system

Abstract

We provide the system. [Solution] Means for receiving user information, Means for receiving climate information, Methods for analyzing past meal content, A means for generating meal suggestions that take nutritional balance into consideration, based on the user information, climate information, and past meal content, A system including means for sending generated meal suggestions to a user's terminal.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, with many people leading busy lives, it is not easy to maintain a nutritionally balanced diet. In particular, considering the differences in nutritional needs due to family composition and climate changes, the time for daily menu planning is limited, and as a result, there is a possibility of continuing an unbalanced diet. Also, by not considering the recent meal content, there may be an excess or deficiency of nutrients. It is necessary to solve such problems.

Means for Solving the Problems

[0005] This invention provides a system that receives user information (age, gender, family structure, etc.) and climate information (weather, temperature, etc.), and further analyzes past meal content. Based on the user information, climate information, and past meal content, it can generate meal suggestions that consider nutritional balance and send them to the user's terminal. Furthermore, by receiving feedback from the user and regenerating meal suggestions based on that information, it enables flexible menu suggestions tailored to the user's preferences and circumstances. This configuration can support a healthy and balanced diet without requiring much time.

[0006] "User information" refers to data necessary for the system to understand the user's personal circumstances and characteristics, and includes information such as age, gender, and family structure.

[0007] "Climate information" refers to data about the weather and temperature in the user's area of ​​residence, and is information about the external environment that the system considers when making meal suggestions.

[0008] "Past meal history" refers to the user's recent meal history and is used to analyze imbalances in ingredients and nutrients.

[0009] "Nutritional balance" is a concept that refers to the appropriate intake ratio of various nutrients necessary to maintain a healthy lifestyle.

[0010] "Meal suggestions" are menu ideas generated based on user information, climate information, and past meal content, and are designed with nutritional balance in mind.

[0011] A "user terminal" refers to a device that receives meal suggestions generated by the system and displays them to the user, such as a smartphone or tablet. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled 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.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled 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.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] In embodiments of the present invention, the system functions as a mechanism for exchanging information between a user terminal and a server and providing the user with appropriate meal suggestions. The user inputs basic information such as age, gender, and family structure, as well as recent meal details, through the terminal. The terminal collects climate data based on the input data and transmits all the information to the server.

[0034] Based on the received data, the server analyzes user information, climate information, and past meal history to generate meal suggestions that take nutritional balance into consideration. These meal suggestions include recommended nutrient intake based on the user's age and gender, as well as meal content tailored to climate conditions.

[0035] For example, if the user is a 30-year-old male who ate a lot of pasta for dinner the previous night, the server can recommend a high-protein, vegetable-rich meal for his next meal. Also, on a cold, rainy day, the server might consider suggesting a meal that includes a warming soup.

[0036] Meal suggestions generated by the server are sent to the terminal and presented to the user. The user can provide feedback on the presented menu and, if necessary, send more detailed requests. Based on this feedback, the server readjusts the meal suggestions and makes new suggestions that meet the user's needs.

[0037] In this way, the system aims to support a healthy and balanced diet by providing personalized menus for each user.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users enter basic information such as age, gender, and family structure into the input form on the device. In addition, they record and enter details of their recent meals into the device.

[0041] Step 2:

[0042] The device saves the entered user information and meal details to local storage. It also accesses an API via the internet to retrieve current weather data based on the user's location.

[0043] Step 3:

[0044] The device packages all the collected information (user information, meal history, weather data) and sends it to the server.

[0045] Step 4:

[0046] The server begins analyzing the received data. First, it calculates the recommended daily nutrient intake based on the user's age and gender.

[0047] Step 5:

[0048] The server analyzes weather information and sets criteria for determining what to eat that day based on the weather conditions. For example, it might consider a warm soup on a cold day and a refreshing salad on a hot day.

[0049] Step 6:

[0050] The server analyzes past meal history and assesses any nutritional imbalances. Based on this information, it identifies the nutrients that should be consumed in the next meal.

[0051] Step 7:

[0052] The server generates nutritionally balanced meal suggestions based on user information, climate information, and analysis of meal history.

[0053] Step 8:

[0054] The server sends the generated meal suggestions to the user's terminal.

[0055] Step 9:

[0056] The device displays the suggested menu to the user and provides a feedback input screen.

[0057] Step 10:

[0058] Users enter feedback on the suggested menu into their device and, if necessary, enter any further requests for changes or additions.

[0059] Step 11:

[0060] The device sends user feedback to the server.

[0061] Step 12:

[0062] Based on user feedback, the server readjusts meal suggestions as needed and provides them to the user again.

[0063] (Example 1)

[0064] 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."

[0065] The aim is to enable users to achieve a healthy and balanced diet by providing a system that offers appropriate meal suggestions tailored to individual health conditions and preferences. However, conventional methods make it difficult to provide precise suggestions that take various factors into account, and there are particular challenges in personalized meal suggestions and their accuracy.

[0066] 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.

[0067] In this invention, the server includes a device for receiving user information, a device for receiving weather information, and a device for analyzing past intake information. This makes it possible to provide personalized meal suggestions tailored to the individual needs of the user and support a healthy diet.

[0068] "User information" refers to personal information necessary for meal suggestions, such as the user's age, gender, family structure, and past meal history.

[0069] "Weather information" refers to data related to the current weather and temperature at the user's geographical location.

[0070] "Past intake information" refers to historical data about the foods and nutrients that the user has previously consumed in meals.

[0071] "Nutritional balance" refers to the appropriate proportion of various nutrients necessary to maintain a healthy diet.

[0072] A "generative AI model" is a model that applies machine learning and artificial intelligence techniques to generate personalized meal suggestions.

[0073] A "user terminal" is a digital device used by users to input information and receive meal suggestions.

[0074] "Responses" refer to user feedback and requests regarding the generated meal suggestions.

[0075] To implement this invention, a system is constructed using a user terminal and a server. First, the user uses the user terminal to input necessary user information such as their age, gender, family structure, and recent meal details. The user terminal is equipped with application software that provides an interface to enable this information input.

[0076] Next, the terminal transmits the entered information to the server via the computer network. At this time, the terminal also obtains weather information based on its current geographic location using an API, and transmits all the data together to the server. For example, API data obtained from a weather information service is used as supplementary information.

[0077] The server uses a generative AI model to analyze data based on received user information and weather information, comparing it with past intake data. This analysis generates meal suggestions tailored to the user's individual nutritional needs. This process utilizes machine learning models to learn patterns from various data, enabling the provision of menus that support the user's healthy eating habits.

[0078] As a concrete example, the server is provided with the following information as a prompt: "30-year-old male, had pasta for dinner yesterday, it's raining and cold today." Based on this information, the server generates a meal suggestion: "A meal high in protein with warm soup." The generated suggestion is then sent back to the user's terminal via the network and presented to the user. The user reviews the suggestion and provides feedback, which allows the server to further improve it and incorporate the feedback into future suggestions.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user enters their age, gender, family structure, and recent meal details into the user terminal. The entered information is temporarily stored in the terminal's database. In this step, there is input from the user, and as a result, a user information dataset is generated as output. The terminal checks the integrity of the information and prepares for the next step.

[0082] Step 2:

[0083] The device collects local weather information via the internet based on the user information entered. The API used here is from a weather service provider and retrieves temperature and weather conditions. Based on the user information as input, an API request is sent, and the weather information is returned to the device as output. This information is integrated with the user information and prepared for transmission to the server.

[0084] Step 3:

[0085] The terminal transmits integrated user information and weather data to the server. This information is delivered to the server via the internet and treated as a single data package. All user information and weather data are used as input, and the data package is generated as output that is transferred to the server.

[0086] Step 4:

[0087] The server receives the transmitted data package and performs analysis using a generative AI model. During the analysis, past intake information and other relevant data are also referenced. The server takes the data package as input, the generative AI model calculates meal suggestions through analysis, and generates the results as output. This process particularly considers the balance of nutrients and individual preference patterns.

[0088] Step 5:

[0089] The server sends the generated meal suggestions to the user's terminal. The suggestions are displayed to the user in an easy-to-understand format on the terminal. The input here is the meal suggestion data, and the output is the visualized suggestions displayed on the user's terminal.

[0090] Step 6:

[0091] Users review the suggested meal menus and submit feedback via their device. This feedback includes acceptance of the suggestions or requests for modifications, and is used as input to further improve the suggestions. The output is user feedback data.

[0092] Step 7:

[0093] The server analyzes the received feedback and readjusts the generated AI model. It also creates new meal suggestions based on the feedback, improving the model's accuracy. Using the feedback data as input, an improved next meal suggestion is generated as output. This loop process gradually improves the accuracy of the suggestions.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern life, proposing nutritionally balanced meals tailored to individual health conditions and lifestyles, and then efficiently providing them, is a crucial challenge. In particular, there is a need for a system that allows for easy selection of healthy meals in busy daily lives. However, conventional systems have struggled to provide meal suggestions that take into account climatic conditions and individual user preferences. Furthermore, a lack of means to directly supply the suggested meals has been a problem.

[0097] 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.

[0098] In this invention, the server includes means for receiving user information, means for receiving climate information, means for analyzing past meal history, and means for coordinating with affiliated food and beverage services to supply suggested meals. This makes it possible to suggest nutritionally balanced meals based on the user's individual conditions and to supply them efficiently.

[0099] "Means for receiving user information" refers to a system for obtaining individual information from users, such as age, gender, family structure, and recent dietary habits.

[0100] A "means for receiving climate information" refers to a mechanism that provides an interface for obtaining current weather conditions.

[0101] "Methods for analyzing past meal history" refers to a process for analyzing a user's past diet and evaluating its nutritional balance.

[0102] "A means of generating meal suggestions that take nutritional balance into consideration" refers to an algorithm that plans meal content to support a healthy lifestyle based on acquired user information, climate information, and meal history.

[0103] "Means for transmitting generated meal suggestions to the user device" refers to communication technology for notifying the user's terminal of the planned meal content.

[0104] "A means of supplying suggested meals in cooperation with partner food and beverage services" refers to a system that collaborates with external food and beverage delivery services to deliver suggested meals to users as actual dishes.

[0105] "Means of receiving feedback" refers to an interface for users to input evaluations and opinions on the suggested meals.

[0106] "Means for regenerating suggestions" refers to the process of updating meal suggestions by taking user feedback into consideration.

[0107] One embodiment of this invention is a system that provides personalized meal suggestions based on user information and climate conditions, and streamlines the process of providing them.

[0108] On the user's terminal, a dedicated application runs, allowing the user to input necessary information such as their age, gender, family structure, and recent diet. The terminal compiles this information and sends it to a server via the internet. Furthermore, the terminal utilizes an API to obtain local climate data and also sends that data to the server.

[0109] The server performs detailed data analysis based on the received user information and weather conditions. This analysis utilizes algorithms built in Python or R programming environments. Using this information, the server performs calculations to generate meal suggestions that take into account the individual nutritional balance of each user. These suggestions include nutritional intake guidelines based on age and gender, as well as menu suggestions tailored to temperature and weather.

[0110] The generated meal suggestions are quickly sent to the user's terminal and notified to the user. The user sends a response to the suggested meal from their terminal, and the server readjusts the suggestions based on that feedback. In this way, the suggestions are more accurately adapted to the user's preferences and wishes.

[0111] The suggested meals are delivered as actual dishes through partnerships with external food and beverage services. This integration utilizes the food and beverage service's API, allowing users to complete their orders within the application.

[0112] As a concrete example, let's assume the user is a 30-year-old man who ate high-calorie ramen last night. Because it's a rainy and cold day, the server suggests a high-protein, low-calorie hot pot dish, which can then be ordered through a local delivery service.

[0113] An example of a prompt message is as follows:

[0114] "Given that the user is 30 years old, male, ate ramen for dinner yesterday, and the weather today is cold and rainy, please suggest an optimally balanced dinner menu. Please include warming dishes and high-protein options."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user terminal receives input from the user, including age, gender, family structure, and recent meal history. Based on this input, the terminal calls a weather API to obtain climate information. This results in a combination of the user's personal data and the latest climate data.

[0118] Step 2:

[0119] The terminal transmits collected user information and climate information to the server via the internet. This data transfer allows the server to obtain all the information necessary for analysis.

[0120] Step 3:

[0121] The server analyzes received user information, climate information, and past dietary data. Using statistical analysis libraries in Python or R, it evaluates the user's recent nutritional intake and determines necessary nutrients. This constitutes data processing and calculation.

