system

Abstract

We provide the system. [Solution] A communication means for receiving animal health check information and intake history data, An analytical means that generates an optimal nutrition plan for an individual based on the information received by the aforementioned communication means, A display means for presenting the nutrition plan generated by the aforementioned analysis means, A management system for ordering food based on the aforementioned nutrition plan and automating its delivery, A learning method for receiving feedback on the animal's response and changes in health status and applying it to the next nutrition plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Conventionally, in maintaining the health and nutritional management of animals, standardized commercially available foods are often used, and there is a problem that an optimal nutrition plan reflecting the characteristics and health status of each individual is not provided. In addition, specialized knowledge is required for animal owners to independently adjust the nutritional balance, and the time burden is large. Furthermore, there is a problem that it is difficult to respond promptly to changes in the health status of animals.

Means for Solving the Problems

[0005] This invention provides an analytical means that receives animal health check information and intake history data via communication means and generates an optimal nutrition plan for the individual based on this information. The generated nutrition plan is presented through a display means, allowing the animal owner to easily confirm it. Furthermore, a management means automates the ordering and delivery of food based on the nutrition plan, reducing the burden on the animal owner. In addition, a learning means is used to receive feedback on the animal's response and changes in health status, and this feedback is reflected in the next nutrition plan, thereby achieving continuous optimization.

[0006] "Communication means" refers to a device that has the function of receiving animal health check information and intake history data and transmitting it to other system components.

[0007] "Analysis means" refers to a device that has the function of generating an optimal nutrition plan for an individual animal based on the received health check information and intake history data.

[0008] A "display means" is a device that has the function of presenting and allowing the animal to confirm the nutritional plan generated by the analysis.

[0009] A "management device" is a device that has the function of automatically ordering and delivering food based on a nutrition plan.

[0010] A "learning tool" is a device that receives feedback on changes in an animal's response and health status, and uses that information to optimize and update the next nutrition plan. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The system for implementing this invention operates through the cooperation of a server, terminals, and users. The server plays a central role in this system, securely managing animal health check information and intake history data, and generating nutritional plans based on the analysis results.

[0033] First, the user inputs basic health information, preferences, and dietary history data for their animal through a terminal. The terminal then sends the data to the server using a secure protocol. The server stores the received data in a database and performs analysis using an AI model.

[0034] The server uses analytical tools to create a nutrition plan optimized for each individual animal. This plan includes necessary nutrients, suitable foods, and intake amounts. The generated plan is notified to the terminal and presented to the user.

[0035] Users can review the presented nutrition plan on their device and make adjustments to suit their preferences if necessary. The system then automatically orders food based on the nutrition plan and handles delivery via the server. Delivery is handled by partner logistics services, significantly reducing the user's effort.

[0036] Furthermore, users can send feedback from their device to the server regarding their pet's response to a new nutrition plan and changes in its health. The server uses this feedback to incorporate it into subsequent analyses, continuously optimizing the plan. This ensures that animal nutritional management is tailored to individual changes, providing continuous support for their health.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users use a terminal to input information about their animals' health checks, preferences, and past feeding history. This information is described in detail based on each input field.

[0040] Step 2:

[0041] The terminal sends the entered data to the server via a secure protocol. The server receives this data and stores it in a secure database.

[0042] Step 3:

[0043] The server passes the stored data to the AI ​​analysis module. The AI ​​applies machine learning algorithms to calculate the optimal nutrition plan for each individual.

[0044] Step 4:

[0045] The server sends the AI-generated nutrition plan to the device. The device then presents the plan to the user via push notification, allowing them to confirm its contents.

[0046] Step 5:

[0047] Users can review the proposed nutrition plan on their device and make adjustments as needed. The approved plan is then sent to the server.

[0048] Step 6:

[0049] The server automatically orders food based on the approved nutrition plan. It issues delivery instructions to partner logistics services and notifies the user of the delivery schedule.

