system

Abstract

We provide the system. [Solution] A generation method for generating individually optimized meal plans based on the user's health information, A means of acquiring information on remaining food from local food and beverage establishments based on the user's location information, A means of providing the generated meal plan and acquired dining facility information to the user in audio and visual formats, A means of adjusting meal plans based on user input, A data storage method that accumulates user usage history and optimizes guidance for future visits, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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 society, especially among the elderly, it is difficult to make an appropriate diet plan for maintaining health, and there is a lack of means to effectively utilize food ingredients and food facility information available within a limited area. Therefore, there is an increasing need for a system that can easily obtain diet proposals according to individual health conditions and nutritional needs. Furthermore, in order to activate the local economy, it is also required to effectively utilize the remaining inventory information of local food facilities.

Means for Solving the Problems

[0005] The present invention provides a system that enables users to plan meals according to their health needs and promotes economic mutual complementarity by utilizing local food and beverage facilities. This system includes a generation means for generating individually optimized meal plans based on the user's health information, an acquisition means for acquiring remaining food information from local food and beverage facilities based on location information, a provision means for providing the generated meal plans and acquired facility information via voice and visual means, an adjustment means for adjusting meal plans based on user input, and a data storage means for accumulating usage history and optimizing guidance.

[0006] "User" refers to an individual who uses this system to obtain meal plans and information on restaurants and food establishments.

[0007] "Health information" refers to data necessary for personalized health management, such as the user's age, medical history, allergy information, and nutritional needs.

[0008] "Personalized optimization" refers to a process that takes into account the user's health information and selects and proposes the most suitable nutrients and foods for that individual.

[0009] A "meal plan" refers to a specific meal plan or recipe proposal offered to a user.

[0010] "Generation means" refers to the algorithm or process in this system for automatically generating meal suggestions based on the user's health condition and preferences.

[0011] "Location information" refers to data that indicates the user's geographical location and is used to obtain information on local restaurants and bars.

[0012] "Local food and beverage establishments" refer to stores that are located within the user's living area and provide food and beverage services or ingredients.

[0013] "Remaining food information" refers to information about unsold menu items and ingredients that can be offered at food and beverage establishments.

[0014] "Acquisition means" refers to a process or technology for collecting the residual food information of food service establishments and importing it into this system.

[0015] "Provision means" refers to a user interface for presenting the generated meal plans and the acquired food service establishment information to users.

[0016] "Adjustment means" refers to a mechanism for reevaluating meal plans based on feedback from users and making changes as necessary.

[0017] "Data accumulation means" refers to a method for collecting users' selection histories and behavior data and using them to improve the system in the future and enhance the quality of proposals.

Brief Description of the Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] To implement this invention, the user first accesses the system using a terminal and logs in. After logging in, the terminal sends the user's health information to the server, which then generates individually optimized meal plans based on this information. The server considers the user's health condition and nutritional needs using, for example, an AI-based algorithm, and generates multiple recipe suggestions based on the results.

[0040] Next, the server obtains location information such as the user's current location, and based on this, accesses a database of local restaurants to retrieve information on remaining food. This information includes, for example, information on unsold menu items and ingredients that can be offered at restaurants. The server combines this information with the user's meal preferences to create more comprehensive suggestions.

[0041] The terminal provides the user with generated meal suggestions and information on local restaurants and eateries, both visually and audibly. Based on the displayed information, the user can provide feedback tailored to their preferences and nutritional needs. For example, they can voice-input requests such as, "I'd like the salt content to be reduced a bit." Based on this feedback, the server uses an AI model to adjust the meal suggestions and sends them back to the terminal.

[0042] Furthermore, the device records the user's choices and usage history in a database. This data is analyzed by a server and used to optimize future suggestions for the user. Based on the ultimately suggested meal plans and information on restaurants, users can then plan their actual meals and purchase necessary ingredients.

[0043] For example, if a user requests a low-carbohydrate meal plan due to diabetes, the server will provide recipes using local low-carbohydrate ingredients and also suggest low-carbohydrate menus available at nearby restaurants. This allows users to easily utilize nearby restaurants in addition to cooking at home.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user starts up the device, accesses the system, and enters their login information. The device then sends the login information to the server for authentication.

[0047] Step 2:

[0048] Once authentication is successful, the server retrieves the user's health information and usage history from the database. This retrieved information is used as material to generate personalized meal plans.

[0049] Step 3:

[0050] The server uses AI algorithms to generate multiple recipe suggestions based on the user's health status and nutritional needs. The generated recipe suggestions are then customized to take nutritional balance and health conditions into consideration.

[0051] Step 4:

[0052] The user's location information is obtained from the device and sent to the server. Based on this location information, the server refers to a database of local restaurants and bars to obtain information on available food in the area.

[0053] Step 5:

[0054] The server integrates the generated meal plans with information on remaining food at local restaurants and creates recommendations for the user. These recommendations combine the user's health needs with local ingredients and dishes.

[0055] Step 6:

[0056] The device visually and audibly presents the user with suggested meal options and information about dining establishments. Based on this, the user can provide feedback on the meal options and request customizations.

[0057] Step 7:

[0058] User feedback is sent from the device to the server. The server uses an AI algorithm to adjust the meal plan based on the feedback and make any necessary changes.

[0059] Step 8:

[0060] The adjusted meal plan is sent to the device and presented to the user again. The user can then review the final suggestion, create an actual meal plan, and purchase the necessary ingredients and menu items.

[0061] Step 9:

[0062] The server records the user's selected meal suggestions and usage history in a database. This data is then used to provide more personalized suggestions for future visits.

[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] In modern society, providing dietary suggestions that address health maintenance and specific nutritional needs is crucial, but there is a lack of systems that can quickly and accurately provide personalized suggestions tailored to each user's health condition and preferences. Furthermore, there is a need for methods that utilize ingredient information held by local food and beverage establishments to enhance convenience and sustainability.

[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 generation means for generating individually optimized meal suggestions based on the user's health information, an information acquisition means for acquiring information on remaining ingredients from local food and beverage establishments based on the user's location information, and an information provision means for providing the generated meal suggestions and acquired food and beverage establishment information to the user via voice and visual means. This enables the rapid provision of personalized meal suggestions and allows for sustainable meal choices that utilize local resources.

[0068] "Generation method" refers to algorithms and technologies that automatically create individually optimized meal suggestions based on the user's health information.

[0069] "Information acquisition method" refers to a system that uses the user's location information to collect data on ingredients and menus from local food and beverage establishments.

[0070] "Information provision means" refers to interfaces and technologies for presenting generated meal plans and information about local food and beverage establishments to users in audio or visual formats.

[0071] "Adjustment mechanism" refers to a system that re-evaluates meal suggestions based on user feedback and requests, and makes changes as necessary.

[0072] "Data storage means" refers to technologies that collect and store users' past choices and usage patterns to optimize future suggestions.

[0073] "AI update method" refers to technology that uses artificial intelligence to automatically improve meal suggestions based on user preferences and feedback.

[0074] To implement this invention, the user first accesses the system using a mobile device or computer and logs in. During this process, the user's health information is transmitted from the device to the server. This health information includes, for example, weight, height, allergy information, and data related to their health status.

[0075] The server receives this health information and inputs it into an AI-powered generative model to generate personalized meal plans. In this process, by inputting specific prompts into the AI ​​model, such as "Please suggest low-carb, high-protein recipes," suggestions that meet specific dietary requirements can be obtained.

[0076] Furthermore, the device uses a GPS module to obtain the user's current location. The location information is sent to a server, which then accesses a database of local restaurants and bars. There, information about leftover menu items and ingredients available in the area is retrieved.

[0077] The acquired information is integrated on a server, and the generated meal suggestions and local restaurant information are combined and provided to the user. On the terminal, the information is displayed visually on the screen, and the information is also guided by voice using a voice assistant.

[0078] When a user provides feedback on a meal plan, such as "I'd like the salt content reduced," that information is sent back to the server, and the meal plan is readjusted using an AI model. The adjusted content is then sent back to the device and presented to the user.

[0079] Furthermore, the terminal records the user's selection history and usage history in a database, and the server analyzes this data to provide optimized suggestions tailored to the user's preferences on subsequent visits.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user accesses the system using a terminal and logs in. The terminal displays an interface for the user to enter their health information, and after successful login, this data is sent to the server. The entered health information may include weight, height, dietary preferences, and allergy information. This provides the server with basic data for making personalized recommendations.

[0083] Step 2:

[0084] The server uses a generative AI model to generate personalized meal plans based on the received health information. This process involves inputting the prompt "Please suggest low-carb, high-protein recipes" into the AI ​​model and outputting the meal plans suggested by the model. As for data processing, the AI ​​model calculates the nutritional value based on the input health information and selects appropriate recipes.

[0085] Step 3:

[0086] The device uses GPS to obtain the user's current location and sends it to the server. This information is entered into the server as location data and used to access a database of local food and beverage establishments. Based on the location data, the server retrieves menus and remaining food information from food and beverage establishments near the user and saves this as output data.

[0087] Step 4:

[0088] The server integrates the generated meal plans with acquired local food and beverage facility information to create optimized meal recommendations. This integration process outputs meal recommendations that link the generated meal plans to specific local resources.

[0089] Step 5:

[0090] The terminal visually displays and provides voice guidance on integrated meal suggestions received from the server. Users are presented with various menus and selectable options displayed on the screen. This allows users to plan their meals based on concrete choices.

[0091] Step 6:

[0092] Users evaluate the suggested menu based on visual displays and audio guidance, and input feedback on the meal plan into the device. This feedback may include requests such as "I would like the salt content reduced," and is recorded on the device via voice or text input.

[0093] Step 7:

[0094] The device sends user feedback back to the server. Based on the received feedback, the server uses an AI model to re-evaluate the meal suggestions, makes adjustments based on the feedback, and generates new suggestions. As a result, newly adjusted meal suggestions are output.

[0095] Step 8:

[0096] The device ultimately provides the user with a revised meal plan, allowing them to review optimized suggestions that reflect their feedback. This enables users to plan meals that suit their preferences and health needs.

[0097] Step 9:

[0098] The terminal records the user's selection and usage history in a database and sends this information to the server. The server analyzes the accumulated data and processes it to better adapt future suggestions to the user. This information is used in the next suggestion generation process.

[0099] (Application Example 1)

[0100] 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."

[0101] In modern society, the growing health consciousness and busy lifestyles have created a demand for easily accessible meal plans tailored to individual health conditions and nutritional needs, and for efficient implementation. However, conventional systems have struggled to provide personalized meal plans and immediate food delivery based on them. This has made the selection and rapid delivery of meals suitable for individual health needs a challenge.

[0102] 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.

[0103] In this invention, the server includes data processing means for generating individually optimized meal plans based on the user's health information, information gathering means for acquiring information on remaining food from local food establishments based on the user's location information, and information providing means for providing the generated meal plan and acquired food establishment information to the user via voice and visual means. This allows the user to efficiently obtain meal plans suited to their individual health condition and to place immediate orders through food delivery services.

[0104] A "user" is an individual who uses this system to access personalized meal plans and food delivery services.

[0105] "Health information" refers to data that shows the user's physical condition and nutritional needs, and is the basic information used to generate meal plans.

[0106] "Location information" refers to data that indicates the user's current geographical location and is used to obtain information from local food and beverage establishments.

[0107] "Local dining establishments" is a general term for restaurants and food service establishments that provide meals selected based on the user's location information.

[0108] "Remaining food information" refers to data on unsold or available ingredients at local food and beverage establishments.

[0109] "Data processing means" refers to a set of functions for generating individually optimized meal plans based on the user's health information.

[0110] "Information gathering means" refers to a group of functions that acquire information on remaining food from local food and beverage establishments based on the user's location information.

[0111] "Information provision means" refers to a set of functions that display the generated meal plan and acquired dining facility information to the user in both audio and visual formats.

[0112] "Adjustment mechanisms" refer to a set of functions that improve and update meal plans based on user feedback.

[0113] An "AI algorithm" is a computational method that uses artificial intelligence to analyze a user's data history and optimize future suggestions.

[0114] A "food delivery service" is a logistics service that allows users to order and have meals delivered instantly based on a generated meal plan.

[0115] To implement this invention, the user first accesses the system using a dedicated terminal and sends health information to the server. This health information includes the user's current health status and specific nutrient needs. Based on this information, the server uses an AI algorithm to generate an individually optimized meal plan.

[0116] This system includes a user interface that utilizes a smartphone, smart glasses, or head-mounted display. Through this hardware, the generated meal plan is provided to the user visually and audibly. For example, a mobile application developed using React Native or Flutter® might be used.

[0117] When a user enables location services, the server uses the Google® Maps API and other tools to collect information on remaining food items from local restaurants. This information is then integrated into a meal plan generated by AI. The server uses AI models built in languages ​​such as Python and R to perform data calculations and adjust and optimize the plan.

[0118] Users can provide feedback based on the suggested meal plans and restaurant information. This feedback is analyzed using natural language processing technology and used to update the meal plan through adjustment mechanisms. For example, users can input a request such as "I'd like the salt content reduced a bit" via voice into their device.

[0119] The AI ​​model analyzes the user's past choices and feedback to optimize future suggestions. This enables personalized meal recommendations that better match the user's preferences and health needs.

[0120] One concrete example of its use is when a user who receives a high cholesterol level result in a health checkup uses this system to generate a low-cholesterol meal plan and instantly orders corresponding menu items from local restaurants.

[0121] An example of a prompt would be, "Based on my health information, please suggest some low-cholesterol diet options that I can use right now."

