The entered information includes the names and quantities of ingredients and seasonings. The terminal sends this information to the server, which stores it in a database. The input in this step is the food information provided by the user, and the output is the data stored on the server.

[0668] Step 2:

[0669] The server retrieves the user's purchase history from an external database.

[0670] The acquired purchase history data includes information about previously purchased foods and their frequency. The server analyzes this information to identify the user's purchasing patterns. The input for this step is the purchase history retrieved from an external database, and the output is the analyzed purchasing pattern information.

[0671] Step 3:

[0672] The user selects the recipe for the dish they want to make on their device.

[0673] The recipe information is sent to the server, which performs analysis. The ingredients required for the recipe are compared with the user's available food information, and any missing ingredients are identified. The input for this step is the recipe information selected by the user, and the output is a list of missing ingredients.

[0674] Step 4:

[0675] The server uses a generative AI model to generate alternative solutions for any missing materials.

[0676] The generative model calculates available alternative ingredients based on the input information about the missing ingredients. The generated alternatives are sent to the terminal and displayed to the user. The input for this step is a list of missing ingredients, and the output is a list of alternatives.

[0677] Step 5:

[0678] The device sends a notification to the user prompting them to acquire any missing materials.

[0679] The notification includes information on alternatives and options for ordering online. This gives users an easy way to supplement missing ingredients. The input for this step is the generated alternatives, and the output is the notification information for the user.

[0680] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0681] This invention relates to an AI system equipped with emotion recognition capabilities to support the cooking process. It measures the user's emotions and provides personalized suggestions and notifications accordingly, offering a more personalized cooking experience. The system analyzes recipes based on user-inputted ingredient information and purchase history, and uses an AI-generated model to suggest optimal alternatives. Furthermore, by combining this with an emotion engine, it recognizes the user's emotional state and provides suggestions tailored to those emotions.

[0682] Specifically, users input information about the ingredients and seasonings they have at home through the application. The device sends this data to a server, which stores it in a database. The server uses the collected data to retrieve the user's purchase history via an API and compares it with the ingredients stored in the database.

[0683] When a user selects a dish they want to make, the server analyzes the necessary ingredients and steps for that recipe. It identifies any missing ingredients, and an AI generative model generates alternatives. In addition, an emotion engine recognizes the user's current emotional state from their facial expressions and voice. Based on this information, the server adjusts the suggestions and sends an alternative that takes the user's emotions into consideration to the device. For example, if the recognized emotion is "anxiety," a simplified suggestion will be provided.

[0684] Furthermore, a feature has been added that adjusts the content and timing of notifications according to the user's emotional state. This adjustment optimizes the timing of purchasing necessary ingredients based on emotions; for example, if the user is feeling stressed, it will send purchase suggestions that take that situation into consideration.

[0685] By taking user emotions into consideration in this way, it becomes possible to provide a richer and more successful cooking experience and reduce food waste.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] Users input the ingredients and seasonings they have at home using an application or web interface. The entered information is sent from the terminal to the server, which stores this data in a database.

[0689] Step 2:

[0690] The server retrieves the user's purchase history via an API. By collecting past purchase data, it understands the user's purchasing patterns. This allows for the identification of frequently purchased food items and condiments. The server stores this data in a database.

[0691] Step 3:

[0692] The user selects a recipe for the dish they want to make. The device sends this information to the server, which retrieves the selected recipe from its database and analyzes the necessary ingredients and steps. It then compares this information with the user's available ingredients to identify any missing ingredients.

[0693] Step 4:

[0694] The server compares the ingredients it has on hand with the ingredients required for the recipe and activates an AI generation model based on the missing ingredients. The generation model devises alternatives to compensate for the missing ingredients and prepares these suggestions.

[0695] Step 5:

[0696] The emotion engine analyzes the user's facial expressions and voice on the device to recognize their current emotional state. The recognized emotional information is sent to a server and used to adjust the suggestions. The server generates emotionally sensitive suggestions and sends them to the device. For example, if the user is feeling anxious, a simplified suggestion will be provided.

[0697] Step 6:

[0698] The device displays suggestions to the user. The user then proceeds with cooking based on these suggestions. The suggestions are optimized for the user's situation and emotions.