[0122] Step 4:

[0123] The server uses a generative AI model to generate optimal meal suggestions based on the analysis results. It uses prompts to create specific menu suggestions tailored to the user's conditions. An example of such a prompt would be, "The user is 30 years old, male, ate ramen for dinner yesterday, and today's weather is cold and rainy. Please suggest an optimally nutritionally balanced dinner menu."

[0124] Step 5:

[0125] The server sends the generated meal suggestions back to the user's device. This allows the user to view the suggested menu on their device.

[0126] Step 6:

[0127] Users provide feedback on the suggested meal. For example, they send responses such as "I don't like this dish" or "I'd like more vegetables" from their device to the server. The server receives this as input and re-analyzes it.

[0128] Step 7:

[0129] The server regenerates meal suggestions based on user feedback and sends the new suggestions to the device. This process allows for readjustment of suggestions to suit the user's preferences and circumstances.

[0130] Step 8:

[0131] Users can order the suggested meals through external food and beverage services. The terminal calls the API of the partner food and beverage service to process the order and ensure that the user's meal is delivered reliably.

[0132] 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.

[0133] This invention provides an innovative system that incorporates an emotion recognition engine to offer meal suggestions that take the user's emotions into account. The user inputs basic personal information (age, gender, family structure, etc.) and recent meal details from a terminal. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotion data.

[0134] The device sends the collected information, along with this emotional data, to the server. The server integrates and analyzes user information, weather information, meal history, and emotional data. For example, if the user is feeling stressed, it will generate meal suggestions that include relaxing herbal tea. It will also consider appropriate meal types based on the weather and temperature and reflect this in the suggestions.

[0135] For example, if a user is feeling fatigued and the outside temperature is low, the server can generate a high-protein, nutritionally balanced menu centered around chicken and vegetable soup. This menu suggestion incorporates elements that promote relaxation and aims to improve the user's mood.

[0136] The generated meal suggestions are delivered to the user via a terminal. The user can provide feedback on the menu and request alternatives based on their feelings and preferences. The server also analyzes this feedback data to provide more personalized suggestions for the next meal.

[0137] This invention constitutes a system that effectively supports the improvement of users' health and mood through personalized meal suggestions based on emotion recognition.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user enters their age, gender, family structure, and recent meal details into the device. The device temporarily stores this entered information.

[0141] Step 2:

[0142] The device uses its built-in camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state using an emotion recognition sensor.

[0143] Step 3:

[0144] The device collects acquired user information, recent meal details, emotional data, and weather information, packages all the data, and sends it to the server.

[0145] Step 4:

[0146] The server analyzes the received data and calculates the recommended daily nutrient intake based on the user's age and gender.

[0147] Step 5:

[0148] The server uses weather information to determine the type of meal appropriate for the day's climate. For example, on cold days, it considers a warm meal.

[0149] Step 6:

[0150] The server analyzes past meal history and identifies nutrients to correct nutritional imbalances.

[0151] Step 7:

[0152] The server analyzes emotional data and identifies and incorporates ingredients that have a relaxing effect and culinary elements that improve emotions.

[0153] Step 8:

[0154] The server combines candidate ingredients to generate nutritionally balanced meal suggestions that improve the user's health and mood.

[0155] Step 9:

[0156] The server sends the generated meal suggestions to the terminal.

[0157] Step 10:

[0158] The terminal displays the suggested menu to the user and provides an interface for requesting feedback.

[0159] Step 11:

[0160] Users input feedback on the presented menu and any elements they wish to change into the terminal.

[0161] Step 12:

[0162] The device sends user feedback to the server, which then re-analyzes the data and readjusts meal suggestions as needed.

[0163] (Example 2)

[0164] 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".

[0165] In recent years, in order to efficiently manage individual health, it has become necessary to suggest meals tailored to the user's emotional state and individual needs. However, conventional systems do not adequately consider the user's emotional state, and there are limitations to realizing personalized meal suggestions. This invention aims to support health maintenance and mental stability by enabling personalized meal suggestions that take the user's emotional state into consideration.

[0166] 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.

[0167] In this invention, the server includes means for receiving the user's basic information and emotional data, means for receiving climate information, and means for analyzing past meal information to analyze the emotional state. This makes it possible to suggest meals that take into account the user's emotional state and nutritional balance.

[0168] "User basic information" refers to fundamental attributes of an individual, such as age, gender, and family structure, and is the basic data that allows the system to individually identify users and make appropriate suggestions.

[0169] "Emotional data" refers to data that expresses a user's emotional state based on information obtained through the user's facial expressions, voice tone, and other means.

[0170] "Climate information" refers to information about environmental factors such as weather and temperature, and is data that is considered in order to provide meal suggestions suitable for the user.

[0171] "Past meal information" refers to data about the meals a user has previously consumed, and is used to understand their meal selection patterns and nutritional status.

[0172] "Meal suggestions" are personalized, nutritionally balanced menu recommendations created based on the user's basic information, emotional data, climate information, and past eating history.

[0173] A "user device" is a terminal used by a user to input information or receive suggestions, and is equipped with sensors for acquiring emotional data.

[0174] "Feedback" refers to the evaluations and opinions that users give regarding suggested meals, and it is an important source of information for making future suggestions more personalized.

[0175] "Analysis" is a series of processes that use collected data to analyze the user's emotional state and eating habits, and then generate appropriate meal suggestions.

[0176] This invention is a system that provides meal suggestions that take the user's emotions into consideration, and is implemented as follows. The system consists of the interaction of the user, a terminal, and a server.

[0177] The user enters their basic information (age, gender, family structure, etc.) and past meal history through the device. The device is equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotional data. This measures the user's current emotional state and records it as data.

[0178] The device transmits acquired basic information, emotional data, and past meal information to the server. The server integrates this information, along with climate information acquired from external sources, and performs data analysis. The data analysis uses an emotional recognition engine and a generative AI model to generate meal suggestions that take into account the user's emotional state and nutritional balance.

[0179] For example, suppose a user inputs, "I'm a man in my 30s, and I've been feeling stressed at work lately. What kind of meal would you suggest for a cold day?" In this case, the system can determine the user's stress level from emotional data and, taking temperature information into consideration, suggest a relaxing herbal tea and a nutritionally balanced chicken and vegetable soup.

[0180] The generated meal suggestions are provided to the user via a terminal. The user inputs their thoughts and preferences as feedback on the provided suggestions into the terminal. This feedback information is sent back to the server and used to improve the accuracy of future meal suggestions.

[0181] Through this process, users can receive meal suggestions tailored to their emotional state and individual needs, enabling them to maintain their health and improve their mood.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] Users enter basic information (age, gender, family structure, etc.) and past meal history using their device. The entered information is saved as initial data within the device. This prepares individual basic data.

[0185] Step 2:

[0186] The user utilizes the camera and microphone built into the device, allowing the emotion recognition sensor to capture the user's facial expressions and voice tone. The acquired data is analyzed by an emotion recognition algorithm to generate the user's emotion data. This emotion data indicates the user's current emotional state.

[0187] Step 3:

[0188] The device sends the basic information and meal information obtained in Step 1, along with the emotional data generated in Step 2, to the server. Data transmission takes place via a secure communication protocol, and a dataset that can be analyzed is constructed on the server side.

[0189] Step 4:

[0190] The server integrates received user information, emotional data, and dietary information with climate information obtained from external sources. Data analysis is then performed on the integrated dataset using generative AI models and analysis engines. Specifically, it generates nutritionally balanced meal suggestions that take into account the user's emotional state and climate conditions.

[0191] Step 5:

[0192] Meal suggestions generated by the server are sent to the terminal. The terminal displays this information to the user, providing them with specific menu details. The user can then make a meal selection based on these suggestions.

[0193] Step 6:

[0194] Users input feedback on the displayed meal suggestions via their device. This feedback includes their impressions and changes in their emotional state regarding the suggestions. This contributes to improving the accuracy of future suggestions.

[0195] Step 7:

[0196] The device sends the input feedback to the server. The server analyzes this feedback and uses it to improve the next meal suggestion, taking into account the user's emotional state, thereby providing more suitable suggestions.

[0197] (Application Example 2)

[0198] 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".

[0199] Conventional meal suggestion systems fail to adequately consider the user's emotions and mood, merely providing meal suggestions based on nutritional balance and basic personal information. As a result, optimal meal suggestions tailored to the user's psychological state and external environment are not obtained, limiting user satisfaction and the effectiveness of health improvement. This invention aims to solve these problems and provide more personalized meal suggestions that take the user's emotional state into consideration.

[0200] 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.

[0201] In this invention, the server includes means for receiving user information, means for receiving weather condition information, means for analyzing past meal history, means equipped with an emotion recognition module for analyzing facial expressions and voice tone as emotion data, and means for automatically placing orders by coordinating the generated meal suggestions with a delivery service. This enables the generation of optimal meal suggestions tailored to the user's emotions and rapid delivery.

[0202] "User information" refers to basic data about an individual, including age, gender, and family structure.

[0203] "Weather condition information" refers to environmental data such as weather, temperature, and humidity for a specific region, and is obtained from external weather information service providers.

[0204] "Past meal history" refers to a record of meals the user has consumed to date, including information such as individual foods, nutrients, and the date and time of consumption.

[0205] "Emotional data" refers to data that indicates a user's psychological state, obtained by analyzing their facial expressions and voice tone, and expresses emotions such as joy, anger, and sadness.

[0206] An "emotion recognition module" is a program or device that analyzes a user's facial expressions and voice tone and generates emotion data based on that analysis.

[0207] "Meal suggestions" refer to suggestions that include menus and ingredients of meals recommended to the user based on specific conditions.

[0208] A "delivery service" is a service that provides logistics functions to deliver meals ordered by users to a specified location.

[0209] The system implementing this invention consists of a user terminal equipped with an emotion recognition sensor and a data processing server in the cloud. The user terminal is equipped with a camera and microphone, which can be used to capture the user's facial expressions and voice tone in real time. This data is analyzed as emotion data using emotion recognition software such as Amazon Rekognition. The terminal also collects the user's basic information and past meal history, and comprehensively transmits this data to the server.

[0210] The server integrates received user information, weather conditions, meal history, and emotional data, and analyzes the data using data processing libraries such as Python's Pandas. Based on this analyzed data, it utilizes deep learning models such as TENSORFLOW® or PyTorch to generate meal suggestions that are appropriate to the user's emotional state and environment. These meal suggestions are optimized using a generative AI model and generated in a user-friendly format using natural language processing technology.

[0211] After meal suggestions are generated, the server sends the details back to the user's device. Simultaneously, it can integrate with APIs of delivery services such as Uber Eats and Grubhub to automatically order the suggested meal. This allows users to quickly receive meals tailored to their emotional state. For example, on a cold day when relaxation is desired, a menu including chicken and vegetable soup might be suggested and ordered immediately.

[0212] Specific example

[0213] A user logs in via their smartphone, and the application analyzes the day's weather and their fatigue level. Emotion recognition reads their tired expression and determines they need a relaxing meal. The server generates a suggestion for a nutritious soup and places an order through the Uber Eats platform. This entire process proceeds automatically without any user configuration.

[0214] Example of a prompt

[0215] "Consider the user's mood and the weather when suggesting today's meal menu."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The device uses its camera and microphone to capture the user's facial expressions and voice. This data is analyzed in real time through an emotion recognition module and output as data indicating the user's emotional state. Technologies such as Amazon Rekognition are used in this process.

[0219] Step 2:

[0220] The device collects the user's basic information (age, gender, family structure, etc.) and past meal history. This data is retrieved from a database in JSON format and sent to the server along with sentiment data.

[0221] Step 3:

[0222] The server retrieves weather information from weather information services such as OpenWeatherMap. This includes weather, temperature, and humidity for a specific area, and the data is received in JSON format via an API.

[0223] Step 4:

[0224] The server integrates the data collected in steps 1 through 3. It uses the Python Pandas library to cleanse, filter, and analyze the data. The integrated dataset is then used as input data for generating meal suggestions.

[0225] Step 5:

[0226] The server launches a generative AI model using TensorFlow or PyTorch to generate meal suggestions tailored to the user's emotional state based on the data. This process outputs the most suitable menu, taking into account nutritional balance and emotional data.

[0227] Step 6:

[0228] The server sends the generated meal suggestions to the user's terminal. Simultaneously, the meal details are passed to the API of a delivery service such as Uber Eats, and the food is automatically ordered. As a result, the user can instantly review the suggested menu and make changes if necessary.

[0229] Step 7:

[0230] On the user's terminal, the user inputs feedback on the suggested meal. This feedback is sent to the server and used to improve the accuracy of future suggestions. A retrainable generative AI model is used to improve the suggestions.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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".