[0050] Step 7:

[0051] The user receives the delivered food and feeds it to their animal. After eating, they observe the animal's reaction and health condition and send feedback from their device to the server.

[0052] Step 8:

[0053] The server incorporates the received feedback into its AI analysis module, which is then used to create the next nutrition plan. This ensures continuous improvement in nutritional management.

[0054] (Example 1)

[0055] 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."

[0056] In modern times, proper nutritional management is essential for maintaining pet health. However, traditional methods have presented challenges, such as difficulty in creating nutritional plans that accommodate individual pet differences, and the time and effort required of owners to prepare meals according to those plans. Furthermore, there is the problem of not being able to respond instantly to changes in a pet's health condition or preferences.

[0057] 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.

[0058] In this invention, the server includes communication means for receiving diagnostic information and intake history data of an organism, analysis means for generating an optimal nutrition plan for the individual, and presentation means for displaying the nutrition plan and allowing the user to confirm it. This enables optimal nutritional management that takes into account the individual differences of pets, reduces the burden on pet owners, and contributes to maintaining the health of pets.

[0059] "Living organism" refers to the animals that are the subject of this invention, and in particular includes dogs, cats, birds, and other animals kept as pets.

[0060] "Diagnostic information" refers to data that indicates the health status of an organism, and includes weight, age, health checkup results, allergy information, etc.

[0061] "Intake history data" refers to records of foods that an organism has consumed in the past, including the amount and frequency of consumption.

[0062] "Communication means" refers to technical means for sending and receiving data from an information processing device to a server, and includes information and communication technologies such as the Internet Protocol.

[0063] "Analysis means" refers to technical means for generating an optimal nutrition plan based on acquired data, and includes functions for performing analysis using artificial intelligence models, etc.

[0064] "Presentation means" refers to an interface that visually provides the generated nutrition plan to the user, and includes screen display and notification functions.

[0065] "Processing means" refers to the function of automatically ordering food based on a nutrition plan and handling delivery procedures through logistics services.

[0066] "Learning methods" refer to technical means that acquire feedback data on the responses and changes in the health status of organisms and use that data to inform future nutritional plans.

[0067] The term "computer system" refers to a system as a whole that includes a set of programs or hardware for integrating and executing these means.

[0068] The embodiment of this invention primarily utilizes a system in which a server, a terminal, and a user work in cooperation. This system incorporates hardware and software for efficiently managing biological diagnostic information and intake history data, and for generating nutritional plans.

[0069] The server plays a central role in this system, handling data management and processing. The server is equipped with a database management system (such as MySQL® or MongoDB) for securely storing data. The server also performs data analysis using artificial intelligence models, employing machine learning frameworks such as Tensorflow® or PyTorch. This allows the server to generate nutritional plans optimized for living organisms.

[0070] The terminal functions as an interface for users to input data and view results. Specifically, using an application installed on a smartphone or PC, users input pet health information, preferences, and intake history data into the terminal. The terminal then transmits this data to the server via a secure protocol (e.g., HTTPS).

[0071] Users are the primary beneficiaries of the system's convenience. They register their pet's health status and dietary preferences using a terminal and view the generated nutritional plan. Furthermore, they can fine-tune the plan according to their own and their pet's preferences and feed that information back into the system.

[0072] As a concrete example, a user enters a prompt message into the terminal such as, "My cat Taro has gained a little weight recently, so I would like him to eat a low-calorie diet." The server analyzes this information, generates an appropriate low-calorie nutrition plan for Taro, and presents it to the user. In this way, the present invention realizes optimal nutritional management tailored to each individual organism and supports the maintenance of their health.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users log in to the device and input basic health check information, preferences, and dietary history data for their organisms. Specifically, users fill in data such as weight, age, and dietary preferences in an input form, which becomes the input data for the device. This data is converted to JSON format and prepared for secure and safe management.