[0699] Step 7:

[0700] The server adjusts the content and timing of purchase suggestion notifications based on the user's emotional state. The device provides these notifications to the user in real time, encouraging them to use them to help with their next grocery purchase. For example, purchase suggestions are adjusted to appear during times when the user is calm.

[0701] (Example 2)

[0702] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] Conventional meal preparation support systems fail to consider the user's emotional state when making suggestions, making it difficult to provide advice optimized for the user's needs in specific situations. Furthermore, suggestions based on available ingredients and purchase history are not adequately integrated, making efficient resource utilization difficult and resulting in waste.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0705] In this invention, the server includes means for collecting user resource information, means for acquiring and analyzing purchase history, means for analyzing necessary materials and comparing them with existing materials, means for recognizing the user's emotional state and adjusting the suggested content accordingly, and means for providing notifications to encourage the purchase of insufficient resources. This enables personalized suggestions that take the user's emotions into consideration, resulting in efficient resource utilization and an improved user experience.

[0706] "Resource information" refers to detailed information about ingredients and seasonings owned by the user, including attributes such as quantity and expiration date.

[0707] "Purchase history" refers to information about materials and items that a user has purchased in the past, and consists of data such as the date and time of purchase and the quantity.

[0708] An "alternative" is another option proposed to fill in the missing materials for the user, and it is optimized by a generative model.

[0709] A "generative model" is an algorithm that uses artificial intelligence technology to analyze data and automatically generate optimal suggestions and solutions.

[0710] "Emotional state" refers to the user's current psychological state, which is recognized in real time from facial expressions, voice, and other factors.

[0711] A "notification" is a message that conveys information or suggestions to a user, and its timing and content are adjusted to meet the user's needs.

[0712] This invention is a system for providing users with personalized cooking suggestions that take their emotions into consideration. This system combines ingredient information and emotion recognition data, and uses a generative AI model to present the optimal alternative. The system includes the following elements:

[0713] Users input information about ingredients and seasonings they have at home through the application. Specifically, they can use a smartphone or tablet to enter details such as type, quantity, and expiration date. The device then transmits this data to the server via the internet.

[0714] The server stores the received material information in a database and retrieves the user's purchase history via an API based on that information. As a software platform, an open-source database management system can be used for server-side data analysis. This data will be used for subsequent analysis.

[0715] When a user selects a dish they want to make, the server analyzes the plan and identifies key ingredients and processes. If necessary, it passes missing ingredients to a generative AI model, which then generates the optimal alternative. This generative AI model can utilize machine learning libraries developed in, for example, Python, and calculates the optimal solution in real time.

[0716] Furthermore, the device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time, and the server processes this data using an emotion engine to recognize the user's psychological state. Based on this information, the server generates suggestions tailored to the user's emotions and sends them to the device.

[0717] A concrete example of a prompt statement is, "I don't have much time today, but I want to make a proper dinner." In response to this prompt, the system can suggest a simple recipe that can be prepared in a short amount of time.

[0718] These features enable the system to offer suggestions that resonate with the user's emotions, improving both the efficiency of meal preparation and the user experience.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] Users enter information about ingredients and seasonings they have at home into a dedicated application on their device. This includes detailed information such as type, quantity, and expiration date. The entered data is transmitted from the device to a server via the internet. The server receives this data and records it in a database. This ensures that the system stores the most up-to-date information about the ingredients the user has on hand.

[0722] Step 2:

[0723] The server retrieves purchase history via an API based on the user's material information. The retrieved purchase history data is stored in a database and serves as foundational data for understanding the user's past purchasing patterns. The server compares the purchase history with the current material information to identify which materials are lacking.

[0724] Step 3:

[0725] The user selects the dish they want to make within the application. The server analyzes this selected recipe information to identify the necessary ingredients and steps. Based on this analysis, any missing ingredients are identified and passed to a generative AI model. This model calculates the optimal alternative ingredients based on past data and the current situation, and generates suggestions.

[0726] Step 4:

[0727] The device uses its built-in camera and microphone to capture the user's facial expressions and voice in real time. The captured data is sent to a server, where an emotion engine analyzes it. The server understands the user's emotional state in real time and initiates a process to adjust the suggestions based on that information.

[0728] Step 5:

[0729] Based on the sentiment analysis results, the server adjusts the alternatives generated by the generative AI model to create suggestions that best suit the user's emotions. For example, if the user is expressing anxiety, a simplified recipe will be suggested. The adjusted suggestions are sent to the device, and the user can receive the information through the application.