[0247] In embodiments of the present invention, the system functions as a mechanism for exchanging information between a user terminal and a server and providing the user with appropriate meal suggestions. The user inputs basic information such as age, gender, and family structure, as well as recent meal details, through the terminal. The terminal collects climate data based on the input data and transmits all the information to the server.

[0248] Based on the received data, the server analyzes user information, climate information, and past meal history to generate meal suggestions that take nutritional balance into consideration. These meal suggestions include recommended nutrient intake based on the user's age and gender, as well as meal content tailored to climate conditions.

[0249] For example, if the user is a 30-year-old male who ate a lot of pasta for dinner the previous night, the server can recommend a high-protein, vegetable-rich meal for his next meal. Also, on a cold, rainy day, the server might consider suggesting a meal that includes a warming soup.

[0250] Meal suggestions generated by the server are sent to the terminal and presented to the user. The user can provide feedback on the presented menu and, if necessary, send more detailed requests. Based on this feedback, the server readjusts the meal suggestions and makes new suggestions that meet the user's needs.

[0251] In this way, the system aims to support a healthy and balanced diet by providing personalized menus for each user.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] Users enter basic information such as age, gender, and family structure into the input form on the device. In addition, they record and enter details of their recent meals into the device.

[0255] Step 2:

[0256] The device saves the entered user information and meal details to local storage. It also accesses an API via the internet to retrieve current weather data based on the user's location.

[0257] Step 3:

[0258] The device packages all the collected information (user information, meal history, weather data) and sends it to the server.

[0259] Step 4:

[0260] The server begins analyzing the received data. First, it calculates the recommended daily nutrient intake based on the user's age and gender.

[0261] Step 5:

[0262] The server analyzes weather information and sets criteria for determining what to eat that day based on the weather conditions. For example, it might consider a warm soup on a cold day and a refreshing salad on a hot day.

[0263] Step 6:

[0264] The server analyzes past meal history and assesses any nutritional imbalances. Based on this information, it identifies the nutrients that should be consumed in the next meal.

[0265] Step 7:

[0266] The server generates nutritionally balanced meal suggestions based on user information, climate information, and analysis of meal history.

[0267] Step 8:

[0268] The server sends the generated meal suggestions to the user's terminal.

[0269] Step 9:

[0270] The device displays the suggested menu to the user and provides a feedback input screen.

[0271] Step 10:

[0272] Users enter feedback on the suggested menu into their device and, if necessary, enter any further requests for changes or additions.

[0273] Step 11:

[0274] The terminal sends the user's feedback to the server.

[0275] Step 12:

[0276] Based on the feedback from the user, the server adjusts the meal suggestions as necessary and provides them to the user again.

[0277] (Example 1)

[0278] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0279] By providing a system that makes appropriate meal suggestions according to an individual's health status and preferences, it aims to enable the user to achieve a healthy and balanced diet. However, with conventional methods, it is difficult to make refined suggestions considering various factors, and there are particularly problems with individualized meal suggestions and their accuracy.

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

[0281] In this invention, the server includes a device for receiving user information, a device for receiving weather information, and a device for analyzing past intake information. Thereby, it becomes possible to make personalized meal suggestions according to the individual needs of the user and support a healthy diet.

[0282] "User information" refers to information related to an individual necessary for meal suggestions, such as the user's age, gender, family composition, and past meal contents.

[0283] "Weather information" refers to data related to the current weather and temperature at the user's geographical location.

[0284] "Past intake information" refers to historical data on foods and nutrients that the user has consumed in meals previously.

[0285] "Nutritional balance" refers to the appropriate proportion of various nutrients necessary to maintain a healthy diet.

[0286] A "generative AI model" is a model that applies technologies of machine learning and artificial intelligence and is used to generate personalized meal suggestions.

[0287] A "user terminal" is a digital device through which a user inputs information and receives meal suggestions.

[0288] A "response" refers to feedback or requests from the user regarding the generated meal suggestions.

[0289] To implement this invention, a system using a user terminal and a server is constructed. First, the user uses the user terminal to input necessary user information such as their age, gender, family composition, and recent meal content. The user terminal is equipped with application software that provides an interface to enable these information inputs.

[0290] Next, the terminal transmits the input information to the server via a computer network. At this time, the terminal also uses an API to obtain weather information based on the current geographical location and transmits all the data to the server together. For example, the API data obtained from the weather information service is utilized as additional information.

[0291] The server performs data analysis using the generative AI model while comparing with past intake information based on the received user information and weather information. Through this analysis, meal suggestions corresponding to the user's individual nutritional needs are generated. In this process, a machine learning model is utilized to learn patterns from various data and provide a menu that supports the user's healthy diet.

[0292] As a concrete example, the server is provided with the following information as a prompt: "30-year-old male, had pasta for dinner yesterday, it's raining and cold today." Based on this information, the server generates a meal suggestion: "A meal high in protein with warm soup." The generated suggestion is then sent back to the user's terminal via the network and presented to the user. The user reviews the suggestion and provides feedback, which allows the server to further improve it and incorporate the feedback into future suggestions.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The user enters their age, gender, family structure, and recent meal details into the user terminal. The entered information is temporarily stored in the terminal's database. In this step, there is input from the user, and as a result, a user information dataset is generated as output. The terminal checks the integrity of the information and prepares for the next step.

[0296] Step 2:

[0297] The device collects local weather information via the internet based on the user information entered. The API used here is from a weather service provider and retrieves temperature and weather conditions. Based on the user information as input, an API request is sent, and the weather information is returned to the device as output. This information is integrated with the user information and prepared for transmission to the server.

[0298] Step 3:

[0299] The terminal transmits integrated user information and weather data to the server. This information is delivered to the server via the internet and treated as a single data package. All user information and weather data are used as input, and the data package is generated as output that is transferred to the server.

[0300] Step 4:

[0301] The server receives the transmitted data package and performs analysis using the generative AI model. When performing the analysis, past intake information and other related data are also referenced. The server takes in the data package as input, the generative AI model calculates a meal recommendation through analysis, and generates the result as output. In this process, the nutrient balance evaluation and the individual's preference pattern are particularly considered.

[0302] Step 5:

[0303] The server transmits the generated meal recommendation to the user terminal. The sent recommendation is clearly displayed to the user on the terminal side. The input here is the data of the meal recommendation, and the output is the visualized recommendation displayed on the user terminal.

[0304] Step 6:

[0305] The user checks the proposed meal menu and sends feedback via the terminal. The feedback includes acceptance of the proposal or a request for modification, and is further used as input to improve the proposal. The output is the feedback data from the user.

[0306] Step 7:

[0307] The server analyzes the received feedback and readjusts the generative AI model. Also, based on the feedback, a new meal recommendation is created to improve the accuracy of the model. Using the feedback data as input, an improved next meal recommendation is generated as output. Through this loop process, the accuracy of the recommendation gradually improves.

[0308] (Application Example 1)

[0309] 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."

[0310] In modern life, proposing nutritionally balanced meals tailored to individual health conditions and lifestyles, and then efficiently providing them, is a crucial challenge. In particular, there is a need for a system that allows for easy selection of healthy meals in busy daily lives. However, conventional systems have struggled to provide meal suggestions that take into account climatic conditions and individual user preferences. Furthermore, a lack of means to directly supply the suggested meals has been a problem.

[0311] 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.

[0312] In this invention, the server includes means for receiving user information, means for receiving climate information, means for analyzing past meal history, and means for coordinating with affiliated food and beverage services to supply suggested meals. This makes it possible to suggest nutritionally balanced meals based on the user's individual conditions and to supply them efficiently.

[0313] "Means for receiving user information" refers to a system for obtaining individual information from users, such as age, gender, family structure, and recent dietary habits.

[0314] A "means for receiving climate information" refers to a mechanism that provides an interface for obtaining current weather conditions.

[0315] "Methods for analyzing past meal history" refers to a process for analyzing a user's past diet and evaluating its nutritional balance.

[0316] "A means of generating meal suggestions that take nutritional balance into consideration" refers to an algorithm that plans meal content to support a healthy lifestyle based on acquired user information, climate information, and meal history.

[0317] "Means for transmitting generated meal suggestions to the user device" refers to communication technology for notifying the user's terminal of the planned meal content.

[0318] "A means of supplying suggested meals in cooperation with partner food and beverage services" refers to a system that collaborates with external food and beverage delivery services to deliver suggested meals to users as actual dishes.

[0319] "Means of receiving feedback" refers to an interface for users to input evaluations and opinions on the suggested meals.

[0320] "Means for regenerating suggestions" refers to the process of updating meal suggestions by taking user feedback into consideration.

[0321] One embodiment of this invention is a system that provides personalized meal suggestions based on user information and climate conditions, and streamlines the process of providing them.

[0322] On the user's terminal, a dedicated application runs, allowing the user to input necessary information such as their age, gender, family structure, and recent diet. The terminal compiles this information and sends it to a server via the internet. Furthermore, the terminal utilizes an API to obtain local climate data and also sends that data to the server.

[0323] The server performs detailed data analysis based on the received user information and weather conditions. This analysis utilizes algorithms built in Python or R programming environments. Using this information, the server performs calculations to generate meal suggestions that take into account the individual nutritional balance of each user. These suggestions include nutritional intake guidelines based on age and gender, as well as menu suggestions tailored to temperature and weather.

[0324] The generated meal suggestions are quickly sent to the user's terminal and notified to the user. The user sends a response to the suggested meal from their terminal, and the server readjusts the suggestions based on that feedback. In this way, the suggestions are more accurately adapted to the user's preferences and wishes.

[0325] The suggested meals are delivered as actual dishes through partnerships with external food and beverage services. This integration utilizes the food and beverage service's API, allowing users to complete their orders within the application.

[0326] As a concrete example, let's assume the user is a 30-year-old man who ate high-calorie ramen last night. Because it's a rainy and cold day, the server suggests a high-protein, low-calorie hot pot dish, which can then be ordered through a local delivery service.

[0327] An example of a prompt message is as follows:

[0328] "Given that the user is 30 years old, male, ate ramen for dinner yesterday, and the weather today is cold and rainy, please suggest an optimally balanced dinner menu. Please include warming dishes and high-protein options."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] The user terminal receives input from the user, including age, gender, family structure, and recent meal history. Based on this input, the terminal calls a weather API to obtain climate information. This results in a combination of the user's personal data and the latest climate data.

[0332] Step 2:

[0333] The terminal transmits collected user information and climate information to the server via the internet. This data transfer allows the server to obtain all the information necessary for analysis.

[0334] Step 3:

[0335] The server analyzes received user information, climate information, and past dietary data. Using statistical analysis libraries in Python or R, it evaluates the user's recent nutritional intake and determines necessary nutrients. This constitutes data processing and calculation.

[0336] Step 4:

[0337] The server uses a generative AI model to generate optimal meal suggestions based on the analysis results. It uses prompts to create specific menu suggestions tailored to the user's conditions. An example of such a prompt would be, "The user is 30 years old, male, ate ramen for dinner yesterday, and today's weather is cold and rainy. Please suggest an optimally nutritionally balanced dinner menu."

[0338] Step 5:

[0339] The server sends the generated meal suggestions back to the user's device. This allows the user to view the suggested menu on their device.

[0340] Step 6:

[0341] Users provide feedback on the suggested meal. For example, they send responses such as "I don't like this dish" or "I'd like more vegetables" from their device to the server. The server receives this as input and re-analyzes it.

[0342] Step 7:

[0343] The server regenerates meal suggestions based on user feedback and sends the new suggestions to the device. This process allows for readjustment of suggestions to suit the user's preferences and circumstances.

[0344] Step 8:

[0345] Users can order the suggested meals through external food and beverage services. The terminal calls the API of the partner food and beverage service to process the order and ensure that the user's meal is delivered reliably.

[0346] 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.

[0347] This invention provides an innovative system that incorporates an emotion recognition engine to offer meal suggestions that take the user's emotions into account. The user inputs basic personal information (age, gender, family structure, etc.) and recent meal details from a terminal. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotion data.

[0348] The device sends the collected information, along with this emotional data, to the server. The server integrates and analyzes user information, weather information, meal history, and emotional data. For example, if the user is feeling stressed, it will generate meal suggestions that include relaxing herbal tea. It will also consider appropriate meal types based on the weather and temperature and reflect this in the suggestions.