[0122] In this way, by generating meal plans tailored to the user's health condition and enabling immediate integration with local restaurants and food establishments, a system can be built that easily supports individual health management.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] Users access the system using a dedicated terminal and input health information. This information is entered as data indicating the user's current health status and specific nutritional needs. The terminal sends this information to the server. The input is health information data, and the output is health data stored on the server.

[0126] Step 2:

[0127] The server uses an AI algorithm to generate an individually optimized meal plan based on the received health information. The AI ​​model takes the user's health data as input and creates a meal plan considering appropriate nutritional balance. The input is health information data, and the output is the generated meal plan.

[0128] Step 3:

[0129] The device obtains the user's location information. Using this location information, the server collects information on leftover food from local restaurants and bars via the Google Maps API, etc. The input is location information, and the output is data on leftover food from local restaurants and bars.

[0130] Step 4:

[0131] The server integrates available menus into the user's meal plan based on collected local food and beverage facility information. In this process, leftover food information and the meal plan are used as input, and an integrated meal plan and menu suggestions are output.

[0132] Step 5:

[0133] The device provides integrated information to the user visually and audibly. The user can provide feedback based on this display. The input is an integrated meal plan, and the output is the presentation of information to the user.

[0134] Step 6:

[0135] The user inputs feedback through their device. The server adjusts the meal plan based on this feedback. Natural language processing analyzes the feedback, and an adjusted meal plan is generated again. The input is user feedback, and the output is the adjusted meal plan.

[0136] Step 7:

[0137] The server stores user selections and feedback history, which is used to optimize future recommendations. An AI algorithm analyzes the historical data to generate next-time recommendations tailored to the user's preferences and health needs. The input is historical data, and the output is an optimized recommendation model.

[0138] Step 8:

[0139] Users can place instant orders through food delivery services based on integrated meal plans. This feature allows users to quickly obtain their meals. The input is the adjusted meal plan, and the output is the completed meal order.

[0140] 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.

[0141] This invention not only provides an individually optimized meal plan tailored to the user's health condition, but also uses an emotion engine to recognize the user's emotional state and present meal suggestions and restaurant information that take this into account, thereby offering more suitable recommendations. The system operates as follows:

[0142] First, the user starts by powering on the device and logging into the system. The device analyzes the user's emotional state through voice input and camera, and sends this information to the server. The server combines the health information provided by the user with emotional data obtained from the emotion engine to generate more personalized meal suggestions. For example, if the user is feeling stressed, the server will prioritize recipes using ingredients that have a relaxing effect.

[0143] The server also obtains information on the remaining food at local restaurants based on the user's location. Based on the analysis results from the emotion engine, it evaluates whether the dishes at a particular restaurant match the user's current emotional state and adjusts their priority. For example, if the user is in a cheerful mood, it will suggest restaurants with a more lively atmosphere.

[0144] The generated meal suggestions and selected restaurant information are provided to the user via audio and visual means through the terminal. The user inputs feedback on this information into the terminal and sends it to the server. The server readjusts the meal suggestions based on the feedback and sentiment history and sends the final suggestion to the terminal. This final suggestion becomes a meal plan that the user can implement, and includes a specific shopping list and cooking instructions.

[0145] Furthermore, emotional history and feedback information are stored in a database and used to make future system recommendations more accurate. This allows users to receive meal plans that best suit their health condition and emotions, supporting a healthier and more satisfying eating lifestyle.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user starts up the device and logs into the system. The device sends the login information to the server for user authentication.

[0149] Step 2:

[0150] The device uses its camera and microphone to capture the user's face and voice, and an emotion engine analyzes the user's emotional state. The analysis results are then sent to a server.

[0151] Step 3:

[0152] The server retrieves user health information from the database and integrates it with emotional data sent from the emotion engine.

[0153] Step 4:

[0154] The server uses an AI algorithm to generate personalized meal suggestions based on acquired health and emotional data. Depending on the user's emotional state, it prioritizes the inclusion of specific ingredients and dishes.

[0155] Step 5:

[0156] The user's location information is obtained from the device and sent to the server. Based on this information, the server consults a database of local restaurants and collects information on remaining food supplies.

[0157] Step 6:

[0158] The server prioritizes information about restaurants and bars based on the user's emotional state. For example, if the user wants to relax, it will select a restaurant that is suitable for that purpose.

[0159] Step 7:

[0160] The generated meal suggestions and prioritized restaurant information are sent from the server to the terminal. The terminal then presents this information to the user both audibly and visually.

[0161] Step 8:

[0162] The user provides feedback on the information presented or requests additional adjustments from the device. This feedback is sent to the server.

[0163] Step 9:

[0164] The server readjusts meal suggestions based on user feedback and generates new suggestions as needed. The adjusted information is then resent to the terminal.

[0165] Step 10:

[0166] The device presents the user with final suggestions. Based on these, the user can purchase ingredients and make reservations at restaurants, supporting a healthy eating lifestyle.

[0167] (Example 2)

[0168] 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".

[0169] In modern life, it is difficult to provide appropriate meal suggestions tailored to individual health conditions and emotions. Traditional systems could provide meal plans that took into account users' health data, but they could not provide suggestions that reflected users' emotions and psychological states. Furthermore, selecting dining establishments was difficult, as it was hard to choose options that suited users' moods, and there was room for improvement there. As a result, users sometimes felt dissatisfied because they could not obtain meals or environments that were well-suited to their stress levels and mood swings.

[0170] 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.

[0171] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state and collecting data, and an acquisition means for acquiring remaining meal information from local dining locations based on the user's location information. This makes it possible to propose meal plans and dining locations that are best suited not only to the user's health state but also to their emotional state.

[0172] A "user" is an individual who uses the system to input health information and emotional status, and receives suggestions for meal plans and dining locations.

[0173] "Health information" refers to data about the user's physical condition, which is used to optimize meal plans.

[0174] A "personally optimized meal plan" is a meal plan specifically tailored based on the user's health information and emotional state.

[0175] "Emotional state" refers to information about the user's psychological and emotional condition, which is taken into consideration when the system selects meal plans and dining locations.

[0176] "Analysis methods" refer to technical techniques that analyze data such as the user's voice and facial expressions to detect their emotional state.

[0177] "Location information" refers to data that indicates the user's current location and is used to select dining and drinking locations.

[0178] "Local dining establishments" refer to nearby restaurants and bars identified based on the user's location information.

[0179] "Remaining food information" refers to information about the food available at local restaurants and bars.

[0180] "Generation means" refers to methods and technologies for creating individually optimized meal plans based on the user's health information.

[0181] "Means of provision" refers to devices and technologies for presenting generated meal plans and acquired information on dining locations to users.

[0182] "Priority measures" refer to a method of determining the priority of meal suggestions and dining location information by taking into account the user's emotional state.

[0183] "Adjustment methods" refer to the methods and processes for modifying meal plans based on feedback from users.

[0184] "Data storage means" refers to a system-based method for saving users' emotional history and usage history to improve the accuracy of future suggestions.

[0185] This invention is a system that provides personalized meal plans and dining location suggestions that take into account the user's health information and emotional state. The invention requires a terminal, a server, and associated software.

[0186] The process begins when the user powers on the device and logs into the system. The device is equipped with a camera and microphone, which are used to analyze the user's facial expressions and voice. Specifically, voice recognition software and facial recognition algorithms are used for the analysis. This collects data about the user's emotional state. Subsequently, the analyzed data, along with health information, is transmitted to a server via the internet.

[0187] The server generates a personalized meal plan by combining the user's health information and emotional state, referencing a database. This generation process utilizes generative AI models and machine learning algorithms. Furthermore, the server searches a database of local restaurants based on the user's current location. The retrieved restaurant information is then prioritized according to the user's emotional state, selecting an appropriate dining establishment.

[0188] The selected meal plan and dining location information are provided to the user via audio and visual means through the device. This allows the user to receive suggestions for meals and dining locations optimized for their health and emotional state. For example, using a prompt such as, "I'm feeling a bit low on energy today, so please recommend some energy-boosting food and a relaxing cafe," the system will provide appropriate suggestions.

[0189] Users can input feedback on the suggested information into their device and send it back to the server. The server uses this feedback to accumulate data and improve the accuracy of future suggestions. This allows users to continuously receive improved services.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The user starts up the terminal and logs into the system. The terminal receives the user ID and password as input and authenticates the user through an authentication program. If authentication is successful, the terminal loads the user profile information into its internal memory.

[0193] Step 2:

[0194] The device uses its camera and microphone to collect the user's facial expressions and voice. This data is then used as input by emotion analysis software within the device, which analyzes the data and outputs the user's emotional state. Specifically, a facial expression recognition algorithm analyzes micro-expressions, and a voice recognition system analyzes the tone of voice to evaluate emotions.

[0195] Step 3:

[0196] The terminal sends the user's emotional state (analysis result) and pre-entered health information to the server. The server receives this information and queries the database using each as input. As output, a personalized meal plan, taking into account the user's health information and emotional state, is generated by a generation AI model.

[0197] Step 4:

[0198] To obtain the user's location information, the device uses a GPS module. This location information is sent to the server as input. The server accesses a database of local food and beverage establishments and retrieves information on remaining meals. As output, the retrieved local food and beverage establishment information is prioritized based on the user's emotional state.

[0199] Step 5:

[0200] The server sends the generated meal suggestions and preferred dining establishment information to the terminal. The terminal retrieves this information and presents it to the user both audibly and visually. The user can view the visualized suggestions through the terminal's screen or voice assistant.

[0201] Step 6:

[0202] Users input feedback on suggested meal options and restaurant information into their terminals. This feedback is sent to the server, which updates its database using the feedback as input and makes adjustments for future suggestions as output. The accumulated data is used to improve future services.

[0203] (Application Example 2)

[0204] 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".

[0205] In today's living environment, providing meal plans tailored to individual health and emotional states is challenging. In particular, the fixed nature of meal and dining suggestions is problematic given the constantly changing emotional states of users. Furthermore, there is a lack of systems that utilize user preferences and location information to provide real-time, optimized suggestions. Therefore, there is a need for means to support users from both a health and emotional perspective.

[0206] 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.

[0207] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state from voice and video input and generating emotional data, and an adaptation means for combining the generated emotional data and health information to adapt a meal plan suitable for the user. This makes it possible to provide a meal plan that is tailored to the user's health and emotional state.

[0208] "Health information" refers to data about the user's physical condition and is primarily used to optimize meal plans.

[0209] "Emotional state" refers to data that indicates the user's psychological or mental state, and is acquired through voice or video input.

[0210] A "meal plan" is a personalized meal plan tailored to the user's health information and emotional state, and its specific content includes ingredients and recipes.

[0211] "Generation means" refers to a device or program that has the function of generating an individually optimized meal plan based on the user's health information.

[0212] "Analysis means" refers to a device or program that analyzes the user's emotional state using voice input and video, and generates the results as data.

[0213] "Adaptation means" refers to a device or program that combines generated emotional data and health information to select or adjust the optimal meal plan for the user.

[0214] "Acquisition means" refers to a device or program that has the function of acquiring remaining food data and facility information from local food and beverage establishments based on the user's location information.

[0215] "Providing means" refers to a device or program that has the function of providing users with generated meal plans and acquired information on dining facilities in both audio and visual formats.

[0216] "Adjustment means" refers to a device or program that has the function of modifying or restructuring the meal plan in response to user feedback.

[0217] A "data storage means" refers to a device or program that has a database function to save users' usage history and feedback information, and to make more accurate suggestions for future use.

[0218] This system primarily operates by leveraging terminals, servers, and user feedback. The terminals monitor the user's emotional state using voice input and cameras, collecting data for analysis. This data is sent to a server in the cloud and analyzed by a generative AI model. The software used includes "Google Cloud Speech-to-Text" for speech recognition, "OpenCV" for video analysis, and "Google Cloud Natural Language AI" for sentiment analysis. The server stores health information data and combines it with sentiment data to optimize meal plans.

[0219] The server utilizes the user's location information to acquire data in real time from local restaurants and bars. For data acquisition, it uses the Google Maps API for location services. During this process, the server integrates the data and generates an optimal meal plan based on the user's emotional state and health information. Specifically, if an emotional state indicating stress is detected, the server will suggest a plan that includes relaxing herbal teas and light meals.

[0220] The generated meal plan is provided to the user visually and audibly through their device. The user interface utilizes Firebase to update information in real time. Users provide feedback on the suggestions via voice and text, which the server stores as data to improve the quality of future suggestions.

[0221] For example, if a user is feeling fatigued from working remotely, the system might prompt them with, "Are you feeling tired today? If you'd like to relax, how about taking a break at a nearby cafe?" This prompt aims to provide effective suggestions tailored to the user's current situation and mood.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The device captures the user's voice and video. Input consists of audio data from the microphone and video data from the camera. The device then formats this data appropriately as an initial process, preparing it for transmission to the server for sentiment analysis.

[0225] Step 2:

[0226] Audio and video data transmitted from the device are received by the server. The server uses "Google Cloud Speech-to-Text" to convert the audio data into text, and then uses "Google Cloud Natural Language AI" to analyze the emotional state from that text. In addition, "OpenCV" is used to recognize emotions from facial expressions and other elements of the video data. Based on these analysis results, the server generates the user's emotional data.

[0227] Step 3:

[0228] The server retrieves individual health data from the user's health information database. The input is the health information stored in the database, and the output is the health data necessary to generate an individually optimized meal plan. This health data is then combined with the emotional data obtained in step 2.

[0229] Step 4:

[0230] The server uses the Google Maps API to retrieve local food and beverage establishment data based on the user's location. Once location information is entered, food information and suggested restaurants are output. This retrieved data is then combined with sentiment data to suggest meal plans.

[0231] Step 5:

[0232] The server generates a meal plan and restaurant information optimized for the user. Inputs include health data, emotional data, and restaurant data, while output is a specific meal plan proposal including ingredient lists and restaurant lists. The generated plan is sent to the terminal.