[0730] Step 6:

[0731] The server optimizes not only suggestions but also the content and timing of notifications based on the user's emotions. If a user is feeling stressed, it adjusts product suggestions and notifications to better promote relaxation. The device receives this information and presents it to the user at the appropriate time.

[0732] (Application Example 2)

[0733] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] In today's busy daily lives, there is a lack of cooking support tailored to individual emotional states, and cooking often becomes a source of stress. Therefore, it is necessary to provide a personalized cooking experience that responds to the user's emotions, thereby improving the success rate of cooking and reducing the purchase and use of unnecessary ingredients.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0736] In this invention, the server includes means for acquiring information about ingredients entered by the user, means for acquiring and analyzing purchase history, means for analyzing information about recipes including necessary ingredients and processes and comparing them with the ingredients owned, means for using a generative model to generate and suggest alternatives based on missing ingredients, means for reading the user's emotional state and utilizing emotion recognition to make adjustment suggestions, and means for providing music and visuals based on the learner's current emotional state. This makes it possible to provide optimal cooking support according to the user's emotional state.

[0737] "Means for acquiring ingredient information" refers to a function that collects data about the ingredients used in the dishes that the user prepares.

[0738] "Means for acquiring and analyzing purchase history" refers to a function that acquires and analyzes a user's past purchase data.

[0739] "A means of analyzing information about a recipe and comparing it to the ingredients you have" refers to a function that analyzes the details of a provided recipe and compares them to the ingredients you have on hand.

[0740] "Means of using generative models to generate and propose alternatives" refers to a function that uses AI models to generate alternative options for the necessary materials and proposes them to the user.

[0741] "A means of utilizing emotion recognition to read emotional states and make adjustment suggestions" refers to a function that analyzes the user's facial expressions and voice to recognize emotions and then makes suggestions based on those emotions.

[0742] "A means of providing music and visuals based on the participant's current emotional state" refers to a function that provides users with appropriate music and visual support based on the results of emotional analysis.

[0743] To realize this invention, the system is configured as a user terminal and a cloud server. The user terminal is a device such as a smartphone or smart glasses, which collects data through its camera and microphone and analyzes emotions in real time using an emotion recognition API. Microsoft's Azure Face API and Google Cloud Speech-to-Text API are suitable tools for emotion recognition.

[0744] The server processes large amounts of data and stores the ingredient information entered by the user in a database. It also retrieves purchase history and compares the ingredients the user owns with the provided recipes. Using an AI generative model (such as OpenAI's GPT-3), it generates alternatives for missing ingredients.

[0745] If the user's emotional state is "specific," the server recognizes that state and sends a customized meal suggestion to the terminal. Furthermore, it provides multimedia content such as music and visual support; for example, if the user's emotion is anxiety, it plays relaxing music.

[0746] As a concrete example, let's say a user is working on a project to bake bread. If the user is feeling anxious, the system can provide support by simplifying suggestions and displaying a specific music playlist on their device, thus creating a sense of reassurance. In this way, the probability of success in the cooking process can be significantly increased.

[0747] An example of a prompt message is: "If the user is anxious, generate support suggestions to simplify the task and play relaxing music."

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] The user inputs ingredient information into the terminal. The terminal sends this information to the cloud server. The input includes ingredient name, quantity, type, etc., and this information is formatted and output as data to be recorded in the database.

[0751] Step 2:

[0752] The server retrieves the user's purchase history data via an API. The retrieved data includes past purchase dates and items, which are then analyzed and compiled into a list to compare with the user's current food inventory.

[0753] Step 3:

[0754] The server uses the entered ingredient information and purchase history to check the ingredients required for the recipe selected by the user and compares them with the user's owned ingredients. This comparison reveals any missing ingredients, which are then compiled into a list.

[0755] Step 4:

[0756] The server generates alternatives using a generative AI model for missing ingredients. The input is a list of missing ingredients, and the alternatives are output as substitutes for ingredients or modified recipes. The generation process is set up using prompts.

[0757] Step 5:

[0758] The device collects the user's emotional state through its camera and microphone and analyzes it using an emotion recognition API. Based on this analysis, the device sends the user's emotional data to the server. The output is data on the emotional state.