[0349] For example, if a user is feeling fatigued and the outside temperature is low, the server can generate a high-protein, nutritionally balanced menu centered around chicken and vegetable soup. This menu suggestion incorporates elements that promote relaxation and aims to improve the user's mood.

[0350] The generated meal suggestions are delivered to the user via a terminal. The user can provide feedback on the menu and request alternatives based on their feelings and preferences. The server also analyzes this feedback data to provide more personalized suggestions for the next meal.

[0351] This invention constitutes a system that effectively supports the improvement of users' health and mood through personalized meal suggestions based on emotion recognition.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] The user enters their age, gender, family structure, and recent meal details into the device. The device temporarily stores this entered information.

[0355] Step 2:

[0356] The device uses its built-in camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state using an emotion recognition sensor.

[0357] Step 3:

[0358] The device collects acquired user information, recent meal details, emotional data, and weather information, packages all the data, and sends it to the server.

[0359] Step 4:

[0360] The server analyzes the received data and calculates the recommended daily nutrient intake based on the user's age and gender.

[0361] Step 5:

[0362] The server uses weather information to determine the type of meal appropriate for the day's climate. For example, on cold days, it considers a warm meal.

[0363] Step 6:

[0364] The server analyzes past meal history and identifies nutrients to correct nutritional imbalances.

[0365] Step 7:

[0366] The server analyzes emotional data and identifies and incorporates ingredients that have a relaxing effect and culinary elements that improve emotions.

[0367] Step 8:

[0368] The server combines candidate ingredients to generate nutritionally balanced meal suggestions that improve the user's health and mood.

[0369] Step 9:

[0370] The server sends the generated meal suggestions to the terminal.

[0371] Step 10:

[0372] The terminal displays the suggested menu to the user and provides an interface for requesting feedback.

[0373] Step 11:

[0374] Users input feedback on the presented menu and any elements they wish to change into the terminal.

[0375] Step 12:

[0376] The device sends user feedback to the server, which then re-analyzes the data and readjusts meal suggestions as needed.

[0377] (Example 2)

[0378] 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".

[0379] In recent years, in order to efficiently manage individual health, it has become necessary to suggest meals tailored to the user's emotional state and individual needs. However, conventional systems do not adequately consider the user's emotional state, and there are limitations to realizing personalized meal suggestions. This invention aims to support health maintenance and mental stability by enabling personalized meal suggestions that take the user's emotional state into consideration.

[0380] 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.

[0381] In this invention, the server includes means for receiving the user's basic information and emotional data, means for receiving climate information, and means for analyzing past meal information to analyze the emotional state. This makes it possible to suggest meals that take into account the user's emotional state and nutritional balance.

[0382] "User basic information" refers to fundamental attributes of an individual, such as age, gender, and family structure, and is the basic data that allows the system to individually identify users and make appropriate suggestions.

[0383] "Emotional data" refers to data that expresses a user's emotional state based on information obtained through the user's facial expressions, voice tone, and other means.

[0384] "Climate information" refers to information about environmental factors such as weather and temperature, and is data that is considered in order to provide meal suggestions suitable for the user.

[0385] "Past meal information" refers to data about the meals a user has previously consumed, and is used to understand their meal selection patterns and nutritional status.

[0386] "Meal suggestions" are personalized, nutritionally balanced menu recommendations created based on the user's basic information, emotional data, climate information, and past eating history.

[0387] A "user device" is a terminal used by a user to input information or receive suggestions, and is equipped with sensors for acquiring emotional data.

[0388] "Feedback" refers to the evaluations and opinions that users give regarding suggested meals, and it is an important source of information for making future suggestions more personalized.

[0389] "Analysis" is a series of processes that use collected data to analyze the user's emotional state and eating habits, and then generate appropriate meal suggestions.

[0390] This invention is a system that provides meal suggestions that take the user's emotions into consideration, and is implemented as follows. The system consists of the interaction of the user, a terminal, and a server.

[0391] The user enters their basic information (age, gender, family structure, etc.) and past meal history through the device. The device is equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotional data. This measures the user's current emotional state and records it as data.

[0392] The device transmits acquired basic information, emotional data, and past meal information to the server. The server integrates this information, along with climate information acquired from external sources, and performs data analysis. The data analysis uses an emotional recognition engine and a generative AI model to generate meal suggestions that take into account the user's emotional state and nutritional balance.

[0393] For example, suppose a user inputs, "I'm a man in my 30s, and I've been feeling stressed at work lately. What kind of meal would you suggest for a cold day?" In this case, the system can determine the user's stress level from emotional data and, taking temperature information into consideration, suggest a relaxing herbal tea and a nutritionally balanced chicken and vegetable soup.

[0394] The generated meal suggestions are provided to the user via a terminal. The user inputs their thoughts and preferences as feedback on the provided suggestions into the terminal. This feedback information is sent back to the server and used to improve the accuracy of future meal suggestions.

[0395] Through this process, users can receive meal suggestions tailored to their emotional state and individual needs, enabling them to maintain their health and improve their mood.

[0396] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0397] Step 1:

[0398] Users enter basic information (age, gender, family structure, etc.) and past meal history using their device. The entered information is saved as initial data within the device. This prepares individual basic data.

[0399] Step 2:

[0400] The user utilizes the camera and microphone built into the device, allowing the emotion recognition sensor to capture the user's facial expressions and voice tone. The acquired data is analyzed by an emotion recognition algorithm to generate the user's emotion data. This emotion data indicates the user's current emotional state.

[0401] Step 3:

[0402] The device sends the basic information and meal information obtained in Step 1, along with the emotional data generated in Step 2, to the server. Data transmission takes place via a secure communication protocol, and a dataset that can be analyzed is constructed on the server side.

[0403] Step 4:

[0404] The server integrates received user information, emotional data, and dietary information with climate information obtained from external sources. Data analysis is then performed on the integrated dataset using generative AI models and analysis engines. Specifically, it generates nutritionally balanced meal suggestions that take into account the user's emotional state and climate conditions.

[0405] Step 5:

[0406] Meal suggestions generated by the server are sent to the terminal. The terminal displays this information to the user, providing them with specific menu details. The user can then make a meal selection based on these suggestions.

[0407] Step 6:

[0408] Users input feedback on the displayed meal suggestions via their device. This feedback includes their impressions and changes in their emotional state regarding the suggestions. This contributes to improving the accuracy of future suggestions.

[0409] Step 7:

[0410] The device sends the input feedback to the server. The server analyzes this feedback and uses it to improve the next meal suggestion, taking into account the user's emotional state, thereby providing more suitable suggestions.

[0411] (Application Example 2)

[0412] 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."

[0413] Conventional meal suggestion systems fail to adequately consider the user's emotions and mood, merely providing meal suggestions based on nutritional balance and basic personal information. As a result, optimal meal suggestions tailored to the user's psychological state and external environment are not obtained, limiting user satisfaction and the effectiveness of health improvement. This invention aims to solve these problems and provide more personalized meal suggestions that take the user's emotional state into consideration.

[0414] 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.

[0415] In this invention, the server includes means for receiving user information, means for receiving weather condition information, means for analyzing past meal history, means equipped with an emotion recognition module for analyzing facial expressions and voice tone as emotion data, and means for automatically placing orders by coordinating the generated meal suggestions with a delivery service. This enables the generation of optimal meal suggestions tailored to the user's emotions and rapid delivery.

[0416] "User information" refers to basic data about an individual, including age, gender, and family structure.

[0417] "Weather condition information" refers to environmental data such as weather, temperature, and humidity for a specific region, and is obtained from external weather information service providers.

[0418] "Past meal history" refers to a record of meals the user has consumed to date, including information such as individual foods, nutrients, and the date and time of consumption.

[0419] "Emotional data" refers to data that indicates a user's psychological state, obtained by analyzing their facial expressions and voice tone, and expresses emotions such as joy, anger, and sadness.

[0420] An "emotion recognition module" is a program or device that analyzes a user's facial expressions and voice tone and generates emotion data based on that analysis.

[0421] "Meal suggestions" refer to suggestions that include menus and ingredients of meals recommended to the user based on specific conditions.

[0422] A "delivery service" is a service that provides logistics functions to deliver meals ordered by users to a specified location.

[0423] The system implementing this invention consists of a user terminal equipped with an emotion recognition sensor and a data processing server in the cloud. The user terminal is equipped with a camera and microphone, which can be used to capture the user's facial expressions and voice tone in real time. This data is analyzed as emotion data using emotion recognition software such as Amazon Rekognition. The terminal also collects the user's basic information and past meal history, and comprehensively transmits this data to the server.

[0424] The server integrates received user information, weather conditions, meal history, and sentiment data, and analyzes the data using data processing libraries such as Python's Pandas. Based on this analyzed data, it utilizes deep learning models such as TensorFlow or PyTorch to generate meal suggestions that are appropriate to the user's emotional state and environment. These meal suggestions are optimized using a generative AI model and generated in a user-friendly format using natural language processing technology.

[0425] After meal suggestions are generated, the server sends the details back to the user's device. Simultaneously, it can integrate with APIs of delivery services such as Uber Eats and Grubhub to automatically order the suggested meal. This allows users to quickly receive meals tailored to their emotional state. For example, on a cold day when relaxation is desired, a menu including chicken and vegetable soup might be suggested and ordered immediately.

[0426] Specific example

[0427] A user logs in via their smartphone, and the application analyzes the day's weather and their fatigue level. Emotion recognition reads their tired expression and determines they need a relaxing meal. The server generates a suggestion for a nutritious soup and places an order through the Uber Eats platform. This entire process proceeds automatically without any user configuration.

[0428] Example of a prompt

[0429] "Consider the user's mood and the weather when suggesting today's meal menu."

[0430] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0431] Step 1:

[0432] The device uses its camera and microphone to capture the user's facial expressions and voice. This data is analyzed in real time through an emotion recognition module and output as data indicating the user's emotional state. Technologies such as Amazon Rekognition are used in this process.

[0433] Step 2:

[0434] The device collects the user's basic information (age, gender, family structure, etc.) and past meal history. This data is retrieved from a database in JSON format and sent to the server along with sentiment data.

[0435] Step 3:

[0436] The server retrieves weather information from weather information services such as OpenWeatherMap. This includes weather, temperature, and humidity for a specific area, and the data is received in JSON format via an API.

[0437] Step 4:

[0438] The server integrates the data collected in steps 1 through 3. It uses the Python Pandas library to cleanse, filter, and analyze the data. The integrated dataset is then used as input data for generating meal suggestions.

[0439] Step 5:

[0440] The server launches a generative AI model using TensorFlow or PyTorch to generate meal suggestions tailored to the user's emotional state based on the data. This process outputs the most suitable menu, taking into account nutritional balance and emotional data.

[0441] Step 6:

[0442] The server sends the generated meal suggestions to the user's terminal. Simultaneously, the meal details are passed to the API of a delivery service such as Uber Eats, and the food is automatically ordered. As a result, the user can instantly review the suggested menu and make changes if necessary.

[0443] Step 7:

[0444] On the user's terminal, the user inputs feedback on the suggested meal. This feedback is sent to the server and used to improve the accuracy of future suggestions. A retrainable generative AI model is used to improve the suggestions.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] In embodiments of the present invention, the system functions as a mechanism for exchanging information between a user terminal and a server and providing the user with appropriate meal suggestions. The user inputs basic information such as age, gender, and family structure, as well as recent meal details, through the terminal. The terminal collects climate data based on the input data and transmits all the information to the server.

[0462] Based on the received data, the server analyzes user information, climate information, and past meal history to generate meal suggestions that take nutritional balance into consideration. These meal suggestions include recommended nutrient intake based on the user's age and gender, as well as meal content tailored to climate conditions.

[0463] For example, if the user is a 30-year-old male who ate a lot of pasta for dinner the previous night, the server can recommend a high-protein, vegetable-rich meal for his next meal. Also, on a cold, rainy day, the server might consider suggesting a meal that includes a warming soup.

[0464] Meal suggestions generated by the server are sent to the terminal and presented to the user. The user can provide feedback on the presented menu and, if necessary, send more detailed requests. Based on this feedback, the server readjusts the meal suggestions and makes new suggestions that meet the user's needs.

[0465] In this way, the system aims to support a healthy and balanced diet by providing personalized menus for each user.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] Users enter basic information such as age, gender, and family structure into the input form on the device. In addition, they record and enter details of their recent meals into the device.

[0469] Step 2:

[0470] The device saves the entered user information and meal details to local storage. It also accesses an API via the internet to retrieve current weather data based on the user's location.

[0471] Step 3:

[0472] The device packages all the collected information (user information, meal history, weather data) and sends it to the server.