[0233] Step 6:

[0234] The terminal receives meal plans and restaurant information sent from the server and presents them to the user visually and audibly. At this time, it waits for the user's reaction and selections and records feedback as needed.

[0235] Step 7:

[0236] The user takes action based on the system's suggestions, and the terminal records the result as feedback. The input is user feedback data, which is sent to the server and stored in a database. This information is used to improve the accuracy of suggestions in the future.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] [Second Embodiment]

[0241] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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".

[0253] To implement this invention, the user first accesses the system using a terminal and logs in. After logging in, the terminal sends the user's health information to the server, which then generates individually optimized meal plans based on this information. The server considers the user's health condition and nutritional needs using, for example, an AI-based algorithm, and generates multiple recipe suggestions based on the results.

[0254] Next, the server obtains location information such as the user's current location, and based on this, accesses a database of local restaurants to retrieve information on remaining food. This information includes, for example, information on unsold menu items and ingredients that can be offered at restaurants. The server combines this information with the user's meal preferences to create more comprehensive suggestions.

[0255] The terminal provides the user with generated meal suggestions and information on local restaurants and eateries, both visually and audibly. Based on the displayed information, the user can provide feedback tailored to their preferences and nutritional needs. For example, they can voice-input requests such as, "I'd like the salt content to be reduced a bit." Based on this feedback, the server uses an AI model to adjust the meal suggestions and sends them back to the terminal.

[0256] Furthermore, the device records the user's choices and usage history in a database. This data is analyzed by a server and used to optimize future suggestions for the user. Based on the ultimately suggested meal plans and information on restaurants, users can then plan their actual meals and purchase necessary ingredients.

[0257] For example, if a user requests a low-carbohydrate meal plan due to diabetes, the server will provide recipes using local low-carbohydrate ingredients and also suggest low-carbohydrate menus available at nearby restaurants. This allows users to easily utilize nearby restaurants in addition to cooking at home.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The user starts up the device, accesses the system, and enters their login information. The device then sends the login information to the server for authentication.

[0261] Step 2:

[0262] Once authentication is successful, the server retrieves the user's health information and usage history from the database. This retrieved information is used as material to generate personalized meal plans.

[0263] Step 3:

[0264] The server uses AI algorithms to generate multiple recipe suggestions based on the user's health status and nutritional needs. The generated recipe suggestions are then customized to take nutritional balance and health conditions into consideration.

[0265] Step 4:

[0266] The user's location information is obtained from the device and sent to the server. Based on this location information, the server refers to a database of local restaurants and bars to obtain information on available food in the area.

[0267] Step 5:

[0268] The server integrates the generated meal plans with information on remaining food at local restaurants and creates recommendations for the user. These recommendations combine the user's health needs with local ingredients and dishes.

[0269] Step 6:

[0270] The device visually and audibly presents the user with suggested meal options and information about dining establishments. Based on this, the user can provide feedback on the meal options and request customizations.

[0271] Step 7:

[0272] User feedback is sent from the device to the server. The server uses an AI algorithm to adjust the meal plan based on the feedback and make any necessary changes.

[0273] Step 8:

[0274] The adjusted meal plan is sent to the device and presented to the user again. The user can then review the final suggestion, create an actual meal plan, and purchase the necessary ingredients and menu items.

[0275] Step 9:

[0276] The server records the user's selected meal suggestions and usage history in a database. This data is then used to provide more personalized suggestions for future visits.

[0277] (Example 1)

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

[0279] In modern society, providing dietary suggestions that address health maintenance and specific nutritional needs is crucial, but there is a lack of systems that can quickly and accurately provide personalized suggestions tailored to each user's health condition and preferences. Furthermore, there is a need for methods that utilize ingredient information held by local food and beverage establishments to enhance convenience and sustainability.

[0280] 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.

[0281] In this invention, the server includes a generation means for generating individually optimized meal suggestions based on the user's health information, an information acquisition means for acquiring information on remaining ingredients from local food and beverage establishments based on the user's location information, and an information provision means for providing the generated meal suggestions and acquired food and beverage establishment information to the user via voice and visual means. This enables the rapid provision of personalized meal suggestions and allows for sustainable meal choices that utilize local resources.

[0282] "Generation method" refers to algorithms and technologies that automatically create individually optimized meal suggestions based on the user's health information.

[0283] The "information acquisition means" refers to a mechanism that utilizes the location information of the user to collect data on food ingredients and menus from local food and beverage establishments.

[0284] The "information provision means" refers to an interface or technology for presenting the generated meal plans and information on local food and beverage establishments to the user in audio or visual form.

[0285] The "adjustment means" refers to a mechanism that re-evaluates meal proposals based on the user's feedback and requests, and makes changes as necessary.

[0286] The "data storage means" refers to a technology that collects and stores the user's past selections and usage situations for use in optimizing future proposals.

[0287] The "AI update means" refers to a technology that utilizes artificial intelligence to continuously improve meal proposals automatically based on the user's preferences and feedback.

[0288] To implement this invention, the user first accesses the system using a mobile terminal or computer and logs in. In this process, the user's health information is transmitted from the terminal to the server. The health information includes, for example, data on weight, height, allergy information, and health status.

[0289] The server receives this health information, inputs it into a generative AI model utilizing AI, and generates an individually optimized meal plan. In this process, by inputting a specific prompt sentence such as "Please propose a low-carbohydrate and high-protein recipe" into the AI model, a proposal that meets specific dietary requirements can be obtained.

[0290] Furthermore, the terminal acquires the user's current location using a GPS module. The location information is transmitted to the server, and based on this, the server accesses the local food and beverage establishment database. There, information on available and remaining menus and food ingredients in the area is obtained.

[0291] The acquired information is integrated on a server, and the generated meal suggestions and local restaurant information are combined and provided to the user. On the terminal, the information is displayed visually on the screen, and the information is also guided by voice using a voice assistant.

[0292] When a user provides feedback on a meal plan, such as "I'd like the salt content reduced," that information is sent back to the server, and the meal plan is readjusted using an AI model. The adjusted content is then sent back to the device and presented to the user.

[0293] Furthermore, the terminal records the user's selection history and usage history in a database, and the server analyzes this data to provide optimized suggestions tailored to the user's preferences on subsequent visits.

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] The user accesses the system using a terminal and logs in. The terminal displays an interface for the user to enter their health information, and after successful login, this data is sent to the server. The entered health information may include weight, height, dietary preferences, and allergy information. This provides the server with basic data for making personalized recommendations.

[0297] Step 2:

[0298] The server uses a generative AI model to generate personalized meal plans based on the received health information. This process involves inputting the prompt "Please suggest low-carb, high-protein recipes" into the AI ​​model and outputting the meal plans suggested by the model. As for data processing, the AI ​​model calculates the nutritional value based on the input health information and selects appropriate recipes.

[0299] Step 3:

[0300] The terminal acquires the user's current location using the GPS function and transmits this to the server. This information is input into the server as location information and is used to access the database of food and beverage establishments in the area. The server acquires the menus and remaining food ingredient information of the food and beverage establishments around the user based on the location information and secures this as output data.

[0301] Step 4:

[0302] The server integrates the generated meal plan and the acquired food and beverage establishment information in the area to create an optimized meal proposal. Through this integration process, a meal proposal that associates the generated meal plan with the specific resources in the area is output.

[0303] Step 5:

[0304] The terminal visually displays the integrated meal proposal received from the server to the user and guides the user with voice. What is provided to the user are various menus and selectable options displayed on the screen. As a result, the user can plan meals based on specific options.

[0305] Step 6:

[0306] The user evaluates the proposed content based on the visual display and voice guidance and inputs feedback on the meal plan into the terminal. This feedback may be a request such as "I want less salt" and is recorded in the terminal by voice input or text input.

[0307] Step 7:

[0308] The terminal sends the feedback from the user back to the server. The server re-evaluates the meal plan using the AI model based on the received feedback, makes adjustments according to the feedback, and generates a new proposal. As a result, a newly adjusted meal proposal is output.

[0309] Step 8:

[0310] The device ultimately provides the user with a revised meal plan, allowing them to review optimized suggestions that reflect their feedback. This enables users to plan meals that suit their preferences and health needs.

[0311] Step 9:

[0312] The terminal records the user's selection and usage history in a database and sends this information to the server. The server analyzes the accumulated data and processes it to better adapt future suggestions to the user. This information is used in the next suggestion generation process.

[0313] (Application Example 1)

[0314] 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."

[0315] In modern society, the growing health consciousness and busy lifestyles have created a demand for easily accessible meal plans tailored to individual health conditions and nutritional needs, and for efficient implementation. However, conventional systems have struggled to provide personalized meal plans and immediate food delivery based on them. This has made the selection and rapid delivery of meals suitable for individual health needs a challenge.

[0316] 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.

[0317] In this invention, the server includes data processing means for generating individually optimized meal plans based on the user's health information, information gathering means for acquiring information on remaining food from local food establishments based on the user's location information, and information providing means for providing the generated meal plan and acquired food establishment information to the user via voice and visual means. This allows the user to efficiently obtain meal plans suited to their individual health condition and to place immediate orders through food delivery services.

[0318] A "user" is an individual who uses this system to access personalized meal plans and food delivery services.

[0319] "Health information" refers to data that shows the user's physical condition and nutritional needs, and is the basic information used to generate meal plans.

[0320] "Location information" refers to data that indicates the user's current geographical location and is used to obtain information from local food and beverage establishments.

[0321] "Local dining establishments" is a general term for restaurants and food service establishments that provide meals selected based on the user's location information.

[0322] "Remaining food information" refers to data on unsold or available ingredients at local food and beverage establishments.

[0323] "Data processing means" refers to a set of functions for generating individually optimized meal plans based on the user's health information.

[0324] "Information gathering means" refers to a group of functions that acquire information on remaining food from local food and beverage establishments based on the user's location information.

[0325] "Information provision means" refers to a set of functions that display the generated meal plan and acquired dining facility information to the user in both audio and visual formats.

[0326] "Adjustment mechanisms" refer to a set of functions that improve and update meal plans based on user feedback.

[0327] An "AI algorithm" is a computational method that uses artificial intelligence to analyze a user's data history and optimize future suggestions.

[0328] A "food delivery service" is a logistics service that allows users to order and have meals delivered instantly based on a generated meal plan.

[0329] To implement this invention, the user first accesses the system using a dedicated terminal and sends health information to the server. This health information includes the user's current health status and specific nutrient needs. Based on this information, the server uses an AI algorithm to generate an individually optimized meal plan.

[0330] This system includes a user interface that utilizes a smartphone, smart glasses, or head-mounted display. Through this hardware, the generated meal plan is provided to the user visually and audibly. For example, a mobile application developed using React Native or Flutter might be used.

[0331] When a user enables location services, the server uses the Google Maps API and other tools to collect information on remaining food items from local restaurants and bars. This information is then integrated into a meal plan generated by AI. The server uses AI models built in languages ​​such as Python and R to perform data calculations and adjust and optimize the plan.

[0332] Users can provide feedback based on the suggested meal plans and restaurant information. This feedback is analyzed using natural language processing technology and used to update the meal plan through adjustment mechanisms. For example, users can input a request such as "I'd like the salt content reduced a bit" via voice into their device.

[0333] The AI ​​model analyzes the user's past choices and feedback to optimize future suggestions. This enables personalized meal recommendations that better match the user's preferences and health needs.

[0334] One concrete example of its use is when a user who receives a high cholesterol level result in a health checkup uses this system to generate a low-cholesterol meal plan and instantly orders corresponding menu items from local restaurants.

[0335] An example of a prompt would be, "Based on my health information, please suggest some low-cholesterol diet options that I can use right now."

[0336] In this way, by generating meal plans tailored to the user's health condition and enabling immediate integration with local restaurants and food establishments, a system can be built that easily supports individual health management.

[0337] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0338] Step 1:

[0339] Users access the system using a dedicated terminal and input health information. This information is entered as data indicating the user's current health status and specific nutritional needs. The terminal sends this information to the server. The input is health information data, and the output is health data stored on the server.

[0340] Step 2:

[0341] The server uses an AI algorithm to generate an individually optimized meal plan based on the received health information. The AI ​​model takes the user's health data as input and creates a meal plan considering appropriate nutritional balance. The input is health information data, and the output is the generated meal plan.

[0342] Step 3:

[0343] The device obtains the user's location information. Using this location information, the server collects information on leftover food from local restaurants and bars via the Google Maps API, etc. The input is location information, and the output is data on leftover food from local restaurants and bars.

[0344] Step 4:

[0345] The server integrates available menus into the user's meal plan based on collected local food and beverage facility information. In this process, leftover food information and the meal plan are used as input, and an integrated meal plan and menu suggestions are output.

[0346] Step 5:

[0347] The device provides integrated information to the user visually and audibly. The user can provide feedback based on this display. The input is an integrated meal plan, and the output is the presentation of information to the user.

[0348] Step 6:

[0349] The user inputs feedback through their device. The server adjusts the meal plan based on this feedback. Natural language processing analyzes the feedback, and an adjusted meal plan is generated again. The input is user feedback, and the output is the adjusted meal plan.

[0350] Step 7:

[0351] The server stores user selections and feedback history, which is used to optimize future recommendations. An AI algorithm analyzes the historical data to generate next-time recommendations tailored to the user's preferences and health needs. The input is historical data, and the output is an optimized recommendation model.

[0352] Step 8:

[0353] Users can place instant orders through food delivery services based on integrated meal plans. This feature allows users to quickly obtain their meals. The input is the adjusted meal plan, and the output is the completed meal order.

[0354] 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.