[0076] Step 2:

[0077] The device sends user-entered information to the server via a secure protocol (e.g., HTTPS). Specifically, the device creates an HTTP POST request, and the data is sent to the specified endpoint on the server. The input is the user's health information, and the output is the completion of data transmission to the server.

[0078] Step 3:

[0079] The server parses the received JSON data and saves the information to a database management system (such as MySQL or MongoDB). During this saving process, the user's pet information is stored in the database. In this process, the server properly organizes the data and prepares it for use in AI models.

[0080] Step 4:

[0081] The server uses an AI model (e.g., TensorFlow or PyTorch) based on stored data to generate an optimal nutrition plan for each individual. Specifically, the AI ​​model takes data as input, calculates nutrient requirements and appropriate foods, and outputs a recommended plan. This output is used in the next step.

[0082] Step 5:

[0083] The server generates a nutrition plan and notifies the terminal, which then presents the plan to the user. The terminal displays the nutrition plan in a visually easy-to-understand format on the user interface, allowing the user to check specific recommended foods and intake amounts. As a result, the output becomes information provided for user review.

[0084] Step 6:

[0085] The user reviews and fine-tunes the presented nutrition plan using their device. Specifically, the user modifies the suggestions in the plan to suit their preferences and then sends that information back to the server as feedback. Based on the feedback, the server updates the data and prepares to reflect it in the next plan generation.

[0086] Step 7:

[0087] The server automatically orders food and arranges delivery based on the finalized nutrition plan. Specifically, it uses the API of partner logistics services to send purchase orders and set delivery schedules. This process ensures that the server secures an optimized logistics route so that ingredients reach the user directly.

[0088] Step 8:

[0089] Users can continuously provide feedback via their device about their pet's responses to the plan and changes in its health. In this feedback process, users input changes in their pet's weight and eating response into their device, and this information is sent to a server. The server receives the feedback and uses it for future analysis by AI models.

[0090] (Application Example 1)

[0091] 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."

[0092] There is a need to provide efficient and accurate individualized nutritional management for pets. Traditional systems require manual management of animal health information and provision of nutritional plans, which is time-consuming and labor-intensive, and may not adequately address individual differences. Furthermore, it is difficult to quickly incorporate current feedback into nutritional plans, making sustainable health maintenance challenging.

[0093] 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.

[0094] In this invention, the server includes communication means for receiving animal health check information and intake history data, analysis means for generating an optimal nutrition plan for the individual, and display and adjustment means for presenting the nutrition plan and allowing the user to confirm and fine-tune it. This enables optimal nutritional management based on the individual health condition of the pet.

[0095] "Communication means" refers to the means of securely transmitting animal health check information and feeding history data to a server.

[0096] "Analysis methods" refer to methods for generating an optimized nutrition plan for each animal using AI models or the like, based on the received information.

[0097] "Display and adjustment means" refers to means that present the generated nutrition plan to the user and allow the user to fine-tune its contents as needed.

[0098] "Management methods" refer to methods for automatically ordering food based on a nutrition plan and arranging delivery through partner logistics organizations.

[0099] A "learning method" is a means of receiving feedback on the animal's responses and changes in its health status, and using that feedback to optimize future nutritional plans.

[0100] "Control means" refers to means of managing the operation of the entire system through an independent electronic device or mobile terminal.

[0101] The system for implementing this invention operates through the interaction of a server, a terminal, and a user. The server plays a key role in analyzing animal health check information and intake history data, and generating an optimal nutrition plan using an AI model.

[0102] The terminal is used by users to input health information about their animals. This information is sent from the terminal to the server via a security protocol. The server stores the received data in a database and generates a personalized nutrition plan for each animal using machine learning libraries such as TensorFlow. This allows users to view the personalized nutrition plan on the terminal and make adjustments as needed.

[0103] Through the management system, food orders are automatically placed based on the nutrition plan, and delivery procedures are carried out through the partnered logistics system. This significantly reduces the effort required from the user.