[0473] Step 4:

[0474] The server begins analyzing the received data. First, it calculates the recommended daily nutrient intake based on the user's age and gender.

[0475] Step 5:

[0476] The server analyzes weather information and sets criteria for determining what to eat that day based on the weather conditions. For example, it might consider a warm soup on a cold day and a refreshing salad on a hot day.

[0477] Step 6:

[0478] The server analyzes past meal history and assesses any nutritional imbalances. Based on this information, it identifies the nutrients that should be consumed in the next meal.

[0479] Step 7:

[0480] The server generates nutritionally balanced meal suggestions based on user information, climate information, and analysis of meal history.

[0481] Step 8:

[0482] The server sends the generated meal suggestions to the user's terminal.

[0483] Step 9:

[0484] The device displays the suggested menu to the user and provides a feedback input screen.

[0485] Step 10:

[0486] Users enter feedback on the suggested menu into their device and, if necessary, enter any further requests for changes or additions.

[0487] Step 11:

[0488] The device sends user feedback to the server.

[0489] Step 12:

[0490] Based on user feedback, the server readjusts meal suggestions as needed and provides them to the user again.

[0491] (Example 1)

[0492] 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."

[0493] The aim is to enable users to achieve a healthy and balanced diet by providing a system that offers appropriate meal suggestions tailored to individual health conditions and preferences. However, conventional methods make it difficult to provide precise suggestions that take various factors into account, and there are particular challenges in personalized meal suggestions and their accuracy.

[0494] 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.

[0495] In this invention, the server includes a device for receiving user information, a device for receiving weather information, and a device for analyzing past intake information. This makes it possible to provide personalized meal suggestions tailored to the individual needs of the user and support a healthy diet.

[0496] "User information" refers to personal information necessary for meal suggestions, such as the user's age, gender, family structure, and past meal history.

[0497] "Weather information" refers to data related to the current weather and temperature at the user's geographical location.

[0498] "Past intake information" refers to historical data about the foods and nutrients that the user has previously consumed in meals.

[0499] "Nutritional balance" refers to the appropriate proportion of various nutrients necessary to maintain a healthy diet.

[0500] A "generative AI model" is a model that applies machine learning and artificial intelligence techniques to generate personalized meal suggestions.

[0501] A "user terminal" is a digital device used by users to input information and receive meal suggestions.

[0502] "Responses" refer to user feedback and requests regarding the generated meal suggestions.

[0503] To implement this invention, a system is constructed using a user terminal and a server. First, the user uses the user terminal to input necessary user information such as their age, gender, family structure, and recent meal details. The user terminal is equipped with application software that provides an interface to enable this information input.

[0504] Next, the terminal transmits the entered information to the server via the computer network. At this time, the terminal also obtains weather information based on its current geographic location using an API, and transmits all the data together to the server. For example, API data obtained from a weather information service is used as supplementary information.

[0505] The server uses a generative AI model to analyze data based on received user information and weather information, comparing it with past intake data. This analysis generates meal suggestions tailored to the user's individual nutritional needs. This process utilizes machine learning models to learn patterns from various data, enabling the provision of menus that support the user's healthy eating habits.

[0506] As a concrete example, the server is provided with the following information as a prompt: "30-year-old male, had pasta for dinner yesterday, it's raining and cold today." Based on this information, the server generates a meal suggestion: "A meal high in protein with warm soup." The generated suggestion is then sent back to the user's terminal via the network and presented to the user. The user reviews the suggestion and provides feedback, which allows the server to further improve it and incorporate the feedback into future suggestions.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] The user enters their age, gender, family structure, and recent meal details into the user terminal. The entered information is temporarily stored in the terminal's database. In this step, there is input from the user, and as a result, a user information dataset is generated as output. The terminal checks the integrity of the information and prepares for the next step.

[0510] Step 2:

[0511] The device collects local weather information via the internet based on the user information entered. The API used here is from a weather service provider and retrieves temperature and weather conditions. Based on the user information as input, an API request is sent, and the weather information is returned to the device as output. This information is integrated with the user information and prepared for transmission to the server.

[0512] Step 3:

[0513] The terminal transmits integrated user information and weather data to the server. This information is delivered to the server via the internet and treated as a single data package. All user information and weather data are used as input, and the data package is generated as output that is transferred to the server.

[0514] Step 4:

[0515] The server receives the transmitted data package and performs analysis using a generative AI model. During the analysis, past intake information and other relevant data are also referenced. The server takes the data package as input, the generative AI model calculates meal suggestions through analysis, and generates the results as output. This process particularly considers the balance of nutrients and individual preference patterns.

[0516] Step 5:

[0517] The server sends the generated meal suggestions to the user's terminal. The suggestions are displayed to the user in an easy-to-understand format on the terminal. The input here is the meal suggestion data, and the output is the visualized suggestions displayed on the user's terminal.

[0518] Step 6:

[0519] Users review the suggested meal menus and submit feedback via their device. This feedback includes acceptance of the suggestions or requests for modifications, and is used as input to further improve the suggestions. The output is user feedback data.

[0520] Step 7:

[0521] The server analyzes the received feedback and readjusts the generated AI model. It also creates new meal suggestions based on the feedback, improving the model's accuracy. Using the feedback data as input, an improved next meal suggestion is generated as output. This loop process gradually improves the accuracy of the suggestions.

[0522] (Application Example 1)

[0523] 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."

[0524] In modern life, proposing nutritionally balanced meals tailored to individual health conditions and lifestyles, and then efficiently providing them, is a crucial challenge. In particular, there is a need for a system that allows for easy selection of healthy meals in busy daily lives. However, conventional systems have struggled to provide meal suggestions that take into account climatic conditions and individual user preferences. Furthermore, a lack of means to directly supply the suggested meals has been a problem.

[0525] 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.

[0526] In this invention, the server includes means for receiving user information, means for receiving climate information, means for analyzing past meal history, and means for coordinating with affiliated food and beverage services to supply suggested meals. This makes it possible to suggest nutritionally balanced meals based on the user's individual conditions and to supply them efficiently.

[0527] "Means for receiving user information" refers to a system for obtaining individual information from users, such as age, gender, family structure, and recent dietary habits.

[0528] A "means for receiving climate information" refers to a mechanism that provides an interface for obtaining current weather conditions.

[0529] "Methods for analyzing past meal history" refers to a process for analyzing a user's past diet and evaluating its nutritional balance.

[0530] "A means of generating meal suggestions that take nutritional balance into consideration" refers to an algorithm that plans meal content to support a healthy lifestyle based on acquired user information, climate information, and meal history.

[0531] "Means for transmitting generated meal suggestions to the user device" refers to communication technology for notifying the user's terminal of the planned meal content.

[0532] "A means of supplying suggested meals in cooperation with partner food and beverage services" refers to a system that collaborates with external food and beverage delivery services to deliver suggested meals to users as actual dishes.

[0533] "Means of receiving feedback" refers to an interface for users to input evaluations and opinions on the suggested meals.

[0534] "Means for regenerating suggestions" refers to the process of updating meal suggestions by taking user feedback into consideration.

[0535] One embodiment of this invention is a system that provides personalized meal suggestions based on user information and climate conditions, and streamlines the process of providing them.

[0536] On the user's terminal, a dedicated application runs, allowing the user to input necessary information such as their age, gender, family structure, and recent diet. The terminal compiles this information and sends it to a server via the internet. Furthermore, the terminal utilizes an API to obtain local climate data and also sends that data to the server.

[0537] The server performs detailed data analysis based on the received user information and weather conditions. This analysis utilizes algorithms built in Python or R programming environments. Using this information, the server performs calculations to generate meal suggestions that take into account the individual nutritional balance of each user. These suggestions include nutritional intake guidelines based on age and gender, as well as menu suggestions tailored to temperature and weather.

[0538] The generated meal suggestions are quickly sent to the user's terminal and notified to the user. The user sends a response to the suggested meal from their terminal, and the server readjusts the suggestions based on that feedback. In this way, the suggestions are more accurately adapted to the user's preferences and wishes.

[0539] The suggested meals are delivered as actual dishes through partnerships with external food and beverage services. This integration utilizes the food and beverage service's API, allowing users to complete their orders within the application.

[0540] As a concrete example, let's assume the user is a 30-year-old man who ate high-calorie ramen last night. Because it's a rainy and cold day, the server suggests a high-protein, low-calorie hot pot dish, which can then be ordered through a local delivery service.

[0541] An example of a prompt message is as follows:

[0542] "Given that the user is 30 years old, male, ate ramen for dinner yesterday, and the weather today is cold and rainy, please suggest an optimally balanced dinner menu. Please include warming dishes and high-protein options."

[0543] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0544] Step 1:

[0545] The user terminal receives input from the user, including age, gender, family structure, and recent meal history. Based on this input, the terminal calls a weather API to obtain climate information. This results in a combination of the user's personal data and the latest climate data.

[0546] Step 2:

[0547] The terminal transmits collected user information and climate information to the server via the internet. This data transfer allows the server to obtain all the information necessary for analysis.

[0548] Step 3:

[0549] The server analyzes received user information, climate information, and past dietary data. Using statistical analysis libraries in Python or R, it evaluates the user's recent nutritional intake and determines necessary nutrients. This constitutes data processing and calculation.

[0550] Step 4:

[0551] The server uses a generative AI model to generate optimal meal suggestions based on the analysis results. It uses prompts to create specific menu suggestions tailored to the user's conditions. An example of such a prompt would be, "The user is 30 years old, male, ate ramen for dinner yesterday, and today's weather is cold and rainy. Please suggest an optimally nutritionally balanced dinner menu."

[0552] Step 5:

[0553] The server sends the generated meal suggestions back to the user's device. This allows the user to view the suggested menu on their device.

[0554] Step 6:

[0555] Users provide feedback on the suggested meal. For example, they send responses such as "I don't like this dish" or "I'd like more vegetables" from their device to the server. The server receives this as input and re-analyzes it.

[0556] Step 7:

[0557] The server regenerates meal suggestions based on user feedback and sends the new suggestions to the device. This process allows for readjustment of suggestions to suit the user's preferences and circumstances.

[0558] Step 8:

[0559] Users can order the suggested meals through external food and beverage services. The terminal calls the API of the partner food and beverage service to process the order and ensure that the user's meal is delivered reliably.

[0560] 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.

[0561] This invention provides an innovative system that incorporates an emotion recognition engine to offer meal suggestions that take the user's emotions into account. The user inputs basic personal information (age, gender, family structure, etc.) and recent meal details from a terminal. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotion data.

[0562] The device sends the collected information, along with this emotional data, to the server. The server integrates and analyzes user information, weather information, meal history, and emotional data. For example, if the user is feeling stressed, it will generate meal suggestions that include relaxing herbal tea. It will also consider appropriate meal types based on the weather and temperature and reflect this in the suggestions.

[0563] For example, if a user is feeling fatigued and the outside temperature is low, the server can generate a high-protein, nutritionally balanced menu centered around chicken and vegetable soup. This menu suggestion incorporates elements that promote relaxation and aims to improve the user's mood.

[0564] The generated meal suggestions are delivered to the user via a terminal. The user can provide feedback on the menu and request alternatives based on their feelings and preferences. The server also analyzes this feedback data to provide more personalized suggestions for the next meal.

[0565] This invention constitutes a system that effectively supports the improvement of users' health and mood through personalized meal suggestions based on emotion recognition.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user enters their age, gender, family structure, and recent meal details into the device. The device temporarily stores this entered information.

[0569] Step 2:

[0570] The device uses its built-in camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state using an emotion recognition sensor.

[0571] Step 3:

[0572] The device collects acquired user information, recent meal details, emotional data, and weather information, packages all the data, and sends it to the server.

[0573] Step 4:

[0574] The server analyzes the received data and calculates the recommended daily nutrient intake based on the user's age and gender.

[0575] Step 5:

[0576] The server uses weather information to determine the type of meal appropriate for the day's climate. For example, on cold days, it considers a warm meal.

[0577] Step 6:

[0578] The server analyzes past meal history and identifies nutrients to correct nutritional imbalances.

[0579] Step 7:

[0580] The server analyzes emotional data and identifies and incorporates ingredients that have a relaxing effect and culinary elements that improve emotions.

[0581] Step 8:

[0582] The server combines candidate ingredients to generate nutritionally balanced meal suggestions that improve the user's health and mood.

[0583] Step 9:

[0584] The server sends the generated meal suggestions to the terminal.

[0585] Step 10:

[0586] The terminal displays the suggested menu to the user and provides an interface for requesting feedback.