[0355] This invention not only provides an individually optimized meal plan tailored to the user's health condition, but also uses an emotion engine to recognize the user's emotional state and present meal suggestions and restaurant information that take this into account, thereby offering more suitable recommendations. The system operates as follows:

[0356] First, the user starts by powering on the device and logging into the system. The device analyzes the user's emotional state through voice input and camera, and sends this information to the server. The server combines the health information provided by the user with emotional data obtained from the emotion engine to generate more personalized meal suggestions. For example, if the user is feeling stressed, the server will prioritize recipes using ingredients that have a relaxing effect.

[0357] The server also obtains information on the remaining food at local restaurants based on the user's location. Based on the analysis results from the emotion engine, it evaluates whether the dishes at a particular restaurant match the user's current emotional state and adjusts their priority. For example, if the user is in a cheerful mood, it will suggest restaurants with a more lively atmosphere.

[0358] The generated meal suggestions and selected restaurant information are provided to the user via audio and visual means through the terminal. The user inputs feedback on this information into the terminal and sends it to the server. The server readjusts the meal suggestions based on the feedback and sentiment history and sends the final suggestion to the terminal. This final suggestion becomes a meal plan that the user can implement, and includes a specific shopping list and cooking instructions.

[0359] Furthermore, emotional history and feedback information are stored in a database and used to make future system recommendations more accurate. This allows users to receive meal plans that best suit their health condition and emotions, supporting a healthier and more satisfying eating lifestyle.

[0360] The following describes the processing flow.

[0361] Step 1:

[0362] The user starts up the device and logs into the system. The device sends the login information to the server for user authentication.

[0363] Step 2:

[0364] The device uses its camera and microphone to capture the user's face and voice, and an emotion engine analyzes the user's emotional state. The analysis results are then sent to a server.

[0365] Step 3:

[0366] The server retrieves user health information from the database and integrates it with emotional data sent from the emotion engine.

[0367] Step 4:

[0368] The server uses an AI algorithm to generate personalized meal suggestions based on acquired health and emotional data. Depending on the user's emotional state, it prioritizes the inclusion of specific ingredients and dishes.

[0369] Step 5:

[0370] The user's location information is obtained from the device and sent to the server. Based on this information, the server consults a database of local restaurants and collects information on remaining food supplies.

[0371] Step 6:

[0372] The server prioritizes information about restaurants and bars based on the user's emotional state. For example, if the user wants to relax, it will select a restaurant that is suitable for that purpose.

[0373] Step 7:

[0374] The generated meal suggestions and prioritized restaurant information are sent from the server to the terminal. The terminal then presents this information to the user both audibly and visually.

[0375] Step 8:

[0376] The user provides feedback on the information presented or requests additional adjustments from the device. This feedback is sent to the server.

[0377] Step 9:

[0378] The server readjusts meal suggestions based on user feedback and generates new suggestions as needed. The adjusted information is then resent to the terminal.

[0379] Step 10:

[0380] The device presents the user with final suggestions. Based on these, the user can purchase ingredients and make reservations at restaurants, supporting a healthy eating lifestyle.

[0381] (Example 2)

[0382] 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".

[0383] In modern life, it is difficult to provide appropriate meal suggestions tailored to individual health conditions and emotions. Traditional systems could provide meal plans that took into account users' health data, but they could not provide suggestions that reflected users' emotions and psychological states. Furthermore, selecting dining establishments was difficult, as it was hard to choose options that suited users' moods, and there was room for improvement there. As a result, users sometimes felt dissatisfied because they could not obtain meals or environments that were well-suited to their stress levels and mood swings.

[0384] 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.

[0385] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state and collecting data, and an acquisition means for acquiring remaining meal information from local dining locations based on the user's location information. This makes it possible to propose meal plans and dining locations that are best suited not only to the user's health state but also to their emotional state.

[0386] A "user" is an individual who uses the system to input health information and emotional status, and receives suggestions for meal plans and dining locations.

[0387] "Health information" refers to data about the user's physical condition, which is used to optimize meal plans.

[0388] A "personally optimized meal plan" is a meal plan specifically tailored based on the user's health information and emotional state.

[0389] "Emotional state" refers to information about the user's psychological and emotional condition, which is taken into consideration when the system selects meal plans and dining locations.

[0390] "Analysis methods" refer to technical techniques that analyze data such as the user's voice and facial expressions to detect their emotional state.

[0391] "Location information" refers to data that indicates the user's current location and is used to select dining and drinking locations.

[0392] "Local dining establishments" refer to nearby restaurants and bars identified based on the user's location information.

[0393] "Remaining food information" refers to information about the food available at local restaurants and bars.

[0394] "Generation means" refers to methods and technologies for creating individually optimized meal plans based on the user's health information.

[0395] "Means of provision" refers to devices and technologies for presenting generated meal plans and acquired information on dining locations to users.

[0396] "Priority measures" refer to a method of determining the priority of meal suggestions and dining location information by taking into account the user's emotional state.

[0397] "Adjustment methods" refer to the methods and processes for modifying meal plans based on feedback from users.

[0398] "Data storage means" refers to a system-based method for saving users' emotional history and usage history to improve the accuracy of future suggestions.

[0399] This invention is a system that provides personalized meal plans and dining location suggestions that take into account the user's health information and emotional state. The invention requires a terminal, a server, and associated software.

[0400] The process begins when the user powers on the device and logs into the system. The device is equipped with a camera and microphone, which are used to analyze the user's facial expressions and voice. Specifically, voice recognition software and facial recognition algorithms are used for the analysis. This collects data about the user's emotional state. Subsequently, the analyzed data, along with health information, is transmitted to a server via the internet.

[0401] The server generates a personalized meal plan by combining the user's health information and emotional state, referencing a database. This generation process utilizes generative AI models and machine learning algorithms. Furthermore, the server searches a database of local restaurants based on the user's current location. The retrieved restaurant information is then prioritized according to the user's emotional state, selecting an appropriate dining establishment.

[0402] The selected meal plan and dining location information are provided to the user via audio and visual means through the device. This allows the user to receive suggestions for meals and dining locations optimized for their health and emotional state. For example, using a prompt such as, "I'm feeling a bit low on energy today, so please recommend some energy-boosting food and a relaxing cafe," the system will provide appropriate suggestions.

[0403] Users can input feedback on the suggested information into their device and send it back to the server. The server uses this feedback to accumulate data and improve the accuracy of future suggestions. This allows users to continuously receive improved services.

[0404] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0405] Step 1:

[0406] The user starts up the terminal and logs into the system. The terminal receives the user ID and password as input and authenticates the user through an authentication program. If authentication is successful, the terminal loads the user profile information into its internal memory.

[0407] Step 2:

[0408] The device uses its camera and microphone to collect the user's facial expressions and voice. This data is then used as input by emotion analysis software within the device, which analyzes the data and outputs the user's emotional state. Specifically, a facial expression recognition algorithm analyzes micro-expressions, and a voice recognition system analyzes the tone of voice to evaluate emotions.

[0409] Step 3:

[0410] The terminal sends the user's emotional state (analysis result) and pre-entered health information to the server. The server receives this information and queries the database using each as input. As output, a personalized meal plan, taking into account the user's health information and emotional state, is generated by a generation AI model.

[0411] Step 4:

[0412] To obtain the user's location information, the device uses a GPS module. This location information is sent to the server as input. The server accesses a database of local food and beverage establishments and retrieves information on remaining meals. As output, the retrieved local food and beverage establishment information is prioritized based on the user's emotional state.

[0413] Step 5:

[0414] The server sends the generated meal suggestions and preferred dining establishment information to the terminal. The terminal retrieves this information and presents it to the user both audibly and visually. The user can view the visualized suggestions through the terminal's screen or voice assistant.

[0415] Step 6:

[0416] Users input feedback on suggested meal options and restaurant information into their terminals. This feedback is sent to the server, which updates its database using the feedback as input and makes adjustments for future suggestions as output. The accumulated data is used to improve future services.

[0417] (Application Example 2)

[0418] 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."

[0419] In today's living environment, providing meal plans tailored to individual health and emotional states is challenging. In particular, the fixed nature of meal and dining suggestions is problematic given the constantly changing emotional states of users. Furthermore, there is a lack of systems that utilize user preferences and location information to provide real-time, optimized suggestions. Therefore, there is a need for means to support users from both a health and emotional perspective.

[0420] 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.

[0421] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state from voice and video input and generating emotional data, and an adaptation means for combining the generated emotional data and health information to adapt a meal plan suitable for the user. This makes it possible to provide a meal plan that is tailored to the user's health and emotional state.

[0422] "Health information" refers to data about the user's physical condition and is primarily used to optimize meal plans.

[0423] "Emotional state" refers to data that indicates the user's psychological or mental state, and is acquired through voice or video input.

[0424] A "meal plan" is a personalized meal plan tailored to the user's health information and emotional state, and its specific content includes ingredients and recipes.

[0425] "Generation means" refers to a device or program that has the function of generating an individually optimized meal plan based on the user's health information.

[0426] "Analysis means" refers to a device or program that analyzes the user's emotional state using voice input and video, and generates the results as data.

[0427] "Adaptation means" refers to a device or program that combines generated emotional data and health information to select or adjust the optimal meal plan for the user.

[0428] "Acquisition means" refers to a device or program that has the function of acquiring remaining food data and facility information from local food and beverage establishments based on the user's location information.

[0429] "Providing means" refers to a device or program that has the function of providing users with generated meal plans and acquired information on dining facilities in both audio and visual formats.

[0430] "Adjustment means" refers to a device or program that has the function of modifying or restructuring the meal plan in response to user feedback.

[0431] A "data storage means" refers to a device or program that has a database function to save users' usage history and feedback information, and to make more accurate suggestions for future use.

[0432] This system primarily operates by leveraging terminals, servers, and user feedback. The terminals monitor the user's emotional state using voice input and cameras, collecting data for analysis. This data is sent to a server in the cloud and analyzed by a generative AI model. The software used includes "Google Cloud Speech-to-Text" for speech recognition, "OpenCV" for video analysis, and "Google Cloud Natural Language AI" for sentiment analysis. The server stores health information data and combines it with sentiment data to optimize meal plans.

[0433] The server utilizes the user's location information to acquire data in real time from local restaurants and bars. For data acquisition, it uses the Google Maps API for location services. During this process, the server integrates the data and generates an optimal meal plan based on the user's emotional state and health information. Specifically, if an emotional state indicating stress is detected, the server will suggest a plan that includes relaxing herbal teas and light meals.

[0434] The generated meal plan is provided to the user visually and audibly through their device. The user interface utilizes Firebase to update information in real time. Users provide feedback on the suggestions via voice and text, which the server stores as data to improve the quality of future suggestions.

[0435] For example, if a user is feeling fatigued from working remotely, the system might prompt them with, "Are you feeling tired today? If you'd like to relax, how about taking a break at a nearby cafe?" This prompt aims to provide effective suggestions tailored to the user's current situation and mood.

[0436] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0437] Step 1:

[0438] The device captures the user's voice and video. Input consists of audio data from the microphone and video data from the camera. The device then formats this data appropriately as an initial process, preparing it for transmission to the server for sentiment analysis.

[0439] Step 2:

[0440] Audio and video data transmitted from the device are received by the server. The server uses "Google Cloud Speech-to-Text" to convert the audio data into text, and then uses "Google Cloud Natural Language AI" to analyze the emotional state from that text. In addition, "OpenCV" is used to recognize emotions from facial expressions and other elements of the video data. Based on these analysis results, the server generates the user's emotional data.

[0441] Step 3:

[0442] The server retrieves individual health data from the user's health information database. The input is the health information stored in the database, and the output is the health data necessary to generate an individually optimized meal plan. This health data is then combined with the emotional data obtained in step 2.

[0443] Step 4:

[0444] The server uses the Google Maps API to retrieve local food and beverage establishment data based on the user's location. Once location information is entered, food information and suggested restaurants are output. This retrieved data is then combined with sentiment data to suggest meal plans.

[0445] Step 5:

[0446] The server generates a meal plan and restaurant information optimized for the user. Inputs include health data, emotional data, and restaurant data, while output is a specific meal plan proposal including ingredient lists and restaurant lists. The generated plan is sent to the terminal.

[0447] Step 6:

[0448] The terminal receives meal plans and restaurant information sent from the server and presents them to the user visually and audibly. At this time, it waits for the user's reaction and selections and records feedback as needed.

[0449] Step 7:

[0450] The user takes action based on the system's suggestions, and the terminal records the result as feedback. The input is user feedback data, which is sent to the server and stored in a database. This information is used to improve the accuracy of suggestions in the future.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Third Embodiment]

[0455] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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".

[0467] To implement this invention, the user first accesses the system using a terminal and logs in. After logging in, the terminal sends the user's health information to the server, which then generates individually optimized meal plans based on this information. The server considers the user's health condition and nutritional needs using, for example, an AI-based algorithm, and generates multiple recipe suggestions based on the results.

[0468] Next, the server obtains location information such as the user's current location, and based on this, accesses a database of local restaurants to retrieve information on remaining food. This information includes, for example, information on unsold menu items and ingredients that can be offered at restaurants. The server combines this information with the user's meal preferences to create more comprehensive suggestions.

[0469] The terminal provides the user with generated meal suggestions and information on local restaurants and eateries, both visually and audibly. Based on the displayed information, the user can provide feedback tailored to their preferences and nutritional needs. For example, they can voice-input requests such as, "I'd like the salt content to be reduced a bit." Based on this feedback, the server uses an AI model to adjust the meal suggestions and sends them back to the terminal.

[0470] Furthermore, the device records the user's choices and usage history in a database. This data is analyzed by a server and used to optimize future suggestions for the user. Based on the ultimately suggested meal plans and information on restaurants, users can then plan their actual meals and purchase necessary ingredients.