[0104] Furthermore, users can provide feedback to the server via their devices regarding the animals' reactions and changes in their health. The server collects this feedback and uses it to optimize future nutritional plans, enabling continuous individualized care.

[0105] As a concrete example, when a user inputs information such as the dog's weight, age, and food allergies, the server uses a generated AI model to create a nutrition plan such as "200g of chicken-based dog food" and notifies the terminal. An example of the prompt message is, "Please suggest the optimal nutrition plan based on the dog's health information: weight 20kg, age 5 years, no allergy information."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user enters the animal's health check information and dietary history data on a terminal. This information includes the animal's weight, age, food allergies, and preferred foods. This data is then used for the next step.

[0109] Step 2:

[0110] The terminal sends the input data to the server using a secure protocol. This transmission process ensures that the data reaches the server safely. Here, input is the data from the terminal, and output is the data received by the server.

[0111] Step 3:

[0112] The server stores the received data in a database. During storage, it organizes the information and saves it in a format that allows for efficient searching. The input is the received data, and the output is the organized data.

[0113] Step 4:

[0114] The server uses the stored data to perform analysis using a generative AI model. Specifically, calculations are performed to identify the optimal nutrients and their quantities based on the input data. The input is information from the database, and the output is the generated nutritional plan.

[0115] Step 5:

[0116] The server sends the generated nutrition plan to the terminal. The user can then review the presented nutrition plan via the terminal and make adjustments as needed. The input is the nutrition plan, and the output is the information displayed to the user.

[0117] Step 6:

[0118] Once the user approves their finely tuned nutrition plan, the terminal automatically places an order for food with a partner logistics company via the server. The order data is the input, and the output is the order confirmation information.

[0119] Step 7:

[0120] Users record their pet's reactions and health status on a device and send this information back to the server as feedback. This feedback data is then used as input for generating the next nutrition plan.

[0121] Step 8:

[0122] The server receives feedback and uses it to optimize future nutrition plans. As a result, it provides an improved nutrition plan next time. The input is feedback data, and the output is the improved nutrition plan.

[0123] 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.

[0124] This invention provides a nutritional plan suggestion system that combines a user emotion recognition engine to assist in animal health management. It is implemented by combining a server, a terminal, and the emotion engine.

[0125] First, the user uses a terminal to input information about the animal's health checkup and feeding history. The terminal sends this information to the server, which securely stores the received data in a database.

[0126] Next, the emotion engine installed in the device analyzes the user's voice and facial image to determine the user's emotional state. For example, if the user is feeling stressed, that information is sent to the server.

[0127] The server integrates user emotional information received from the emotion engine with animal health data into an AI analysis module. Based on this integrated data, it generates an optimal nutrition plan for each individual. This plan is adjusted accordingly; for example, if the user is experiencing stress, it will suggest a simpler, quicker meal plan.

[0128] The server then sends the generated nutrition plan to the terminal, which presents it to the user in an appropriate interface. The user reviews the plan, makes any necessary adjustments, and then approves it. The approved plan is sent back to the server, which then handles the automated ordering and delivery management of food.

[0129] The emotion engine also monitors user reactions when reviewing nutrition plans and sends this data to the server, contributing to continuous data accumulation. This feedback information is used when generating future nutrition plans, optimizing the system. For example, if a user expresses satisfaction with a particular meal plan, similar plans will be prioritized in future suggestions.

[0130] This system makes it possible to provide a more personalized experience that takes into account the user's emotions during the process of managing animal health.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users use a terminal to input animal health check information and dietary history data. This input includes age, weight, health status, and information about past dietary preferences and allergies.

[0134] Step 2:

[0135] The terminal transmits the entered data to the server via a secure protocol. This data is fundamental to animal health management and therefore its security is ensured.

[0136] Step 3:

[0137] The emotion engine built into the device analyzes the user's voice and facial expressions in real time to determine their emotional state. In this process, it focuses on recognizing emotions such as stress and anxiety.