[0587] Step 11:

[0588] Users input feedback on the presented menu and any elements they wish to change into the terminal.

[0589] Step 12:

[0590] The device sends user feedback to the server, which then re-analyzes the data and readjusts meal suggestions as needed.

[0591] (Example 2)

[0592] 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."

[0593] In recent years, in order to efficiently manage individual health, it has become necessary to suggest meals tailored to the user's emotional state and individual needs. However, conventional systems do not adequately consider the user's emotional state, and there are limitations to realizing personalized meal suggestions. This invention aims to support health maintenance and mental stability by enabling personalized meal suggestions that take the user's emotional state into consideration.

[0594] 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.

[0595] In this invention, the server includes means for receiving the user's basic information and emotional data, means for receiving climate information, and means for analyzing past meal information to analyze the emotional state. This makes it possible to suggest meals that take into account the user's emotional state and nutritional balance.

[0596] "User basic information" refers to fundamental attributes of an individual, such as age, gender, and family structure, and is the basic data that allows the system to individually identify users and make appropriate suggestions.

[0597] "Emotional data" refers to data that expresses a user's emotional state based on information obtained through the user's facial expressions, voice tone, and other means.

[0598] "Climate information" refers to information about environmental factors such as weather and temperature, and is data that is considered in order to provide meal suggestions suitable for the user.

[0599] "Past meal information" refers to data about the meals a user has previously consumed, and is used to understand their meal selection patterns and nutritional status.

[0600] "Meal suggestions" are personalized, nutritionally balanced menu recommendations created based on the user's basic information, emotional data, climate information, and past eating history.

[0601] A "user device" is a terminal used by a user to input information or receive suggestions, and is equipped with sensors for acquiring emotional data.

[0602] "Feedback" refers to the evaluations and opinions that users give regarding suggested meals, and it is an important source of information for making future suggestions more personalized.

[0603] "Analysis" is a series of processes that use collected data to analyze the user's emotional state and eating habits, and then generate appropriate meal suggestions.

[0604] This invention is a system that provides meal suggestions that take the user's emotions into consideration, and is implemented as follows. The system consists of the interaction of the user, a terminal, and a server.

[0605] The user enters their basic information (age, gender, family structure, etc.) and past meal history through the device. The device is equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotional data. This measures the user's current emotional state and records it as data.

[0606] The device transmits acquired basic information, emotional data, and past meal information to the server. The server integrates this information, along with climate information acquired from external sources, and performs data analysis. The data analysis uses an emotional recognition engine and a generative AI model to generate meal suggestions that take into account the user's emotional state and nutritional balance.

[0607] For example, suppose a user inputs, "I'm a man in my 30s, and I've been feeling stressed at work lately. What kind of meal would you suggest for a cold day?" In this case, the system can determine the user's stress level from emotional data and, taking temperature information into consideration, suggest a relaxing herbal tea and a nutritionally balanced chicken and vegetable soup.

[0608] The generated meal suggestions are provided to the user via a terminal. The user inputs their thoughts and preferences as feedback on the provided suggestions into the terminal. This feedback information is sent back to the server and used to improve the accuracy of future meal suggestions.

[0609] Through this process, users can receive meal suggestions tailored to their emotional state and individual needs, enabling them to maintain their health and improve their mood.

[0610] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0611] Step 1:

[0612] Users enter basic information (age, gender, family structure, etc.) and past meal history using their device. The entered information is saved as initial data within the device. This prepares individual basic data.

[0613] Step 2:

[0614] The user utilizes the camera and microphone built into the device, allowing the emotion recognition sensor to capture the user's facial expressions and voice tone. The acquired data is analyzed by an emotion recognition algorithm to generate the user's emotion data. This emotion data indicates the user's current emotional state.

[0615] Step 3:

[0616] The device sends the basic information and meal information obtained in Step 1, along with the emotional data generated in Step 2, to the server. Data transmission takes place via a secure communication protocol, and a dataset that can be analyzed is constructed on the server side.

[0617] Step 4:

[0618] The server integrates received user information, emotional data, and dietary information with climate information obtained from external sources. Data analysis is then performed on the integrated dataset using generative AI models and analysis engines. Specifically, it generates nutritionally balanced meal suggestions that take into account the user's emotional state and climate conditions.

[0619] Step 5:

[0620] Meal suggestions generated by the server are sent to the terminal. The terminal displays this information to the user, providing them with specific menu details. The user can then make a meal selection based on these suggestions.

[0621] Step 6:

[0622] Users input feedback on the displayed meal suggestions via their device. This feedback includes their impressions and changes in their emotional state regarding the suggestions. This contributes to improving the accuracy of future suggestions.

[0623] Step 7:

[0624] The device sends the input feedback to the server. The server analyzes this feedback and uses it to improve the next meal suggestion, taking into account the user's emotional state, thereby providing more suitable suggestions.

[0625] (Application Example 2)

[0626] 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."

[0627] Conventional meal suggestion systems fail to adequately consider the user's emotions and mood, merely providing meal suggestions based on nutritional balance and basic personal information. As a result, optimal meal suggestions tailored to the user's psychological state and external environment are not obtained, limiting user satisfaction and the effectiveness of health improvement. This invention aims to solve these problems and provide more personalized meal suggestions that take the user's emotional state into consideration.

[0628] 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.

[0629] In this invention, the server includes means for receiving user information, means for receiving weather condition information, means for analyzing past meal history, means equipped with an emotion recognition module for analyzing facial expressions and voice tone as emotion data, and means for automatically placing orders by coordinating the generated meal suggestions with a delivery service. This enables the generation of optimal meal suggestions tailored to the user's emotions and rapid delivery.

[0630] "User information" refers to basic data about an individual, including age, gender, and family structure.

[0631] "Weather condition information" refers to environmental data such as weather, temperature, and humidity for a specific region, and is obtained from external weather information service providers.

[0632] "Past meal history" refers to a record of meals the user has consumed to date, including information such as individual foods, nutrients, and the date and time of consumption.

[0633] "Emotional data" refers to data that indicates a user's psychological state, obtained by analyzing their facial expressions and voice tone, and expresses emotions such as joy, anger, and sadness.

[0634] An "emotion recognition module" is a program or device that analyzes a user's facial expressions and voice tone and generates emotion data based on that analysis.

[0635] "Meal suggestions" refer to suggestions that include menus and ingredients of meals recommended to the user based on specific conditions.

[0636] A "delivery service" is a service that provides logistics functions to deliver meals ordered by users to a specified location.

[0637] The system implementing this invention consists of a user terminal equipped with an emotion recognition sensor and a data processing server in the cloud. The user terminal is equipped with a camera and microphone, which can be used to capture the user's facial expressions and voice tone in real time. This data is analyzed as emotion data using emotion recognition software such as Amazon Rekognition. The terminal also collects the user's basic information and past meal history, and comprehensively transmits this data to the server.

[0638] The server integrates received user information, weather conditions, meal history, and sentiment data, and analyzes the data using data processing libraries such as Python's Pandas. Based on this analyzed data, it utilizes deep learning models such as TensorFlow or PyTorch to generate meal suggestions that are appropriate to the user's emotional state and environment. These meal suggestions are optimized using a generative AI model and generated in a user-friendly format using natural language processing technology.

[0639] After meal suggestions are generated, the server sends the details back to the user's device. Simultaneously, it can integrate with APIs of delivery services such as Uber Eats and Grubhub to automatically order the suggested meal. This allows users to quickly receive meals tailored to their emotional state. For example, on a cold day when relaxation is desired, a menu including chicken and vegetable soup might be suggested and ordered immediately.

[0640] Specific example

[0641] A user logs in via their smartphone, and the application analyzes the day's weather and their fatigue level. Emotion recognition reads their tired expression and determines they need a relaxing meal. The server generates a suggestion for a nutritious soup and places an order through the Uber Eats platform. This entire process proceeds automatically without any user configuration.

[0642] Example of a prompt

[0643] "Consider the user's mood and the weather when suggesting today's meal menu."

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The device uses its camera and microphone to capture the user's facial expressions and voice. This data is analyzed in real time through an emotion recognition module and output as data indicating the user's emotional state. Technologies such as Amazon Rekognition are used in this process.

[0647] Step 2:

[0648] The device collects the user's basic information (age, gender, family structure, etc.) and past meal history. This data is retrieved from a database in JSON format and sent to the server along with sentiment data.

[0649] Step 3:

[0650] The server retrieves weather information from weather information services such as OpenWeatherMap. This includes weather, temperature, and humidity for a specific area, and the data is received in JSON format via an API.

[0651] Step 4:

[0652] The server integrates the data collected in steps 1 through 3. It uses the Python Pandas library to cleanse, filter, and analyze the data. The integrated dataset is then used as input data for generating meal suggestions.

[0653] Step 5:

[0654] The server launches a generative AI model using TensorFlow or PyTorch to generate meal suggestions tailored to the user's emotional state based on the data. This process outputs the most suitable menu, taking into account nutritional balance and emotional data.

[0655] Step 6:

[0656] The server sends the generated meal suggestions to the user's terminal. Simultaneously, the meal details are passed to the API of a delivery service such as Uber Eats, and the food is automatically ordered. As a result, the user can instantly review the suggested menu and make changes if necessary.

[0657] Step 7:

[0658] On the user's terminal, the user inputs feedback on the suggested meal. This feedback is sent to the server and used to improve the accuracy of future suggestions. A retrainable generative AI model is used to improve the suggestions.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] [Fourth Embodiment]

[0663] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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).

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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".

[0676] In embodiments of the present invention, the system functions as a mechanism for exchanging information between a user terminal and a server and providing the user with appropriate meal suggestions. The user inputs basic information such as age, gender, and family structure, as well as recent meal details, through the terminal. The terminal collects climate data based on the input data and transmits all the information to the server.

[0677] Based on the received data, the server analyzes user information, climate information, and past meal history to generate meal suggestions that take nutritional balance into consideration. These meal suggestions include recommended nutrient intake based on the user's age and gender, as well as meal content tailored to climate conditions.

[0678] For example, if the user is a 30-year-old male who ate a lot of pasta for dinner the previous night, the server can recommend a high-protein, vegetable-rich meal for his next meal. Also, on a cold, rainy day, the server might consider suggesting a meal that includes a warming soup.

[0679] Meal suggestions generated by the server are sent to the terminal and presented to the user. The user can provide feedback on the presented menu and, if necessary, send more detailed requests. Based on this feedback, the server readjusts the meal suggestions and makes new suggestions that meet the user's needs.

[0680] In this way, the system aims to support a healthy and balanced diet by providing personalized menus for each user.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] Users enter basic information such as age, gender, and family structure into the input form on the device. In addition, they record and enter details of their recent meals into the device.

[0684] Step 2:

[0685] The device saves the entered user information and meal details to local storage. It also accesses an API via the internet to retrieve current weather data based on the user's location.

[0686] Step 3:

[0687] The device packages all the collected information (user information, meal history, weather data) and sends it to the server.

[0688] Step 4:

[0689] The server begins analyzing the received data. First, it calculates the recommended daily nutrient intake based on the user's age and gender.

[0690] Step 5:

[0691] The server analyzes weather information and sets criteria for determining what to eat that day based on the weather conditions. For example, it might consider a warm soup on a cold day and a refreshing salad on a hot day.

[0692] Step 6:

[0693] The server analyzes past meal history and assesses any nutritional imbalances. Based on this information, it identifies the nutrients that should be consumed in the next meal.

[0694] Step 7:

[0695] The server generates nutritionally balanced meal suggestions based on user information, climate information, and analysis of meal history.

[0696] Step 8:

[0697] The server sends the generated meal suggestions to the user's terminal.

[0698] Step 9:

[0699] The device displays the suggested menu to the user and provides a feedback input screen.

[0700] Step 10:

[0701] Users enter feedback on the suggested menu into their device and, if necessary, enter any further requests for changes or additions.

[0702] Step 11:

[0703] The device sends user feedback to the server.

[0704] Step 12:

[0705] Based on user feedback, the server readjusts meal suggestions as needed and provides them to the user again.

[0706] (Example 1)

[0707] 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".

[0708] The aim is to enable users to achieve a healthy and balanced diet by providing a system that offers appropriate meal suggestions tailored to individual health conditions and preferences. However, conventional methods make it difficult to provide precise suggestions that take various factors into account, and there are particular challenges in personalized meal suggestions and their accuracy.

[0709] 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.