[0471] For example, if a user requests a low-carbohydrate meal plan due to diabetes, the server will provide recipes using local low-carbohydrate ingredients and also suggest low-carbohydrate menus available at nearby restaurants. This allows users to easily utilize nearby restaurants in addition to cooking at home.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user starts up the device, accesses the system, and enters their login information. The device then sends the login information to the server for authentication.

[0475] Step 2:

[0476] Once authentication is successful, the server retrieves the user's health information and usage history from the database. This retrieved information is used as material to generate personalized meal plans.

[0477] Step 3:

[0478] The server uses AI algorithms to generate multiple recipe suggestions based on the user's health status and nutritional needs. The generated recipe suggestions are then customized to take nutritional balance and health conditions into consideration.

[0479] Step 4:

[0480] The user's location information is obtained from the device and sent to the server. Based on this location information, the server refers to a database of local restaurants and bars to obtain information on available food in the area.

[0481] Step 5:

[0482] The server integrates the generated meal plans with information on remaining food at local restaurants and creates recommendations for the user. These recommendations combine the user's health needs with local ingredients and dishes.

[0483] Step 6:

[0484] The device visually and audibly presents the user with suggested meal options and information about dining establishments. Based on this, the user can provide feedback on the meal options and request customizations.

[0485] Step 7:

[0486] User feedback is sent from the device to the server. The server uses an AI algorithm to adjust the meal plan based on the feedback and make any necessary changes.

[0487] Step 8:

[0488] The adjusted meal plan is sent to the device and presented to the user again. The user can then review the final suggestion, create an actual meal plan, and purchase the necessary ingredients and menu items.

[0489] Step 9:

[0490] The server records the user's selected meal suggestions and usage history in a database. This data is then used to provide more personalized suggestions for future visits.

[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] In modern society, providing dietary suggestions that address health maintenance and specific nutritional needs is crucial, but there is a lack of systems that can quickly and accurately provide personalized suggestions tailored to each user's health condition and preferences. Furthermore, there is a need for methods that utilize ingredient information held by local food and beverage establishments to enhance convenience and sustainability.

[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 generation means for generating individually optimized meal suggestions based on the user's health information, an information acquisition means for acquiring information on remaining ingredients from local food and beverage establishments based on the user's location information, and an information provision means for providing the generated meal suggestions and acquired food and beverage establishment information to the user via voice and visual means. This enables the rapid provision of personalized meal suggestions and allows for sustainable meal choices that utilize local resources.

[0496] "Generation method" refers to algorithms and technologies that automatically create individually optimized meal suggestions based on the user's health information.

[0497] "Information acquisition method" refers to a system that uses the user's location information to collect data on ingredients and menus from local food and beverage establishments.

[0498] "Information provision means" refers to interfaces and technologies for presenting generated meal plans and information about local food and beverage establishments to users in audio or visual formats.

[0499] "Adjustment mechanism" refers to a system that re-evaluates meal suggestions based on user feedback and requests, and makes changes as necessary.

[0500] "Data storage means" refers to technologies that collect and store users' past choices and usage patterns to optimize future suggestions.

[0501] "AI update method" refers to technology that uses artificial intelligence to automatically improve meal suggestions based on user preferences and feedback.

[0502] To implement this invention, the user first accesses the system using a mobile device or computer and logs in. During this process, the user's health information is transmitted from the device to the server. This health information includes, for example, weight, height, allergy information, and data related to their health status.

[0503] The server receives this health information and inputs it into an AI-powered generative model to generate personalized meal plans. In this process, by inputting specific prompts into the AI ​​model, such as "Please suggest low-carb, high-protein recipes," suggestions that meet specific dietary requirements can be obtained.

[0504] Furthermore, the device uses a GPS module to obtain the user's current location. The location information is sent to a server, which then accesses a database of local restaurants and bars. There, information about leftover menu items and ingredients available in the area is retrieved.

[0505] The acquired information is integrated on a server, and the generated meal suggestions and local restaurant information are combined and provided to the user. On the terminal, the information is displayed visually on the screen, and the information is also guided by voice using a voice assistant.

[0506] When a user provides feedback on a meal plan, such as "I'd like the salt content reduced," that information is sent back to the server, and the meal plan is readjusted using an AI model. The adjusted content is then sent back to the device and presented to the user.

[0507] Furthermore, the terminal records the user's selection history and usage history in a database, and the server analyzes this data to provide optimized suggestions tailored to the user's preferences on subsequent visits.

[0508] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0509] Step 1:

[0510] The user accesses the system using a terminal and logs in. The terminal displays an interface for the user to enter their health information, and after successful login, this data is sent to the server. The entered health information may include weight, height, dietary preferences, and allergy information. This provides the server with basic data for making personalized recommendations.

[0511] Step 2:

[0512] The server uses a generative AI model to generate personalized meal plans based on the received health information. This process involves inputting the prompt "Please suggest low-carb, high-protein recipes" into the AI ​​model and outputting the meal plans suggested by the model. As for data processing, the AI ​​model calculates the nutritional value based on the input health information and selects appropriate recipes.

[0513] Step 3:

[0514] The device uses GPS to obtain the user's current location and sends it to the server. This information is entered into the server as location data and used to access a database of local food and beverage establishments. Based on the location data, the server retrieves menus and remaining food information from food and beverage establishments near the user and saves this as output data.

[0515] Step 4:

[0516] The server integrates the generated meal plans with acquired local food and beverage facility information to create optimized meal recommendations. This integration process outputs meal recommendations that link the generated meal plans to specific local resources.

[0517] Step 5:

[0518] The terminal visually displays and provides voice guidance on integrated meal suggestions received from the server. Users are presented with various menus and selectable options displayed on the screen. This allows users to plan their meals based on concrete choices.

[0519] Step 6:

[0520] Users evaluate the suggested menu based on visual displays and audio guidance, and input feedback on the meal plan into the device. This feedback may include requests such as "I would like the salt content reduced," and is recorded on the device via voice or text input.

[0521] Step 7:

[0522] The device sends user feedback back to the server. Based on the received feedback, the server uses an AI model to re-evaluate the meal suggestions, makes adjustments based on the feedback, and generates new suggestions. As a result, newly adjusted meal suggestions are output.

[0523] Step 8:

[0524] The device ultimately provides the user with a revised meal plan, allowing them to review optimized suggestions that reflect their feedback. This enables users to plan meals that suit their preferences and health needs.

[0525] Step 9:

[0526] The terminal records the user's selection and usage history in a database and sends this information to the server. The server analyzes the accumulated data and processes it to better adapt future suggestions to the user. This information is used in the next suggestion generation process.

[0527] (Application Example 1)

[0528] 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."

[0529] In modern society, the growing health consciousness and busy lifestyles have created a demand for easily accessible meal plans tailored to individual health conditions and nutritional needs, and for efficient implementation. However, conventional systems have struggled to provide personalized meal plans and immediate food delivery based on them. This has made the selection and rapid delivery of meals suitable for individual health needs a challenge.

[0530] 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.

[0531] In this invention, the server includes data processing means for generating individually optimized meal plans based on the user's health information, information gathering means for acquiring information on remaining food from local food establishments based on the user's location information, and information providing means for providing the generated meal plan and acquired food establishment information to the user via voice and visual means. This allows the user to efficiently obtain meal plans suited to their individual health condition and to place immediate orders through food delivery services.

[0532] A "user" is an individual who uses this system to access personalized meal plans and food delivery services.

[0533] "Health information" refers to data that shows the user's physical condition and nutritional needs, and is the basic information used to generate meal plans.

[0534] "Location information" refers to data that indicates the user's current geographical location and is used to obtain information from local food and beverage establishments.

[0535] "Local dining establishments" is a general term for restaurants and food service establishments that provide meals selected based on the user's location information.

[0536] "Remaining food information" refers to data on unsold or available ingredients at local food and beverage establishments.

[0537] "Data processing means" refers to a set of functions for generating individually optimized meal plans based on the user's health information.

[0538] "Information gathering means" refers to a group of functions that acquire information on remaining food from local food and beverage establishments based on the user's location information.

[0539] "Information provision means" refers to a set of functions that display the generated meal plan and acquired dining facility information to the user in both audio and visual formats.

[0540] "Adjustment mechanisms" refer to a set of functions that improve and update meal plans based on user feedback.

[0541] An "AI algorithm" is a computational method that uses artificial intelligence to analyze a user's data history and optimize future suggestions.

[0542] A "food delivery service" is a logistics service that allows users to order and have meals delivered instantly based on a generated meal plan.

[0543] To implement this invention, the user first accesses the system using a dedicated terminal and sends health information to the server. This health information includes the user's current health status and specific nutrient needs. Based on this information, the server uses an AI algorithm to generate an individually optimized meal plan.

[0544] This system includes a user interface that utilizes a smartphone, smart glasses, or head-mounted display. Through this hardware, the generated meal plan is provided to the user visually and audibly. For example, a mobile application developed using React Native or Flutter might be used.

[0545] When a user enables location services, the server uses the Google Maps API and other tools to collect information on remaining food items from local restaurants and bars. This information is then integrated into a meal plan generated by AI. The server uses AI models built in languages ​​such as Python and R to perform data calculations and adjust and optimize the plan.

[0546] Users can provide feedback based on the suggested meal plans and restaurant information. This feedback is analyzed using natural language processing technology and used to update the meal plan through adjustment mechanisms. For example, users can input a request such as "I'd like the salt content reduced a bit" via voice into their device.

[0547] The AI ​​model analyzes the user's past choices and feedback to optimize future suggestions. This enables personalized meal recommendations that better match the user's preferences and health needs.

[0548] One concrete example of its use is when a user who receives a high cholesterol level result in a health checkup uses this system to generate a low-cholesterol meal plan and instantly orders corresponding menu items from local restaurants.

[0549] An example of a prompt would be, "Based on my health information, please suggest some low-cholesterol diet options that I can use right now."

[0550] In this way, by generating meal plans tailored to the user's health condition and enabling immediate integration with local restaurants and food establishments, a system can be built that easily supports individual health management.

[0551] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0552] Step 1:

[0553] Users access the system using a dedicated terminal and input health information. This information is entered as data indicating the user's current health status and specific nutritional needs. The terminal sends this information to the server. The input is health information data, and the output is health data stored on the server.

[0554] Step 2:

[0555] The server uses an AI algorithm to generate an individually optimized meal plan based on the received health information. The AI ​​model takes the user's health data as input and creates a meal plan considering appropriate nutritional balance. The input is health information data, and the output is the generated meal plan.

[0556] Step 3:

[0557] The device obtains the user's location information. Using this location information, the server collects information on leftover food from local restaurants and bars via the Google Maps API, etc. The input is location information, and the output is data on leftover food from local restaurants and bars.

[0558] Step 4:

[0559] The server integrates available menus into the user's meal plan based on collected local food and beverage facility information. In this process, leftover food information and the meal plan are used as input, and an integrated meal plan and menu suggestions are output.

[0560] Step 5:

[0561] The device provides integrated information to the user visually and audibly. The user can provide feedback based on this display. The input is an integrated meal plan, and the output is the presentation of information to the user.

[0562] Step 6:

[0563] The user inputs feedback through their device. The server adjusts the meal plan based on this feedback. Natural language processing analyzes the feedback, and an adjusted meal plan is generated again. The input is user feedback, and the output is the adjusted meal plan.

[0564] Step 7:

[0565] The server stores user selections and feedback history, which is used to optimize future recommendations. An AI algorithm analyzes the historical data to generate next-time recommendations tailored to the user's preferences and health needs. The input is historical data, and the output is an optimized recommendation model.

[0566] Step 8:

[0567] Users can place instant orders through food delivery services based on integrated meal plans. This feature allows users to quickly obtain their meals. The input is the adjusted meal plan, and the output is the completed meal order.

[0568] 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.

[0569] This invention not only provides an individually optimized meal plan tailored to the user's health condition, but also uses an emotion engine to recognize the user's emotional state and present meal suggestions and restaurant information that take this into account, thereby offering more suitable recommendations. The system operates as follows:

[0570] First, the user starts by powering on the device and logging into the system. The device analyzes the user's emotional state through voice input and camera, and sends this information to the server. The server combines the health information provided by the user with emotional data obtained from the emotion engine to generate more personalized meal suggestions. For example, if the user is feeling stressed, the server will prioritize recipes using ingredients that have a relaxing effect.

[0571] The server also obtains information on the remaining food at local restaurants based on the user's location. Based on the analysis results from the emotion engine, it evaluates whether the dishes at a particular restaurant match the user's current emotional state and adjusts their priority. For example, if the user is in a cheerful mood, it will suggest restaurants with a more lively atmosphere.

[0572] The generated meal suggestions and selected restaurant information are provided to the user via audio and visual means through the terminal. The user inputs feedback on this information into the terminal and sends it to the server. The server readjusts the meal suggestions based on the feedback and sentiment history and sends the final suggestion to the terminal. This final suggestion becomes a meal plan that the user can implement, and includes a specific shopping list and cooking instructions.

[0573] Furthermore, emotional history and feedback information are stored in a database and used to make future system recommendations more accurate. This allows users to receive meal plans that best suit their health condition and emotions, supporting a healthier and more satisfying eating lifestyle.

[0574] The following describes the processing flow.

[0575] Step 1:

[0576] The user starts up the device and logs into the system. The device sends the login information to the server for user authentication.

[0577] Step 2:

[0578] The device uses its camera and microphone to capture the user's face and voice, and an emotion engine analyzes the user's emotional state. The analysis results are then sent to a server.

[0579] Step 3:

[0580] The server retrieves user health information from the database and integrates it with emotional data sent from the emotion engine.