[0759] Step 6:

[0760] The server considers emotional data and alternatives, and sends recipe suggestions and simplified procedures tailored to the user's emotions to the device. Relaxing music and visual materials are also added as support information, depending on the emotional state.

[0761] 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.

[0762] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0763] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0764] 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.

[0765] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0766] 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.

[0767] 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.

[0768] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0769] 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."

[0770] 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.

[0771] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0772] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0781] 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.

[0782] The following is further disclosed regarding the embodiments described above.

[0783] (Claim 1)

[0784] A means of collecting ingredient information entered by the user,

[0785] A means of acquiring and analyzing purchase history,

[0786] A method for analyzing recipe information, including necessary ingredients and steps, and comparing it with the ingredients you have on hand,

[0787] A method using a generative model that generates and proposes alternatives based on the lack of materials,

[0788] A means of providing notifications to encourage the purchase of shortages of food ingredients,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising means for collecting user feedback and utilizing it as data to improve the generative model.

[0792] (Claim 3)

[0793] The system according to claim 1, comprising an interface that displays suggestions and notifications on a terminal in real time.

[0794] "Example 1"

[0795] (Claim 1)

[0796] A means of collecting food information entered by the user,

[0797] A means of acquiring and analyzing purchase history,

[0798] A method to analyze cooking information, including necessary ingredients and preparation steps, and compare it with the ingredients available,

[0799] A means of using a generation system that generates and presents alternative solutions based on the lack of materials,

[0800] A means of providing notifications to encourage the purchase of shortages of materials,

[0801] A method for analyzing user purchasing trends and adding missing items to the purchase list,

[0802] A means of prompting the user to input a command and instructing the generation system,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for collecting user feedback and using it as data to improve the generation system.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising an information display unit that immediately displays suggestions and notifications on the terminal.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of collecting food information entered by the user,

[0811] A means of acquiring and analyzing purchase history,

[0812] A means of analyzing processing information, including the necessary materials and processes, and comparing it with the materials on hand,

[0813] A method using a generative model that generates and proposes alternatives based on the lack of materials,

[0814] Means of providing notifications to promote the acquisition of scarce food items,

[0815] A means including a communication device that allows ordering of shortage food items in advance,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, comprising means for collecting user feedback and utilizing it as numerical data to improve the generative model.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising a communication screen that displays suggestions and notifications on a terminal in real time.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] A means of collecting resource information entered by the user,

[0824] A means of acquiring and analyzing purchase history,

[0825] A means of analyzing planning information, including necessary materials and procedures, and comparing it with the materials on hand,

[0826] A method using a generative model that generates and proposes alternatives based on the lack of materials,

[0827] A means of recognizing the user's emotional state and adjusting the suggested content accordingly,

[0828] Means of providing notifications to encourage the purchase of scarce resources,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, comprising means for adjusting the content and timing of notifications based on user sentiment information.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising an interface that displays emotionally sensitive suggestions and notifications on a terminal in real time.

[0834] "Application example 2 when combining with an emotional engine"

[0835] (Claim 1)

[0836] A means of obtaining information about ingredients entered by the user,

[0837] A means of acquiring and analyzing purchase history,

[0838] Analyze information about recipes, including the necessary ingredients and processes, and compare it with the ingredients you have.

[0839] A means of using generative models to generate and propose alternatives based on missing materials,

[0840] A means of providing notifications to encourage the purchase of scarce food items,

[0841] A means of utilizing emotion recognition to read the user's emotional state and make adjustment suggestions,

[0842] A means of providing music and visuals based on the current emotional state of the participants,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, comprising means for collecting user feedback and using it as data to improve the generative model.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising information display means for displaying suggestions and notifications on a terminal in real time. [Explanation of Symbols]

[0848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting ingredient information entered by the user, A means of acquiring and analyzing purchase history, A method for analyzing recipe information, including necessary ingredients and steps, and comparing it with the ingredients you have on hand, A method using a generative model that generates and proposes alternatives based on the lack of materials, A means of providing notifications to encourage the purchase of shortages of food ingredients, A system that includes this.

2. The system according to claim 1, further comprising means for collecting user feedback and utilizing it as data to improve the generative model.

3. The system according to claim 1, comprising an interface that displays suggestions and notifications on a terminal in real time.

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