[0710] In this invention, the server includes a device for receiving user information, a device for receiving weather information, and a device for analyzing past intake information. This makes it possible to provide personalized meal suggestions tailored to the individual needs of the user and support a healthy diet.

[0711] "User information" refers to personal information necessary for meal suggestions, such as the user's age, gender, family structure, and past meal history.

[0712] "Weather information" refers to data related to the current weather and temperature at the user's geographical location.

[0713] "Past intake information" refers to historical data about the foods and nutrients that the user has previously consumed in meals.

[0714] "Nutritional balance" refers to the appropriate proportion of various nutrients necessary to maintain a healthy diet.

[0715] A "generative AI model" is a model that applies machine learning and artificial intelligence techniques to generate personalized meal suggestions.

[0716] A "user terminal" is a digital device used by users to input information and receive meal suggestions.

[0717] "Responses" refer to user feedback and requests regarding the generated meal suggestions.

[0718] To implement this invention, a system is constructed using a user terminal and a server. First, the user uses the user terminal to input necessary user information such as their age, gender, family structure, and recent meal details. The user terminal is equipped with application software that provides an interface to enable this information input.

[0719] Next, the terminal transmits the entered information to the server via the computer network. At this time, the terminal also obtains weather information based on its current geographic location using an API, and transmits all the data together to the server. For example, API data obtained from a weather information service is used as supplementary information.

[0720] The server uses a generative AI model to analyze data based on received user information and weather information, comparing it with past intake data. This analysis generates meal suggestions tailored to the user's individual nutritional needs. This process utilizes machine learning models to learn patterns from various data, enabling the provision of menus that support the user's healthy eating habits.

[0721] As a concrete example, the server is provided with the following information as a prompt: "30-year-old male, had pasta for dinner yesterday, it's raining and cold today." Based on this information, the server generates a meal suggestion: "A meal high in protein with warm soup." The generated suggestion is then sent back to the user's terminal via the network and presented to the user. The user reviews the suggestion and provides feedback, which allows the server to further improve it and incorporate the feedback into future suggestions.

[0722] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0723] Step 1:

[0724] The user enters their age, gender, family structure, and recent meal details into the user terminal. The entered information is temporarily stored in the terminal's database. In this step, there is input from the user, and as a result, a user information dataset is generated as output. The terminal checks the integrity of the information and prepares for the next step.

[0725] Step 2:

[0726] The device collects local weather information via the internet based on the user information entered. The API used here is from a weather service provider and retrieves temperature and weather conditions. Based on the user information as input, an API request is sent, and the weather information is returned to the device as output. This information is integrated with the user information and prepared for transmission to the server.

[0727] Step 3:

[0728] The terminal transmits integrated user information and weather data to the server. This information is delivered to the server via the internet and treated as a single data package. All user information and weather data are used as input, and the data package is generated as output that is transferred to the server.

[0729] Step 4:

[0730] The server receives the transmitted data package and performs analysis using a generative AI model. During the analysis, past intake information and other relevant data are also referenced. The server takes the data package as input, the generative AI model calculates meal suggestions through analysis, and generates the results as output. This process particularly considers the balance of nutrients and individual preference patterns.

[0731] Step 5:

[0732] The server sends the generated meal suggestions to the user's terminal. The suggestions are displayed to the user in an easy-to-understand format on the terminal. The input here is the meal suggestion data, and the output is the visualized suggestions displayed on the user's terminal.

[0733] Step 6:

[0734] Users review the suggested meal menus and submit feedback via their device. This feedback includes acceptance of the suggestions or requests for modifications, and is used as input to further improve the suggestions. The output is user feedback data.

[0735] Step 7:

[0736] The server analyzes the received feedback and readjusts the generated AI model. It also creates new meal suggestions based on the feedback, improving the model's accuracy. Using the feedback data as input, an improved next meal suggestion is generated as output. This loop process gradually improves the accuracy of the suggestions.

[0737] (Application Example 1)

[0738] 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".

[0739] In modern life, proposing nutritionally balanced meals tailored to individual health conditions and lifestyles, and then efficiently providing them, is a crucial challenge. In particular, there is a need for a system that allows for easy selection of healthy meals in busy daily lives. However, conventional systems have struggled to provide meal suggestions that take into account climatic conditions and individual user preferences. Furthermore, a lack of means to directly supply the suggested meals has been a problem.

[0740] 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.

[0741] In this invention, the server includes means for receiving user information, means for receiving climate information, means for analyzing past meal history, and means for coordinating with affiliated food and beverage services to supply suggested meals. This makes it possible to suggest nutritionally balanced meals based on the user's individual conditions and to supply them efficiently.

[0742] "Means for receiving user information" refers to a system for obtaining individual information from users, such as age, gender, family structure, and recent dietary habits.

[0743] A "means for receiving climate information" refers to a mechanism that provides an interface for obtaining current weather conditions.

[0744] "Methods for analyzing past meal history" refers to a process for analyzing a user's past diet and evaluating its nutritional balance.

[0745] "A means of generating meal suggestions that take nutritional balance into consideration" refers to an algorithm that plans meal content to support a healthy lifestyle based on acquired user information, climate information, and meal history.

[0746] "Means for transmitting generated meal suggestions to the user device" refers to communication technology for notifying the user's terminal of the planned meal content.

[0747] "A means of supplying suggested meals in cooperation with partner food and beverage services" refers to a system that collaborates with external food and beverage delivery services to deliver suggested meals to users as actual dishes.

[0748] "Means of receiving feedback" refers to an interface for users to input evaluations and opinions on the suggested meals.

[0749] "Means for regenerating suggestions" refers to the process of updating meal suggestions by taking user feedback into consideration.

[0750] One embodiment of this invention is a system that provides personalized meal suggestions based on user information and climate conditions, and streamlines the process of providing them.

[0751] On the user's terminal, a dedicated application runs, allowing the user to input necessary information such as their age, gender, family structure, and recent diet. The terminal compiles this information and sends it to a server via the internet. Furthermore, the terminal utilizes an API to obtain local climate data and also sends that data to the server.

[0752] The server performs detailed data analysis based on the received user information and weather conditions. This analysis utilizes algorithms built in Python or R programming environments. Using this information, the server performs calculations to generate meal suggestions that take into account the individual nutritional balance of each user. These suggestions include nutritional intake guidelines based on age and gender, as well as menu suggestions tailored to temperature and weather.

[0753] The generated meal suggestions are quickly sent to the user's terminal and notified to the user. The user sends a response to the suggested meal from their terminal, and the server readjusts the suggestions based on that feedback. In this way, the suggestions are more accurately adapted to the user's preferences and wishes.

[0754] The suggested meals are delivered as actual dishes through partnerships with external food and beverage services. This integration utilizes the food and beverage service's API, allowing users to complete their orders within the application.

[0755] As a concrete example, let's assume the user is a 30-year-old man who ate high-calorie ramen last night. Because it's a rainy and cold day, the server suggests a high-protein, low-calorie hot pot dish, which can then be ordered through a local delivery service.

[0756] An example of a prompt message is as follows:

[0757] "Given that the user is 30 years old, male, ate ramen for dinner yesterday, and the weather today is cold and rainy, please suggest an optimally balanced dinner menu. Please include warming dishes and high-protein options."

[0758] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0759] Step 1:

[0760] The user terminal receives input from the user, including age, gender, family structure, and recent meal history. Based on this input, the terminal calls a weather API to obtain climate information. This results in a combination of the user's personal data and the latest climate data.

[0761] Step 2:

[0762] The terminal transmits collected user information and climate information to the server via the internet. This data transfer allows the server to obtain all the information necessary for analysis.

[0763] Step 3:

[0764] The server analyzes received user information, climate information, and past dietary data. Using statistical analysis libraries in Python or R, it evaluates the user's recent nutritional intake and determines necessary nutrients. This constitutes data processing and calculation.

[0765] Step 4:

[0766] The server uses a generative AI model to generate optimal meal suggestions based on the analysis results. It uses prompts to create specific menu suggestions tailored to the user's conditions. An example of such a prompt would be, "The user is 30 years old, male, ate ramen for dinner yesterday, and today's weather is cold and rainy. Please suggest an optimally nutritionally balanced dinner menu."

[0767] Step 5:

[0768] The server sends the generated meal suggestions back to the user's device. This allows the user to view the suggested menu on their device.

[0769] Step 6:

[0770] Users provide feedback on the suggested meal. For example, they send responses such as "I don't like this dish" or "I'd like more vegetables" from their device to the server. The server receives this as input and re-analyzes it.

[0771] Step 7:

[0772] The server regenerates meal suggestions based on user feedback and sends the new suggestions to the device. This process allows for readjustment of suggestions to suit the user's preferences and circumstances.

[0773] Step 8:

[0774] Users can order the suggested meals through external food and beverage services. The terminal calls the API of the partner food and beverage service to process the order and ensure that the user's meal is delivered reliably.

[0775] 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.

[0776] This invention provides an innovative system that incorporates an emotion recognition engine to offer meal suggestions that take the user's emotions into account. The user inputs basic personal information (age, gender, family structure, etc.) and recent meal details from a terminal. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotion data.

[0777] The device sends the collected information, along with this emotional data, to the server. The server integrates and analyzes user information, weather information, meal history, and emotional data. For example, if the user is feeling stressed, it will generate meal suggestions that include relaxing herbal tea. It will also consider appropriate meal types based on the weather and temperature and reflect this in the suggestions.

[0778] For example, if a user is feeling fatigued and the outside temperature is low, the server can generate a high-protein, nutritionally balanced menu centered around chicken and vegetable soup. This menu suggestion incorporates elements that promote relaxation and aims to improve the user's mood.

[0779] The generated meal suggestions are delivered to the user via a terminal. The user can provide feedback on the menu and request alternatives based on their feelings and preferences. The server also analyzes this feedback data to provide more personalized suggestions for the next meal.

[0780] This invention constitutes a system that effectively supports the improvement of users' health and mood through personalized meal suggestions based on emotion recognition.

[0781] The following describes the processing flow.

[0782] Step 1:

[0783] The user enters their age, gender, family structure, and recent meal details into the device. The device temporarily stores this entered information.

[0784] Step 2:

[0785] The device uses its built-in camera and microphone to collect the user's facial expressions and voice, and analyzes the user's emotional state using an emotion recognition sensor.

[0786] Step 3:

[0787] The device collects acquired user information, recent meal details, emotional data, and weather information, packages all the data, and sends it to the server.

[0788] Step 4:

[0789] The server analyzes the received data and calculates the recommended daily nutrient intake based on the user's age and gender.

[0790] Step 5:

[0791] The server uses weather information to determine the type of meal appropriate for the day's climate. For example, on cold days, it considers a warm meal.

[0792] Step 6:

[0793] The server analyzes past meal history and identifies nutrients to correct nutritional imbalances.

[0794] Step 7:

[0795] The server analyzes emotional data and identifies and incorporates ingredients that have a relaxing effect and culinary elements that improve emotions.

[0796] Step 8:

[0797] The server combines candidate ingredients to generate nutritionally balanced meal suggestions that improve the user's health and mood.

[0798] Step 9:

[0799] The server sends the generated meal suggestions to the terminal.

[0800] Step 10:

[0801] The terminal displays the suggested menu to the user and provides an interface for requesting feedback.

[0802] Step 11:

[0803] Users input feedback on the presented menu and any elements they wish to change into the terminal.

[0804] Step 12:

[0805] The device sends user feedback to the server, which then re-analyzes the data and readjusts meal suggestions as needed.

[0806] (Example 2)

[0807] 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".

[0808] In recent years, in order to efficiently manage individual health, it has become necessary to suggest meals tailored to the user's emotional state and individual needs. However, conventional systems do not adequately consider the user's emotional state, and there are limitations to realizing personalized meal suggestions. This invention aims to support health maintenance and mental stability by enabling personalized meal suggestions that take the user's emotional state into consideration.

[0809] 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.

[0810] In this invention, the server includes means for receiving the user's basic information and emotional data, means for receiving climate information, and means for analyzing past meal information to analyze the emotional state. This makes it possible to suggest meals that take into account the user's emotional state and nutritional balance.

[0811] "User basic information" refers to fundamental attributes of an individual, such as age, gender, and family structure, and is the basic data that allows the system to individually identify users and make appropriate suggestions.

[0812] "Emotional data" refers to data that expresses a user's emotional state based on information obtained through the user's facial expressions, voice tone, and other means.

[0813] "Climate information" refers to information about environmental factors such as weather and temperature, and is data that is considered in order to provide meal suggestions suitable for the user.

[0814] "Past meal information" refers to data about the meals a user has previously consumed, and is used to understand their meal selection patterns and nutritional status.