[0581] Step 4:

[0582] The server uses an AI algorithm to generate personalized meal suggestions based on acquired health and emotional data. Depending on the user's emotional state, it prioritizes the inclusion of specific ingredients and dishes.

[0583] Step 5:

[0584] The user's location information is obtained from the device and sent to the server. Based on this information, the server consults a database of local restaurants and collects information on remaining food supplies.

[0585] Step 6:

[0586] The server prioritizes information about restaurants and bars based on the user's emotional state. For example, if the user wants to relax, it will select a restaurant that is suitable for that purpose.

[0587] Step 7:

[0588] The generated meal suggestions and prioritized restaurant information are sent from the server to the terminal. The terminal then presents this information to the user both audibly and visually.

[0589] Step 8:

[0590] The user provides feedback on the information presented or requests additional adjustments from the device. This feedback is sent to the server.

[0591] Step 9:

[0592] The server readjusts meal suggestions based on user feedback and generates new suggestions as needed. The adjusted information is then resent to the terminal.

[0593] Step 10:

[0594] The device presents the user with final suggestions. Based on these, the user can purchase ingredients and make reservations at restaurants, supporting a healthy eating lifestyle.

[0595] (Example 2)

[0596] 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."

[0597] In modern life, it is difficult to provide appropriate meal suggestions tailored to individual health conditions and emotions. Traditional systems could provide meal plans that took into account users' health data, but they could not provide suggestions that reflected users' emotions and psychological states. Furthermore, selecting dining establishments was difficult, as it was hard to choose options that suited users' moods, and there was room for improvement there. As a result, users sometimes felt dissatisfied because they could not obtain meals or environments that were well-suited to their stress levels and mood swings.

[0598] 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.

[0599] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state and collecting data, and an acquisition means for acquiring remaining meal information from local dining locations based on the user's location information. This makes it possible to propose meal plans and dining locations that are best suited not only to the user's health state but also to their emotional state.

[0600] A "user" is an individual who uses the system to input health information and emotional status, and receives suggestions for meal plans and dining locations.

[0601] "Health information" refers to data about the user's physical condition, which is used to optimize meal plans.

[0602] A "personally optimized meal plan" is a meal plan specifically tailored based on the user's health information and emotional state.

[0603] "Emotional state" refers to information about the user's psychological and emotional condition, which is taken into consideration when the system selects meal plans and dining locations.

[0604] "Analysis methods" refer to technical techniques that analyze data such as the user's voice and facial expressions to detect their emotional state.

[0605] "Location information" refers to data that indicates the user's current location and is used to select dining and drinking locations.

[0606] "Local dining establishments" refer to nearby restaurants and bars identified based on the user's location information.

[0607] "Remaining food information" refers to information about the food available at local restaurants and bars.

[0608] "Generation means" refers to methods and technologies for creating individually optimized meal plans based on the user's health information.

[0609] "Means of provision" refers to devices and technologies for presenting generated meal plans and acquired information on dining locations to users.

[0610] "Priority measures" refer to a method of determining the priority of meal suggestions and dining location information by taking into account the user's emotional state.

[0611] "Adjustment methods" refer to the methods and processes for modifying meal plans based on feedback from users.

[0612] "Data storage means" refers to a system-based method for saving users' emotional history and usage history to improve the accuracy of future suggestions.

[0613] This invention is a system that provides personalized meal plans and dining location suggestions that take into account the user's health information and emotional state. The invention requires a terminal, a server, and associated software.

[0614] The process begins when the user powers on the device and logs into the system. The device is equipped with a camera and microphone, which are used to analyze the user's facial expressions and voice. Specifically, voice recognition software and facial recognition algorithms are used for the analysis. This collects data about the user's emotional state. Subsequently, the analyzed data, along with health information, is transmitted to a server via the internet.

[0615] The server generates a personalized meal plan by combining the user's health information and emotional state, referencing a database. This generation process utilizes generative AI models and machine learning algorithms. Furthermore, the server searches a database of local restaurants based on the user's current location. The retrieved restaurant information is then prioritized according to the user's emotional state, selecting an appropriate dining establishment.

[0616] The selected meal plan and dining location information are provided to the user via audio and visual means through the device. This allows the user to receive suggestions for meals and dining locations optimized for their health and emotional state. For example, using a prompt such as, "I'm feeling a bit low on energy today, so please recommend some energy-boosting food and a relaxing cafe," the system will provide appropriate suggestions.

[0617] Users can input feedback on the suggested information into their device and send it back to the server. The server uses this feedback to accumulate data and improve the accuracy of future suggestions. This allows users to continuously receive improved services.

[0618] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0619] Step 1:

[0620] The user starts up the terminal and logs into the system. The terminal receives the user ID and password as input and authenticates the user through an authentication program. If authentication is successful, the terminal loads the user profile information into its internal memory.

[0621] Step 2:

[0622] The device uses its camera and microphone to collect the user's facial expressions and voice. This data is then used as input by emotion analysis software within the device, which analyzes the data and outputs the user's emotional state. Specifically, a facial expression recognition algorithm analyzes micro-expressions, and a voice recognition system analyzes the tone of voice to evaluate emotions.

[0623] Step 3:

[0624] The terminal sends the user's emotional state (analysis result) and pre-entered health information to the server. The server receives this information and queries the database using each as input. As output, a personalized meal plan, taking into account the user's health information and emotional state, is generated by a generation AI model.

[0625] Step 4:

[0626] To obtain the user's location information, the device uses a GPS module. This location information is sent to the server as input. The server accesses a database of local food and beverage establishments and retrieves information on remaining meals. As output, the retrieved local food and beverage establishment information is prioritized based on the user's emotional state.

[0627] Step 5:

[0628] The server sends the generated meal suggestions and preferred dining establishment information to the terminal. The terminal retrieves this information and presents it to the user both audibly and visually. The user can view the visualized suggestions through the terminal's screen or voice assistant.

[0629] Step 6:

[0630] Users input feedback on suggested meal options and restaurant information into their terminals. This feedback is sent to the server, which updates its database using the feedback as input and makes adjustments for future suggestions as output. The accumulated data is used to improve future services.

[0631] (Application Example 2)

[0632] 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."

[0633] In today's living environment, providing meal plans tailored to individual health and emotional states is challenging. In particular, the fixed nature of meal and dining suggestions is problematic given the constantly changing emotional states of users. Furthermore, there is a lack of systems that utilize user preferences and location information to provide real-time, optimized suggestions. Therefore, there is a need for means to support users from both a health and emotional perspective.

[0634] 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.

[0635] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state from voice and video input and generating emotional data, and an adaptation means for combining the generated emotional data and health information to adapt a meal plan suitable for the user. This makes it possible to provide a meal plan that is tailored to the user's health and emotional state.

[0636] "Health information" refers to data about the user's physical condition and is primarily used to optimize meal plans.

[0637] "Emotional state" refers to data that indicates the user's psychological or mental state, and is acquired through voice or video input.

[0638] A "meal plan" is a personalized meal plan tailored to the user's health information and emotional state, and its specific content includes ingredients and recipes.

[0639] "Generation means" refers to a device or program that has the function of generating an individually optimized meal plan based on the user's health information.

[0640] "Analysis means" refers to a device or program that analyzes the user's emotional state using voice input and video, and generates the results as data.

[0641] "Adaptation means" refers to a device or program that combines generated emotional data and health information to select or adjust the optimal meal plan for the user.

[0642] "Acquisition means" refers to a device or program that has the function of acquiring remaining food data and facility information from local food and beverage establishments based on the user's location information.

[0643] "Providing means" refers to a device or program that has the function of providing users with generated meal plans and acquired information on dining facilities in both audio and visual formats.

[0644] "Adjustment means" refers to a device or program that has the function of modifying or restructuring the meal plan in response to user feedback.

[0645] A "data storage means" refers to a device or program that has a database function to save users' usage history and feedback information, and to make more accurate suggestions for future use.

[0646] This system primarily operates by leveraging terminals, servers, and user feedback. The terminals monitor the user's emotional state using voice input and cameras, collecting data for analysis. This data is sent to a server in the cloud and analyzed by a generative AI model. The software used includes "Google Cloud Speech-to-Text" for speech recognition, "OpenCV" for video analysis, and "Google Cloud Natural Language AI" for sentiment analysis. The server stores health information data and combines it with sentiment data to optimize meal plans.

[0647] The server utilizes the user's location information to acquire data in real time from local restaurants and bars. For data acquisition, it uses the Google Maps API for location services. During this process, the server integrates the data and generates an optimal meal plan based on the user's emotional state and health information. Specifically, if an emotional state indicating stress is detected, the server will suggest a plan that includes relaxing herbal teas and light meals.

[0648] The generated meal plan is provided to the user visually and audibly through their device. The user interface utilizes Firebase to update information in real time. Users provide feedback on the suggestions via voice and text, which the server stores as data to improve the quality of future suggestions.

[0649] For example, if a user is feeling fatigued from working remotely, the system might prompt them with, "Are you feeling tired today? If you'd like to relax, how about taking a break at a nearby cafe?" This prompt aims to provide effective suggestions tailored to the user's current situation and mood.

[0650] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0651] Step 1:

[0652] The device captures the user's voice and video. Input consists of audio data from the microphone and video data from the camera. The device then formats this data appropriately as an initial process, preparing it for transmission to the server for sentiment analysis.

[0653] Step 2:

[0654] Audio and video data transmitted from the device are received by the server. The server uses "Google Cloud Speech-to-Text" to convert the audio data into text, and then uses "Google Cloud Natural Language AI" to analyze the emotional state from that text. In addition, "OpenCV" is used to recognize emotions from facial expressions and other elements of the video data. Based on these analysis results, the server generates the user's emotional data.

[0655] Step 3:

[0656] The server retrieves individual health data from the user's health information database. The input is the health information stored in the database, and the output is the health data necessary to generate an individually optimized meal plan. This health data is then combined with the emotional data obtained in step 2.

[0657] Step 4:

[0658] The server uses the Google Maps API to retrieve local food and beverage establishment data based on the user's location. Once location information is entered, food information and suggested restaurants are output. This retrieved data is then combined with sentiment data to suggest meal plans.

[0659] Step 5:

[0660] The server generates a meal plan and restaurant information optimized for the user. Inputs include health data, emotional data, and restaurant data, while output is a specific meal plan proposal including ingredient lists and restaurant lists. The generated plan is sent to the terminal.

[0661] Step 6:

[0662] The terminal receives meal plans and restaurant information sent from the server and presents them to the user visually and audibly. At this time, it waits for the user's reaction and selections and records feedback as needed.

[0663] Step 7:

[0664] The user takes action based on the system's suggestions, and the terminal records the result as feedback. The input is user feedback data, which is sent to the server and stored in a database. This information is used to improve the accuracy of suggestions in the future.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] [Fourth Embodiment]

[0669] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0670] 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.

[0671] 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).

[0672] 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.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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".

[0682] To implement this invention, the user first accesses the system using a terminal and logs in. After logging in, the terminal sends the user's health information to the server, which then generates individually optimized meal plans based on this information. The server considers the user's health condition and nutritional needs using, for example, an AI-based algorithm, and generates multiple recipe suggestions based on the results.

[0683] Next, the server obtains location information such as the user's current location, and based on this, accesses a database of local restaurants to retrieve information on remaining food. This information includes, for example, information on unsold menu items and ingredients that can be offered at restaurants. The server combines this information with the user's meal preferences to create more comprehensive suggestions.

[0684] The terminal provides the user with generated meal suggestions and information on local restaurants and eateries, both visually and audibly. Based on the displayed information, the user can provide feedback tailored to their preferences and nutritional needs. For example, they can voice-input requests such as, "I'd like the salt content to be reduced a bit." Based on this feedback, the server uses an AI model to adjust the meal suggestions and sends them back to the terminal.

[0685] Furthermore, the device records the user's choices and usage history in a database. This data is analyzed by a server and used to optimize future suggestions for the user. Based on the ultimately suggested meal plans and information on restaurants, users can then plan their actual meals and purchase necessary ingredients.

[0686] For example, if a user requests a low-carbohydrate meal plan due to diabetes, the server will provide recipes using local low-carbohydrate ingredients and also suggest low-carbohydrate menus available at nearby restaurants. This allows users to easily utilize nearby restaurants in addition to cooking at home.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The user starts up the device, accesses the system, and enters their login information. The device then sends the login information to the server for authentication.

[0690] Step 2:

[0691] Once authentication is successful, the server retrieves the user's health information and usage history from the database. This retrieved information is used as material to generate personalized meal plans.

[0692] Step 3:

[0693] The server uses AI algorithms to generate multiple recipe suggestions based on the user's health status and nutritional needs. The generated recipe suggestions are then customized to take nutritional balance and health conditions into consideration.

[0694] Step 4:

[0695] The user's location information is obtained from the device and sent to the server. Based on this location information, the server refers to a database of local restaurants and bars to obtain information on available food in the area.

[0696] Step 5:

[0697] The server integrates the generated meal plans with information on remaining food at local restaurants and creates recommendations for the user. These recommendations combine the user's health needs with local ingredients and dishes.

[0698] Step 6:

[0699] The device visually and audibly presents the user with suggested meal options and information about dining establishments. Based on this, the user can provide feedback on the meal options and request customizations.

[0700] Step 7:

[0701] User feedback is sent from the device to the server. The server uses an AI algorithm to adjust the meal plan based on the feedback and make any necessary changes.

[0702] Step 8:

[0703] The adjusted meal plan is sent to the device and presented to the user again. The user can then review the final suggestion, create an actual meal plan, and purchase the necessary ingredients and menu items.

[0704] Step 9:

[0705] The server records the user's selected meal suggestions and usage history in a database. This data is then used to provide more personalized suggestions for future visits.