[0138] Step 4:

[0139] The terminal sends the determined user's emotional information to the server. The server stores this information in a database and uses it in the next analysis step.

[0140] Step 5:

[0141] The server supplies the received animal health data and user sentiment information to the AI ​​analysis module. The AI ​​combines this information to generate an optimal nutrition plan for each individual animal.

[0142] Step 6:

[0143] The server sends a nutrition plan generated by AI to the device. The device then presents the nutrition plan to the user in an interface that is adjusted based on emotional information. For example, if the user is stressed, the plan is presented in a simpler and easier-to-understand format.

[0144] Step 7:

[0145] Users can review the presented nutrition plan and make adjustments as needed. These adjustments are sent from the device to the server, and the final plan is confirmed.

[0146] Step 8:

[0147] The server automatically orders food and arranges delivery based on the established nutrition plan. This process is handled through partner logistics services, and the user is notified of the delivery schedule.

[0148] Step 9:

[0149] The emotion engine monitors the presentation of the nutrition plan and the user's subsequent reactions, updating the emotion information. This information is sent to the server and used as feedback to generate the next nutrition plan.

[0150] Step 10:

[0151] The server incorporates accumulated feedback data into the AI ​​analysis module, enabling continuous system optimization. This allows for the provision of more accurate and satisfying nutritional plans that reflect user sentiment.

[0152] (Example 2)

[0153] 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".

[0154] In animal health management, it is necessary not only to create standard nutritional plans based on animal physiological data, but also to provide more individualized meal plans that take into account the emotional state of the animal's caretaker. However, current systems have the challenge of not being able to generate flexible plans that reflect both animal data and the caretaker's emotions. Furthermore, continuous optimization of plans based on the caretaker's responses and changes in the animal's health status is also necessary.

[0155] 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.

[0156] In this invention, the server includes communication means for receiving information on the animal's physiological state and feeding history data, emotion analysis means for analyzing the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This makes it possible to provide a personalized nutrition plan that integrates the animal's health data with the caregiver's emotions. Furthermore, it enables continuous optimization of the plan based on feedback.

[0157] "Communication means" refers to a technical device for receiving information on the physiological state of animals and their eating and drinking history data, and transferring it to a server.

[0158] "Analysis methods" refer to technologies used to generate an optimal nutritional plan for an individual based on the received information.

[0159] "Display means" refers to a device or interface for visually presenting the generated nutrition plan to the user.

[0160] "Management measures" refer to technical mechanisms for automating the ordering and delivery procedures of food products based on a plan.

[0161] "Emotional analysis methods" refer to technologies that analyze a user's voice and images to evaluate their emotional state.

[0162] A "learning tool" is a technology that receives feedback based on the user's responses and changes in their health status, and uses this feedback to generate the next nutrition plan.

[0163] A "generative AI model" is an artificial intelligence model that uses machine learning to effectively analyze data and optimize nutritional plans.

[0164] This invention is a system that proposes an individualized nutritional plan for animal health management that takes into account the user's emotions. To implement it, a server, terminal, and emotion analysis function are used in combination.

[0165] The user first uses a terminal to input information about the animal's physiological status and feeding history. This terminal is a commonly used computer or smartphone. The terminal then transmits this information to a server. This server operates using cloud services and securely stores the information using a database.

[0166] The server performs analysis using a generative AI model based on the stored data. The generative AI model integrates the animal's physiological state and feeding history to generate an optimal nutrition plan for each individual. This analysis utilizes data analysis software and machine learning algorithms. After generating the plan, the server sends its contents to the terminal.

[0167] The device is equipped with an emotion analysis function. This emotion analysis uses specialized software to analyze the user's voice and facial images. This software utilizes speech recognition and image processing technologies to evaluate the user's emotions in real time. The results of the emotion analysis are sent to a server and incorporated into the nutrition plan.

[0168] For example, if a pet cat has recently gained weight, and emotional analysis determines that the user is experiencing stress, the server will take that information into consideration and suggest a simpler meal plan.