[0815] "Meal suggestions" are personalized, nutritionally balanced menu recommendations created based on the user's basic information, emotional data, climate information, and past eating history.

[0816] A "user device" is a terminal used by a user to input information or receive suggestions, and is equipped with sensors for acquiring emotional data.

[0817] "Feedback" refers to the evaluations and opinions that users give regarding suggested meals, and it is an important source of information for making future suggestions more personalized.

[0818] "Analysis" is a series of processes that use collected data to analyze the user's emotional state and eating habits, and then generate appropriate meal suggestions.

[0819] This invention is a system that provides meal suggestions that take the user's emotions into consideration, and is implemented as follows. The system consists of the interaction of the user, a terminal, and a server.

[0820] The user enters their basic information (age, gender, family structure, etc.) and past meal history through the device. The device is equipped with emotion recognition sensors such as a camera and microphone, which are used to analyze the user's facial expressions and voice tone to acquire emotional data. This measures the user's current emotional state and records it as data.

[0821] The device transmits acquired basic information, emotional data, and past meal information to the server. The server integrates this information, along with climate information acquired from external sources, and performs data analysis. The data analysis uses an emotional recognition engine and a generative AI model to generate meal suggestions that take into account the user's emotional state and nutritional balance.

[0822] For example, suppose a user inputs, "I'm a man in my 30s, and I've been feeling stressed at work lately. What kind of meal would you suggest for a cold day?" In this case, the system can determine the user's stress level from emotional data and, taking temperature information into consideration, suggest a relaxing herbal tea and a nutritionally balanced chicken and vegetable soup.

[0823] The generated meal suggestions are provided to the user via a terminal. The user inputs their thoughts and preferences as feedback on the provided suggestions into the terminal. This feedback information is sent back to the server and used to improve the accuracy of future meal suggestions.

[0824] Through this process, users can receive meal suggestions tailored to their emotional state and individual needs, enabling them to maintain their health and improve their mood.

[0825] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0826] Step 1:

[0827] Users enter basic information (age, gender, family structure, etc.) and past meal history using their device. The entered information is saved as initial data within the device. This prepares individual basic data.

[0828] Step 2:

[0829] The user utilizes the camera and microphone built into the device, allowing the emotion recognition sensor to capture the user's facial expressions and voice tone. The acquired data is analyzed by an emotion recognition algorithm to generate the user's emotion data. This emotion data indicates the user's current emotional state.

[0830] Step 3:

[0831] The device sends the basic information and meal information obtained in Step 1, along with the emotional data generated in Step 2, to the server. Data transmission takes place via a secure communication protocol, and a dataset that can be analyzed is constructed on the server side.

[0832] Step 4:

[0833] The server integrates received user information, emotional data, and dietary information with climate information obtained from external sources. Data analysis is then performed on the integrated dataset using generative AI models and analysis engines. Specifically, it generates nutritionally balanced meal suggestions that take into account the user's emotional state and climate conditions.

[0834] Step 5:

[0835] Meal suggestions generated by the server are sent to the terminal. The terminal displays this information to the user, providing them with specific menu details. The user can then make a meal selection based on these suggestions.

[0836] Step 6:

[0837] Users input feedback on the displayed meal suggestions via their device. This feedback includes their impressions and changes in their emotional state regarding the suggestions. This contributes to improving the accuracy of future suggestions.

[0838] Step 7:

[0839] The device sends the input feedback to the server. The server analyzes this feedback and uses it to improve the next meal suggestion, taking into account the user's emotional state, thereby providing more suitable suggestions.

[0840] (Application Example 2)

[0841] 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".

[0842] Conventional meal suggestion systems fail to adequately consider the user's emotions and mood, merely providing meal suggestions based on nutritional balance and basic personal information. As a result, optimal meal suggestions tailored to the user's psychological state and external environment are not obtained, limiting user satisfaction and the effectiveness of health improvement. This invention aims to solve these problems and provide more personalized meal suggestions that take the user's emotional state into consideration.

[0843] 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.

[0844] In this invention, the server includes means for receiving user information, means for receiving weather condition information, means for analyzing past meal history, means equipped with an emotion recognition module for analyzing facial expressions and voice tone as emotion data, and means for automatically placing orders by coordinating the generated meal suggestions with a delivery service. This enables the generation of optimal meal suggestions tailored to the user's emotions and rapid delivery.

[0845] "User information" refers to basic data about an individual, including age, gender, and family structure.

[0846] "Weather condition information" refers to environmental data such as weather, temperature, and humidity for a specific region, and is obtained from external weather information service providers.

[0847] "Past meal history" refers to a record of meals the user has consumed to date, including information such as individual foods, nutrients, and the date and time of consumption.

[0848] "Emotional data" refers to data that indicates a user's psychological state, obtained by analyzing their facial expressions and voice tone, and expresses emotions such as joy, anger, and sadness.

[0849] An "emotion recognition module" is a program or device that analyzes a user's facial expressions and voice tone and generates emotion data based on that analysis.

[0850] "Meal suggestions" refer to suggestions that include menus and ingredients of meals recommended to the user based on specific conditions.

[0851] A "delivery service" is a service that provides logistics functions to deliver meals ordered by users to a specified location.

[0852] The system implementing this invention consists of a user terminal equipped with an emotion recognition sensor and a data processing server in the cloud. The user terminal is equipped with a camera and microphone, which can be used to capture the user's facial expressions and voice tone in real time. This data is analyzed as emotion data using emotion recognition software such as Amazon Rekognition. The terminal also collects the user's basic information and past meal history, and comprehensively transmits this data to the server.

[0853] The server integrates received user information, weather conditions, meal history, and sentiment data, and analyzes the data using data processing libraries such as Python's Pandas. Based on this analyzed data, it utilizes deep learning models such as TensorFlow or PyTorch to generate meal suggestions that are appropriate to the user's emotional state and environment. These meal suggestions are optimized using a generative AI model and generated in a user-friendly format using natural language processing technology.

[0854] After meal suggestions are generated, the server sends the details back to the user's device. Simultaneously, it can integrate with APIs of delivery services such as Uber Eats and Grubhub to automatically order the suggested meal. This allows users to quickly receive meals tailored to their emotional state. For example, on a cold day when relaxation is desired, a menu including chicken and vegetable soup might be suggested and ordered immediately.

[0855] Specific example

[0856] A user logs in via their smartphone, and the application analyzes the day's weather and their fatigue level. Emotion recognition reads their tired expression and determines they need a relaxing meal. The server generates a suggestion for a nutritious soup and places an order through the Uber Eats platform. This entire process proceeds automatically without any user configuration.

[0857] Example of a prompt

[0858] "Consider the user's mood and the weather when suggesting today's meal menu."

[0859] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0860] Step 1:

[0861] The device uses its camera and microphone to capture the user's facial expressions and voice. This data is analyzed in real time through an emotion recognition module and output as data indicating the user's emotional state. Technologies such as Amazon Rekognition are used in this process.

[0862] Step 2:

[0863] The device collects the user's basic information (age, gender, family structure, etc.) and past meal history. This data is retrieved from a database in JSON format and sent to the server along with sentiment data.

[0864] Step 3:

[0865] The server retrieves weather information from weather information services such as OpenWeatherMap. This includes weather, temperature, and humidity for a specific area, and the data is received in JSON format via an API.

[0866] Step 4:

[0867] The server integrates the data collected in steps 1 through 3. It uses the Python Pandas library to cleanse, filter, and analyze the data. The integrated dataset is then used as input data for generating meal suggestions.

[0868] Step 5:

[0869] The server launches a generative AI model using TensorFlow or PyTorch to generate meal suggestions tailored to the user's emotional state based on the data. This process outputs the most suitable menu, taking into account nutritional balance and emotional data.

[0870] Step 6:

[0871] The server sends the generated meal suggestions to the user's terminal. Simultaneously, the meal details are passed to the API of a delivery service such as Uber Eats, and the food is automatically ordered. As a result, the user can instantly review the suggested menu and make changes if necessary.

[0872] Step 7:

[0873] On the user's terminal, the user inputs feedback on the suggested meal. This feedback is sent to the server and used to improve the accuracy of future suggestions. A retrainable generative AI model is used to improve the suggestions.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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."

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] The following is further disclosed regarding the embodiments described above.

[0896] (Claim 1)

[0897] Means for receiving user information,

[0898] Means for receiving climate information,

[0899] Methods for analyzing past meal content,

[0900] A means for generating meal suggestions that take nutritional balance into consideration, based on the user information, climate information, and past meal content,

[0901] A system including means for sending generated meal suggestions to a user's terminal.

[0902] (Claim 2)

[0903] The system according to claim 1, further comprising means for receiving user feedback on generated meal suggestions, and for regenerating meal suggestions based on the feedback.

[0904] (Claim 3)

[0905] The system according to claim 1, wherein the generated meal suggestions are generated taking into account the recommended daily nutrient intake based on the user's age and gender.

[0906] "Example 1"

[0907] (Claim 1)

[0908] A device that receives user information,

[0909] A device for receiving weather information,

[0910] A device that analyzes past intake information,

[0911] A device that generates suggestions considering nutritional balance based on the user information, weather information, and past intake information,

[0912] A device that constructs personalized meal suggestions using a generative AI model,

[0913] A system including a device that sends the generated suggestions to the user's terminal.

[0914] (Claim 2)

[0915] The system according to claim 1, further comprising a device for receiving user responses to generated meal suggestions, and for regenerating suggestions based on the responses.

[0916] (Claim 3)

[0917] The system according to claim 1, wherein the generated meal suggestions are analyzed by a generating AI model, taking into account the user's age and gender and recommended daily nutrient intake.

[0918] "Application Example 1"

[0919] (Claim 1)

[0920] Means for receiving user information,

[0921] Means for receiving climate information,

[0922] Methods for analyzing past eating history,

[0923] A means for generating meal suggestions that take nutritional balance into consideration, based on the user information, climate information, and past meal history,

[0924] A means for transmitting the generated meal suggestions to the user device,

[0925] A system that includes means of supplying proposed meals in cooperation with partner food and beverage services.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising means for receiving responses from users to generated meal suggestions, and for regenerating meal suggestions based on the responses.

[0928] (Claim 3)

[0929] The system according to claim 1, wherein the generated meal suggestions are generated taking into account the recommended daily nutrient intake based on the user's age and gender.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] A means of receiving basic user information and sentiment data,

[0933] Means for receiving climate information,

[0934] A method for analyzing emotional states by analyzing past meal information,

[0935] A means for generating meal suggestions that take into account the user's emotional state and nutritional balance, based on the user's basic information, climate information, past meal information, and emotional data.

[0936] A system including means for transmitting generated meal suggestions to a user device.

[0937] (Claim 2)

[0938] The system according to claim 1, which receives feedback from the user on the generated meal suggestions and regenerates the meal suggestions based on the feedback and the user's emotional state.

[0939] (Claim 3)

[0940] The system according to claim 1, wherein the generated meal suggestions are generated taking into account the user's age, gender, and emotional state, and are based on a recommended daily intake of nutrients.

[0941] "Application example 2 when combining with an emotional engine"

[0942] (Claim 1)

[0943] Means for receiving user information,

[0944] A means of receiving weather condition information,

[0945] Methods for analyzing past eating history,

[0946] A means for generating meal suggestions that take emotional data into consideration, based on the user information, weather conditions information, and past meal history,

[0947] A means for transmitting the generated meal suggestions to the user device,

[0948] The means includes an emotion recognition module for analyzing facial expressions and voice tone as emotion data,

[0949] A system including a means of automatically placing an order by linking the generated meal suggestions with a delivery service.

[0950] (Claim 2)

[0951] The system according to claim 1, further comprising means for receiving user responses to generated meal suggestions, and for regenerating meal suggestions based on the responses.

[0952] (Claim 3)

[0953] The system according to claim 1, wherein the generated meal suggestions are produced taking into account the recommended daily intake of nutrients based on the user's age, gender, and emotional state. [Explanation of symbols]

[0954] 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. Means for receiving user information, Means for receiving climate information, Methods for analyzing past meal content, A means for generating meal suggestions that take nutritional balance into consideration, based on the user information, climate information, and past meal content, A system including means for sending generated meal suggestions to a user's terminal.

2. The system according to claim 1, further comprising means for receiving user feedback on generated meal suggestions, and for regenerating meal suggestions based on the feedback.

3. The system according to claim 1, wherein the generated meal suggestions are generated taking into account the recommended daily nutrient intake based on the user's age and gender.

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