[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] In modern society, providing dietary suggestions that address health maintenance and specific nutritional needs is crucial, but there is a lack of systems that can quickly and accurately provide personalized suggestions tailored to each user's health condition and preferences. Furthermore, there is a need for methods that utilize ingredient information held by local food and beverage establishments to enhance convenience and sustainability.

[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 generation means for generating individually optimized meal suggestions based on the user's health information, an information acquisition means for acquiring information on remaining ingredients from local food and beverage establishments based on the user's location information, and an information provision means for providing the generated meal suggestions and acquired food and beverage establishment information to the user via voice and visual means. This enables the rapid provision of personalized meal suggestions and allows for sustainable meal choices that utilize local resources.

[0711] "Generation method" refers to algorithms and technologies that automatically create individually optimized meal suggestions based on the user's health information.

[0712] "Information acquisition method" refers to a system that uses the user's location information to collect data on ingredients and menus from local food and beverage establishments.

[0713] "Information provision means" refers to interfaces and technologies for presenting generated meal plans and information about local food and beverage establishments to users in audio or visual formats.

[0714] "Adjustment mechanism" refers to a system that re-evaluates meal suggestions based on user feedback and requests, and makes changes as necessary.

[0715] "Data storage means" refers to technologies that collect and store users' past choices and usage patterns to optimize future suggestions.

[0716] "AI update method" refers to technology that uses artificial intelligence to automatically improve meal suggestions based on user preferences and feedback.

[0717] To implement this invention, the user first accesses the system using a mobile device or computer and logs in. During this process, the user's health information is transmitted from the device to the server. This health information includes, for example, weight, height, allergy information, and data related to their health status.

[0718] The server receives this health information and inputs it into an AI-powered generative model to generate personalized meal plans. In this process, by inputting specific prompts into the AI ​​model, such as "Please suggest low-carb, high-protein recipes," suggestions that meet specific dietary requirements can be obtained.

[0719] Furthermore, the device uses a GPS module to obtain the user's current location. The location information is sent to a server, which then accesses a database of local restaurants and bars. There, information about leftover menu items and ingredients available in the area is retrieved.

[0720] The acquired information is integrated on a server, and the generated meal suggestions and local restaurant information are combined and provided to the user. On the terminal, the information is displayed visually on the screen, and the information is also guided by voice using a voice assistant.

[0721] When a user provides feedback on a meal plan, such as "I'd like the salt content reduced," that information is sent back to the server, and the meal plan is readjusted using an AI model. The adjusted content is then sent back to the device and presented to the user.

[0722] Furthermore, the terminal records the user's selection history and usage history in a database, and the server analyzes this data to provide optimized suggestions tailored to the user's preferences on subsequent visits.

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] The user accesses the system using a terminal and logs in. The terminal displays an interface for the user to enter their health information, and after successful login, this data is sent to the server. The entered health information may include weight, height, dietary preferences, and allergy information. This provides the server with basic data for making personalized recommendations.

[0726] Step 2:

[0727] The server uses a generative AI model to generate personalized meal plans based on the received health information. This process involves inputting the prompt "Please suggest low-carb, high-protein recipes" into the AI ​​model and outputting the meal plans suggested by the model. As for data processing, the AI ​​model calculates the nutritional value based on the input health information and selects appropriate recipes.

[0728] Step 3:

[0729] The device uses GPS to obtain the user's current location and sends it to the server. This information is entered into the server as location data and used to access a database of local food and beverage establishments. Based on the location data, the server retrieves menus and remaining food information from food and beverage establishments near the user and saves this as output data.

[0730] Step 4:

[0731] The server integrates the generated meal plans with acquired local food and beverage facility information to create optimized meal recommendations. This integration process outputs meal recommendations that link the generated meal plans to specific local resources.

[0732] Step 5:

[0733] The terminal visually displays and provides voice guidance on integrated meal suggestions received from the server. Users are presented with various menus and selectable options displayed on the screen. This allows users to plan their meals based on concrete choices.

[0734] Step 6:

[0735] Users evaluate the suggested menu based on visual displays and audio guidance, and input feedback on the meal plan into the device. This feedback may include requests such as "I would like the salt content reduced," and is recorded on the device via voice or text input.

[0736] Step 7:

[0737] The device sends user feedback back to the server. Based on the received feedback, the server uses an AI model to re-evaluate the meal suggestions, makes adjustments based on the feedback, and generates new suggestions. As a result, newly adjusted meal suggestions are output.

[0738] Step 8:

[0739] The device ultimately provides the user with a revised meal plan, allowing them to review optimized suggestions that reflect their feedback. This enables users to plan meals that suit their preferences and health needs.

[0740] Step 9:

[0741] The terminal records the user's selection and usage history in a database and sends this information to the server. The server analyzes the accumulated data and processes it to better adapt future suggestions to the user. This information is used in the next suggestion generation process.

[0742] (Application Example 1)

[0743] 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".

[0744] In modern society, the growing health consciousness and busy lifestyles have created a demand for easily accessible meal plans tailored to individual health conditions and nutritional needs, and for efficient implementation. However, conventional systems have struggled to provide personalized meal plans and immediate food delivery based on them. This has made the selection and rapid delivery of meals suitable for individual health needs a challenge.

[0745] 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.

[0746] In this invention, the server includes data processing means for generating individually optimized meal plans based on the user's health information, information gathering means for acquiring information on remaining food from local food establishments based on the user's location information, and information providing means for providing the generated meal plan and acquired food establishment information to the user via voice and visual means. This allows the user to efficiently obtain meal plans suited to their individual health condition and to place immediate orders through food delivery services.

[0747] A "user" is an individual who uses this system to access personalized meal plans and food delivery services.

[0748] "Health information" refers to data that shows the user's physical condition and nutritional needs, and is the basic information used to generate meal plans.

[0749] "Location information" refers to data that indicates the user's current geographical location and is used to obtain information from local food and beverage establishments.

[0750] "Local dining establishments" is a general term for restaurants and food service establishments that provide meals selected based on the user's location information.

[0751] "Remaining food information" refers to data on unsold or available ingredients at local food and beverage establishments.

[0752] "Data processing means" refers to a set of functions for generating individually optimized meal plans based on the user's health information.

[0753] "Information gathering means" refers to a group of functions that acquire information on remaining food from local food and beverage establishments based on the user's location information.

[0754] "Information provision means" refers to a set of functions that display the generated meal plan and acquired dining facility information to the user in both audio and visual formats.

[0755] "Adjustment mechanisms" refer to a set of functions that improve and update meal plans based on user feedback.

[0756] An "AI algorithm" is a computational method that uses artificial intelligence to analyze a user's data history and optimize future suggestions.

[0757] A "food delivery service" is a logistics service that allows users to order and have meals delivered instantly based on a generated meal plan.

[0758] To implement this invention, the user first accesses the system using a dedicated terminal and sends health information to the server. This health information includes the user's current health status and specific nutrient needs. Based on this information, the server uses an AI algorithm to generate an individually optimized meal plan.

[0759] This system includes a user interface that utilizes a smartphone, smart glasses, or head-mounted display. Through this hardware, the generated meal plan is provided to the user visually and audibly. For example, a mobile application developed using React Native or Flutter might be used.

[0760] When a user enables location services, the server uses the Google Maps API and other tools to collect information on remaining food items from local restaurants and bars. This information is then integrated into a meal plan generated by AI. The server uses AI models built in languages ​​such as Python and R to perform data calculations and adjust and optimize the plan.

[0761] Users can provide feedback based on the suggested meal plans and restaurant information. This feedback is analyzed using natural language processing technology and used to update the meal plan through adjustment mechanisms. For example, users can input a request such as "I'd like the salt content reduced a bit" via voice into their device.

[0762] The AI ​​model analyzes the user's past choices and feedback to optimize future suggestions. This enables personalized meal recommendations that better match the user's preferences and health needs.

[0763] One concrete example of its use is when a user who receives a high cholesterol level result in a health checkup uses this system to generate a low-cholesterol meal plan and instantly orders corresponding menu items from local restaurants.

[0764] An example of a prompt would be, "Based on my health information, please suggest some low-cholesterol diet options that I can use right now."

[0765] In this way, by generating meal plans tailored to the user's health condition and enabling immediate integration with local restaurants and food establishments, a system can be built that easily supports individual health management.

[0766] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0767] Step 1:

[0768] Users access the system using a dedicated terminal and input health information. This information is entered as data indicating the user's current health status and specific nutritional needs. The terminal sends this information to the server. The input is health information data, and the output is health data stored on the server.

[0769] Step 2:

[0770] The server uses an AI algorithm to generate an individually optimized meal plan based on the received health information. The AI ​​model takes the user's health data as input and creates a meal plan considering appropriate nutritional balance. The input is health information data, and the output is the generated meal plan.

[0771] Step 3:

[0772] The device obtains the user's location information. Using this location information, the server collects information on leftover food from local restaurants and bars via the Google Maps API, etc. The input is location information, and the output is data on leftover food from local restaurants and bars.

[0773] Step 4:

[0774] The server integrates available menus into the user's meal plan based on collected local food and beverage facility information. In this process, leftover food information and the meal plan are used as input, and an integrated meal plan and menu suggestions are output.

[0775] Step 5:

[0776] The device provides integrated information to the user visually and audibly. The user can provide feedback based on this display. The input is an integrated meal plan, and the output is the presentation of information to the user.

[0777] Step 6:

[0778] The user inputs feedback through their device. The server adjusts the meal plan based on this feedback. Natural language processing analyzes the feedback, and an adjusted meal plan is generated again. The input is user feedback, and the output is the adjusted meal plan.

[0779] Step 7:

[0780] The server stores user selections and feedback history, which is used to optimize future recommendations. An AI algorithm analyzes the historical data to generate next-time recommendations tailored to the user's preferences and health needs. The input is historical data, and the output is an optimized recommendation model.

[0781] Step 8:

[0782] Users can place instant orders through food delivery services based on integrated meal plans. This feature allows users to quickly obtain their meals. The input is the adjusted meal plan, and the output is the completed meal order.

[0783] 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.

[0784] This invention not only provides an individually optimized meal plan tailored to the user's health condition, but also uses an emotion engine to recognize the user's emotional state and present meal suggestions and restaurant information that take this into account, thereby offering more suitable recommendations. The system operates as follows:

[0785] First, the user starts by powering on the device and logging into the system. The device analyzes the user's emotional state through voice input and camera, and sends this information to the server. The server combines the health information provided by the user with emotional data obtained from the emotion engine to generate more personalized meal suggestions. For example, if the user is feeling stressed, the server will prioritize recipes using ingredients that have a relaxing effect.

[0786] The server also obtains information on the remaining food at local restaurants based on the user's location. Based on the analysis results from the emotion engine, it evaluates whether the dishes at a particular restaurant match the user's current emotional state and adjusts their priority. For example, if the user is in a cheerful mood, it will suggest restaurants with a more lively atmosphere.

[0787] The generated meal suggestions and selected restaurant information are provided to the user via audio and visual means through the terminal. The user inputs feedback on this information into the terminal and sends it to the server. The server readjusts the meal suggestions based on the feedback and sentiment history and sends the final suggestion to the terminal. This final suggestion becomes a meal plan that the user can implement, and includes a specific shopping list and cooking instructions.

[0788] Furthermore, emotional history and feedback information are stored in a database and used to make future system recommendations more accurate. This allows users to receive meal plans that best suit their health condition and emotions, supporting a healthier and more satisfying eating lifestyle.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] The user starts up the device and logs into the system. The device sends the login information to the server for user authentication.

[0792] Step 2:

[0793] The device uses its camera and microphone to capture the user's face and voice, and an emotion engine analyzes the user's emotional state. The analysis results are then sent to a server.

[0794] Step 3:

[0795] The server retrieves user health information from the database and integrates it with emotional data sent from the emotion engine.

[0796] Step 4:

[0797] The server uses an AI algorithm to generate personalized meal suggestions based on acquired health and emotional data. Depending on the user's emotional state, it prioritizes the inclusion of specific ingredients and dishes.

[0798] Step 5:

[0799] The user's location information is obtained from the device and sent to the server. Based on this information, the server consults a database of local restaurants and collects information on remaining food supplies.

[0800] Step 6:

[0801] The server prioritizes information about restaurants and bars based on the user's emotional state. For example, if the user wants to relax, it will select a restaurant that is suitable for that purpose.

[0802] Step 7:

[0803] The generated meal suggestions and prioritized restaurant information are sent from the server to the terminal. The terminal then presents this information to the user both audibly and visually.

[0804] Step 8:

[0805] The user provides feedback on the information presented or requests additional adjustments from the device. This feedback is sent to the server.

[0806] Step 9:

[0807] The server readjusts meal suggestions based on user feedback and generates new suggestions as needed. The adjusted information is then resent to the terminal.

[0808] Step 10:

[0809] The device presents the user with final suggestions. Based on these, the user can purchase ingredients and make reservations at restaurants, supporting a healthy eating lifestyle.

[0810] (Example 2)

[0811] 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".

[0812] In modern life, it is difficult to provide appropriate meal suggestions tailored to individual health conditions and emotions. Traditional systems could provide meal plans that took into account users' health data, but they could not provide suggestions that reflected users' emotions and psychological states. Furthermore, selecting dining establishments was difficult, as it was hard to choose options that suited users' moods, and there was room for improvement there. As a result, users sometimes felt dissatisfied because they could not obtain meals or environments that were well-suited to their stress levels and mood swings.

[0813] 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.

[0814] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state and collecting data, and an acquisition means for acquiring remaining meal information from local dining locations based on the user's location information. This makes it possible to propose meal plans and dining locations that are best suited not only to the user's health state but also to their emotional state.