[0169] A possible example of a prompt message for a specific generative AI model would be something like, "Please suggest a low-calorie meal plan for my cat. The user is stressed."

[0170] This system allows for flexible nutritional planning based on the user's emotional state when managing animal health, providing a highly convenient service for administrators.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user operates the terminal to input information about the animal's physiological state and feeding history. The entered data is collected through a digital form and temporarily stored within the terminal. This data is the foundational information necessary for subsequent analysis and evaluation of emotional state. The terminal packets this data and formats it into a format that the server can receive.

[0174] Step 2:

[0175] The terminal transmits information on the animal's physiological state and feeding history to the server. The server stores the received data in a dedicated database. Here, the data is stored in a structurally optimized format and processed to allow for easy searching and access. This storage process prepares the data for subsequent analysis.

[0176] Step 3:

[0177] The emotion analysis function built into the device acquires the user's voice and facial image as input. This data is analyzed using speech recognition technology and image processing algorithms to determine the user's emotional state. As a result of this process, the user's emotional state (stress, relaxation, etc.) is extracted. The analysis results are then sent from the device to the server.

[0178] Step 4:

[0179] The server inputs data combining animal physiological status information, dietary history, and user emotional state into an AI analysis module. A generative AI model analyzes this data and generates an optimal nutrition plan. This model is optimized through historical data and learning, allowing it to propose flexible plans while considering the user's emotional state. The generated nutrition plan is output as a digital document.

[0180] Step 5:

[0181] The server sends the generated nutrition plan to the terminal. The terminal displays the received plan to the user in a visually easy-to-understand format. The user reviews the displayed plan and makes adjustments as needed. This user interface is designed to be intuitive and easy to use.

[0182] Step 6:

[0183] Once a user approves their nutrition plan, the terminal sends it back to the server. Based on the approved nutrition plan, the server automates the food ordering process and integrates it with the logistics management system. This automation ensures timely food delivery.

[0184] Step 7:

[0185] The device monitors the user's reactions during the nutrition plan usage and acquires new emotional data. This data is sent to the server as feedback and used to generate future nutrition plans. The server continues to learn from this data and improves the overall system performance.

[0186] (Application Example 2)

[0187] 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".

[0188] In animal health management, traditional methods make it difficult to consider the owner's emotions and stress levels, hindering the provision of optimal nutritional plans for animals. Furthermore, the process of determining an appropriate food plan for an animal, ordering it quickly, and having it delivered is complex and time-consuming. Therefore, there is a need for a system that can efficiently and quickly develop nutritional plans and provide necessary supplies while taking the owner's emotional state into account.

[0189] 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.

[0190] In this invention, the server includes communication means for receiving health checkup information and intake history data, data analysis means for detecting the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This enables the generation of an animal nutrition plan that takes the owner's emotions into consideration, and the rapid ordering and delivery of supplies based on that plan.

[0191] "Communication means" refers to the technical means of transmitting animal health check information and feeding history data from a terminal to a server.

[0192] "Analysis tools" refer to technical means for generating an optimal nutrition plan for an individual based on the information received and the user's emotional state.

[0193] The "display means" is an interface for presenting the generated nutrition plan and food ordering and delivery management to the user.

[0194] "Management means" refers to technical means for ordering goods based on a nutrition plan and automating their delivery.

[0195] A "learning tool" is a technical means of receiving feedback on animal responses, changes in health status, and user emotional responses, and optimizing the system to apply this feedback to the next nutritional plan.

[0196] "User's emotional state" refers to the user's mental and emotional state as determined from audio and visual information.

[0197] This invention is a system aimed at proposing an optimal nutritional plan for animal health management, taking into account the emotional state of the user. This system mainly consists of communication means, analysis means, display means, management means, and learning means.

[0198] The server receives animal health check information and feeding history data from the terminal via communication methods. The terminal also uses speech recognition APIs and facial image recognition APIs to understand the user's emotional state. Known technologies such as Google® Cloud Vision API and Amazon Polly can be used for this purpose.

[0199] The analysis system on the server uses a generative AI model to generate an optimal nutrition plan for animals based on the received data. It also integrates user emotion data to propose a plan tailored to the user's emotional state. This process involves data computation using TensorFlow and PyTorch.

[0200] The generated nutrition plan is presented to the user on their device via a display device. The user reviews the plan, makes adjustments as needed, and finally approves it. Once approved, the management device automatically orders and delivers the goods based on the plan. For delivery management, APIs such as Uber Eats' API may be used.

[0201] Furthermore, the learning mechanism receives feedback from the user's emotional responses and changes in the animal's health, and uses this feedback to optimize the next nutrition plan.

[0202] For example, imagine a situation where a pet is unwell and the user is stressed but wants to prepare a meal quickly. In this case, the system can suggest nutritious food that can be prepared in a short time and immediately arrange for ordering and delivery.

[0203] An example of a prompt message could be: "The user is currently experiencing stress, so please suggest a nutritious pet food plan that can be prepared quickly."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The terminal receives animal health check information and feeding history data from the user as input. This data is then transmitted to the server using a communication method. The transmitted data is securely stored in string and numerical format.

[0207] Step 2:

[0208] The device analyzes the user's emotional state using their voice and facial image. This is done using speech recognition APIs (such as Amazon Polly) and facial image recognition APIs (such as Google Cloud Vision API), outputting numerical data that represents the emotional state. This data is also sent to the server.

[0209] Step 3:

[0210] The server integrates animal health and emotional data received via communication methods and processes it using analytical tools. Specifically, this data is input into an AI model (TensorFlow or PyTorch), and an optimal nutrition plan is generated using statistical methods and machine learning algorithms. This plan is output as a new dataset.

[0211] Step 4:

[0212] The server sends the generated nutrition plan to the terminal via a display device, presenting the plan to the user. The user reviews the plan based on this information and makes adjustments if necessary. The adjusted plan is sent back to the server, and the final approved plan is finalized.

[0213] Step 5:

[0214] The server uses management tools to automatically order necessary items based on the confirmed nutrition plan and arrange delivery. This process uses a delivery service API (e.g., Uber Eats API) to output information when delivery is ready.

[0215] Step 6:

[0216] After the plan is executed, the user records feedback on the animal's health and their own emotional state on the device and sends it to the server. The server inputs this feedback into a learning mechanism and processes and stores the data to optimize the system. In this process, it is used as preliminary data for generating the next nutrition plan.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] The system for implementing this invention operates through the cooperation of a server, terminals, and users. The server plays a central role in this system, securely managing animal health check information and intake history data, and generating nutritional plans based on the analysis results.

[0234] First, the user inputs basic health information, preferences, and dietary history data for their animal through a terminal. The terminal then sends the data to the server using a secure protocol. The server stores the received data in a database and performs analysis using an AI model.

[0235] The server uses analytical tools to create a nutrition plan optimized for each individual animal. This plan includes necessary nutrients, suitable foods, and intake amounts. The generated plan is notified to the terminal and presented to the user.

[0236] Users can review the presented nutrition plan on their device and make adjustments to suit their preferences if necessary. The system then automatically orders food based on the nutrition plan and handles delivery via the server. Delivery is handled by partner logistics services, significantly reducing the user's effort.

[0237] Furthermore, users can send feedback from their device to the server regarding their pet's response to a new nutrition plan and changes in its health. The server uses this feedback to incorporate it into subsequent analyses, continuously optimizing the plan. This ensures that animal nutritional management is tailored to individual changes, providing continuous support for their health.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] Users use a terminal to input information about their animals' health checks, preferences, and past feeding history. This information is described in detail based on each input field.

[0241] Step 2:

[0242] The terminal sends the entered data to the server via a secure protocol. The server receives this data and stores it in a secure database.