[0815] A "user" is an individual who uses the system to input health information and emotional status, and receives suggestions for meal plans and dining locations.

[0816] "Health information" refers to data about the user's physical condition, which is used to optimize meal plans.

[0817] A "personally optimized meal plan" is a meal plan specifically tailored based on the user's health information and emotional state.

[0818] "Emotional state" refers to information about the user's psychological and emotional condition, which is taken into consideration when the system selects meal plans and dining locations.

[0819] "Analysis methods" refer to technical techniques that analyze data such as the user's voice and facial expressions to detect their emotional state.

[0820] "Location information" refers to data that indicates the user's current location and is used to select dining and drinking locations.

[0821] "Local dining establishments" refer to nearby restaurants and bars identified based on the user's location information.

[0822] "Remaining food information" refers to information about the food available at local restaurants and bars.

[0823] "Generation means" refers to methods and technologies for creating individually optimized meal plans based on the user's health information.

[0824] "Means of provision" refers to devices and technologies for presenting generated meal plans and acquired information on dining locations to users.

[0825] "Priority measures" refer to a method of determining the priority of meal suggestions and dining location information by taking into account the user's emotional state.

[0826] "Adjustment methods" refer to the methods and processes for modifying meal plans based on feedback from users.

[0827] "Data storage means" refers to a system-based method for saving users' emotional history and usage history to improve the accuracy of future suggestions.

[0828] This invention is a system that provides personalized meal plans and dining location suggestions that take into account the user's health information and emotional state. The invention requires a terminal, a server, and associated software.

[0829] The process begins when the user powers on the device and logs into the system. The device is equipped with a camera and microphone, which are used to analyze the user's facial expressions and voice. Specifically, voice recognition software and facial recognition algorithms are used for the analysis. This collects data about the user's emotional state. Subsequently, the analyzed data, along with health information, is transmitted to a server via the internet.

[0830] The server generates a personalized meal plan by combining the user's health information and emotional state, referencing a database. This generation process utilizes generative AI models and machine learning algorithms. Furthermore, the server searches a database of local restaurants based on the user's current location. The retrieved restaurant information is then prioritized according to the user's emotional state, selecting an appropriate dining establishment.

[0831] The selected meal plan and dining location information are provided to the user via audio and visual means through the device. This allows the user to receive suggestions for meals and dining locations optimized for their health and emotional state. For example, using a prompt such as, "I'm feeling a bit low on energy today, so please recommend some energy-boosting food and a relaxing cafe," the system will provide appropriate suggestions.

[0832] Users can input feedback on the suggested information into their device and send it back to the server. The server uses this feedback to accumulate data and improve the accuracy of future suggestions. This allows users to continuously receive improved services.

[0833] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0834] Step 1:

[0835] The user starts up the terminal and logs into the system. The terminal receives the user ID and password as input and authenticates the user through an authentication program. If authentication is successful, the terminal loads the user profile information into its internal memory.

[0836] Step 2:

[0837] The device uses its camera and microphone to collect the user's facial expressions and voice. This data is then used as input by emotion analysis software within the device, which analyzes the data and outputs the user's emotional state. Specifically, a facial expression recognition algorithm analyzes micro-expressions, and a voice recognition system analyzes the tone of voice to evaluate emotions.

[0838] Step 3:

[0839] The terminal sends the user's emotional state (analysis result) and pre-entered health information to the server. The server receives this information and queries the database using each as input. As output, a personalized meal plan, taking into account the user's health information and emotional state, is generated by a generation AI model.

[0840] Step 4:

[0841] To obtain the user's location information, the device uses a GPS module. This location information is sent to the server as input. The server accesses a database of local food and beverage establishments and retrieves information on remaining meals. As output, the retrieved local food and beverage establishment information is prioritized based on the user's emotional state.

[0842] Step 5:

[0843] The server sends the generated meal suggestions and preferred dining establishment information to the terminal. The terminal retrieves this information and presents it to the user both audibly and visually. The user can view the visualized suggestions through the terminal's screen or voice assistant.

[0844] Step 6:

[0845] Users input feedback on suggested meal options and restaurant information into their terminals. This feedback is sent to the server, which updates its database using the feedback as input and makes adjustments for future suggestions as output. The accumulated data is used to improve future services.

[0846] (Application Example 2)

[0847] 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".

[0848] In today's living environment, providing meal plans tailored to individual health and emotional states is challenging. In particular, the fixed nature of meal and dining suggestions is problematic given the constantly changing emotional states of users. Furthermore, there is a lack of systems that utilize user preferences and location information to provide real-time, optimized suggestions. Therefore, there is a need for means to support users from both a health and emotional perspective.

[0849] 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.

[0850] In this invention, the server includes a generation means for generating an individually optimized meal plan based on the user's health information, an analysis means for analyzing the user's emotional state from voice and video input and generating emotional data, and an adaptation means for combining the generated emotional data and health information to adapt a meal plan suitable for the user. This makes it possible to provide a meal plan that is tailored to the user's health and emotional state.

[0851] "Health information" refers to data about the user's physical condition and is primarily used to optimize meal plans.

[0852] "Emotional state" refers to data that indicates the user's psychological or mental state, and is acquired through voice or video input.

[0853] A "meal plan" is a personalized meal plan tailored to the user's health information and emotional state, and its specific content includes ingredients and recipes.

[0854] "Generation means" refers to a device or program that has the function of generating an individually optimized meal plan based on the user's health information.

[0855] "Analysis means" refers to a device or program that analyzes the user's emotional state using voice input and video, and generates the results as data.

[0856] "Adaptation means" refers to a device or program that combines generated emotional data and health information to select or adjust the optimal meal plan for the user.

[0857] "Acquisition means" refers to a device or program that has the function of acquiring remaining food data and facility information from local food and beverage establishments based on the user's location information.

[0858] "Providing means" refers to a device or program that has the function of providing users with generated meal plans and acquired information on dining facilities in both audio and visual formats.

[0859] "Adjustment means" refers to a device or program that has the function of modifying or restructuring the meal plan in response to user feedback.

[0860] A "data storage means" refers to a device or program that has a database function to save users' usage history and feedback information, and to make more accurate suggestions for future use.

[0861] This system primarily operates by leveraging terminals, servers, and user feedback. The terminals monitor the user's emotional state using voice input and cameras, collecting data for analysis. This data is sent to a server in the cloud and analyzed by a generative AI model. The software used includes "Google Cloud Speech-to-Text" for speech recognition, "OpenCV" for video analysis, and "Google Cloud Natural Language AI" for sentiment analysis. The server stores health information data and combines it with sentiment data to optimize meal plans.

[0862] The server utilizes the user's location information to acquire data in real time from local restaurants and bars. For data acquisition, it uses the Google Maps API for location services. During this process, the server integrates the data and generates an optimal meal plan based on the user's emotional state and health information. Specifically, if an emotional state indicating stress is detected, the server will suggest a plan that includes relaxing herbal teas and light meals.

[0863] The generated meal plan is provided to the user visually and audibly through their device. The user interface utilizes Firebase to update information in real time. Users provide feedback on the suggestions via voice and text, which the server stores as data to improve the quality of future suggestions.

[0864] For example, if a user is feeling fatigued from working remotely, the system might prompt them with, "Are you feeling tired today? If you'd like to relax, how about taking a break at a nearby cafe?" This prompt aims to provide effective suggestions tailored to the user's current situation and mood.

[0865] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0866] Step 1:

[0867] The device captures the user's voice and video. Input consists of audio data from the microphone and video data from the camera. The device then formats this data appropriately as an initial process, preparing it for transmission to the server for sentiment analysis.

[0868] Step 2:

[0869] Audio and video data transmitted from the device are received by the server. The server uses "Google Cloud Speech-to-Text" to convert the audio data into text, and then uses "Google Cloud Natural Language AI" to analyze the emotional state from that text. In addition, "OpenCV" is used to recognize emotions from facial expressions and other elements of the video data. Based on these analysis results, the server generates the user's emotional data.

[0870] Step 3:

[0871] The server retrieves individual health data from the user's health information database. The input is the health information stored in the database, and the output is the health data necessary to generate an individually optimized meal plan. This health data is then combined with the emotional data obtained in step 2.

[0872] Step 4:

[0873] The server uses the Google Maps API to retrieve local food and beverage establishment data based on the user's location. Once location information is entered, food information and suggested restaurants are output. This retrieved data is then combined with sentiment data to suggest meal plans.

[0874] Step 5:

[0875] The server generates a meal plan and restaurant information optimized for the user. Inputs include health data, emotional data, and restaurant data, while output is a specific meal plan proposal including ingredient lists and restaurant lists. The generated plan is sent to the terminal.

[0876] Step 6:

[0877] The terminal receives meal plans and restaurant information sent from the server and presents them to the user visually and audibly. At this time, it waits for the user's reaction and selections and records feedback as needed.

[0878] Step 7:

[0879] The user takes action based on the system's suggestions, and the terminal records the result as feedback. The input is user feedback data, which is sent to the server and stored in a database. This information is used to improve the accuracy of suggestions in the future.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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."

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] The following is further disclosed regarding the embodiments described above.

[0902] (Claim 1)

[0903] A generation method for generating individually optimized meal plans based on the user's health information,

[0904] A means of acquiring information on remaining food from local food and beverage establishments based on the user's location information,

[0905] A means of providing the generated meal plan and acquired dining facility information to the user in audio and visual formats,

[0906] A means of adjusting meal plans based on user input,

[0907] A data storage method that accumulates user usage history and optimizes guidance for future visits,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, which takes additional nutritional information into consideration when adjusting meal plans.

[0911] (Claim 3)

[0912] The system according to claim 1, which prioritizes and presents acquired local food and beverage establishment information based on the user's preferences.

[0913] "Example 1"

[0914] (Claim 1)

[0915] A generation method for generating individually optimized meal plans based on the user's health information,

[0916] An information acquisition method that obtains information on remaining food ingredients from local food and beverage establishments based on the user's location information,

[0917] An information provision means that provides users with generated meal suggestions and acquired information on food and beverage establishments in audio and visual formats,

[0918] A means of adjusting meal suggestions based on user input,

[0919] A data storage method that accumulates users' past usage data to optimize suggestions for future use,

[0920] An AI update method that uses an AI model to update meal suggestions based on user preferences,

[0921] A system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, which takes additional nutritional information into consideration when adjusting meal suggestions.

[0924] (Claim 3)

[0925] The system according to claim 1, which presents information on local food and beverage establishments in a prioritized manner based on the user's preferences.

[0926] "Application Example 1"

[0927] (Claim 1)

[0928] A data processing method that generates an individually optimized meal plan based on the user's health information,

[0929] A means of collecting information to obtain information on remaining food from local food and beverage establishments based on the user's location information,

[0930] An information provision means that provides users with generated meal plans and acquired information on dining facilities in audio and visual form,

[0931] A means of adjusting the meal plan based on user feedback,

[0932] A data analysis method that uses an AI algorithm to record the user's usage history and optimize future suggestions,

[0933] An ordering method that provides meal menus that can be ordered instantly through a food delivery service,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, which takes additional nutritional information into consideration when adjusting a meal plan.

[0937] (Claim 3)

[0938] The system according to claim 1, which prioritizes and presents acquired local food and beverage establishment information based on the user's preferences and health information.

[0939] "Example 2 of combining an emotion engine"

[0940] (Claim 1)

[0941] A generation method for generating an individually optimized meal plan based on the user's health information,

[0942] An analytical means for analyzing the emotional state of users and collecting data,

[0943] A means of acquiring remaining food information from local restaurants based on the user's location information,

[0944] A means of providing the user with a generated meal plan and acquired dining location information via audio and visual means,

[0945] A prioritization method that takes user sentiment data into consideration and presents meal suggestions and dining location information with priority,

[0946] An adjustment mechanism that adjusts the meal plan based on user input,

[0947] A data storage method that accumulates user emotional history and usage history to optimize guidance for future visits,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, which takes additional nutritional information into consideration when adjusting a meal plan.

[0951] (Claim 3)

[0952] The system according to claim 1, which suggests a dining location suitable for the user's current emotional state based on data obtained through emotion analysis.

[0953] "Application example 2 when combining with an emotional engine"

[0954] (Claim 1)

[0955] A generation method for generating an individually optimized meal plan based on the user's health information,

[0956] An analysis means that analyzes the user's emotional state from audio and video input and generates emotional data,

[0957] A means of adapting a meal plan to suit the user by combining generated emotional data and health information,

[0958] A means of acquiring data on remaining food from local food and beverage establishments based on the user's location information,

[0959] A means of providing the generated meal plan and acquired dining facility information to the user via audio and visual means,

[0960] An adjustment mechanism that adjusts the meal plan based on user input,

[0961] A data storage method that accumulates user usage history and optimizes guidance for future visits,

[0962] A system that includes this.

[0963] (Claim 2)

[0964] The system according to claim 1, which takes into account additional nutritional data and emotional data when adjusting meal plans.

[0965] (Claim 3)

[0966] The system according to claim 1, which prioritizes and presents acquired local food and beverage establishment information based on the user's emotional state and preferences. [Explanation of symbols]

[0967] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A generation method for generating individually optimized meal plans based on the user's health information, A means of acquiring information on remaining food from local food and beverage establishments based on the user's location information, A means of providing the generated meal plan and acquired dining facility information to the user in audio and visual formats, A means of adjusting meal plans based on user input, A data storage method that accumulates user usage history and optimizes guidance for future visits, A system that includes this.

2. The system according to claim 1, which takes additional nutritional information into consideration when adjusting meal plans.

3. The system according to claim 1, which prioritizes and presents acquired local food and beverage establishment information based on the user's preferences.

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