[0243] Step 3:

[0244] The server passes the stored data to the AI ​​analysis module. The AI ​​applies machine learning algorithms to calculate the optimal nutrition plan for each individual.

[0245] Step 4:

[0246] The server sends the AI-generated nutrition plan to the device. The device then presents the plan to the user via push notification, allowing them to confirm its contents.

[0247] Step 5:

[0248] Users can review the proposed nutrition plan on their device and make adjustments as needed. The approved plan is then sent to the server.

[0249] Step 6:

[0250] The server automatically orders food based on the approved nutrition plan. It issues delivery instructions to partner logistics services and notifies the user of the delivery schedule.

[0251] Step 7:

[0252] The user receives the delivered food and feeds it to their animal. After eating, they observe the animal's reaction and health condition and send feedback from their device to the server.

[0253] Step 8:

[0254] The server incorporates the received feedback into its AI analysis module, which is then used to create the next nutrition plan. This ensures continuous improvement in nutritional management.

[0255] (Example 1)

[0256] 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."

[0257] In modern times, proper nutritional management is essential for maintaining pet health. However, traditional methods have presented challenges, such as difficulty in creating nutritional plans that accommodate individual pet differences, and the time and effort required of owners to prepare meals according to those plans. Furthermore, there is the problem of not being able to respond instantly to changes in a pet's health condition or preferences.

[0258] 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.

[0259] In this invention, the server includes communication means for receiving diagnostic information and intake history data of an organism, analysis means for generating an optimal nutrition plan for the individual, and presentation means for displaying the nutrition plan and allowing the user to confirm it. This enables optimal nutritional management that takes into account the individual differences of pets, reduces the burden on pet owners, and contributes to maintaining the health of pets.

[0260] "Living organism" refers to the animals that are the subject of this invention, and in particular includes dogs, cats, birds, and other animals kept as pets.

[0261] "Diagnostic information" refers to data that indicates the health status of an organism, and includes weight, age, health checkup results, allergy information, etc.

[0262] "Intake history data" refers to records of foods that an organism has consumed in the past, including the amount and frequency of consumption.

[0263] "Communication means" refers to technical means for sending and receiving data from an information processing device to a server, and includes information and communication technologies such as the Internet Protocol.

[0264] "Analysis means" refers to technical means for generating an optimal nutrition plan based on acquired data, and includes functions for performing analysis using artificial intelligence models, etc.

[0265] "Presentation means" refers to an interface that visually provides the generated nutrition plan to the user, and includes screen display and notification functions.

[0266] "Processing means" refers to the function of automatically ordering food based on a nutrition plan and handling delivery procedures through logistics services.

[0267] "Learning methods" refer to technical means that acquire feedback data on the responses and changes in the health status of organisms and use that data to inform future nutritional plans.

[0268] The term "computer system" refers to a system as a whole that includes a set of programs or hardware for integrating and executing these means.

[0269] The embodiment of this invention primarily utilizes a system in which a server, a terminal, and a user work in cooperation. This system incorporates hardware and software for efficiently managing biological diagnostic information and intake history data, and for generating nutritional plans.

[0270] The server plays a central role in this system, handling data management and processing. The server is equipped with a database management system (such as MySQL or MongoDB) for securely storing data. The server also performs data analysis using artificial intelligence models, employing machine learning frameworks such as TensorFlow and PyTorch. This allows the server to generate nutritional plans optimized for living organisms.

[0271] The terminal functions as an interface for users to input data and view results. Specifically, using an application installed on a smartphone or PC, users input pet health information, preferences, and intake history data into the terminal. The terminal then transmits this data to the server via a secure protocol (e.g., HTTPS).

[0272] Users are the primary beneficiaries of the system's convenience. They register their pet's health status and dietary preferences using a terminal and view the generated nutritional plan. Furthermore, they can fine-tune the plan according to their own and their pet's preferences and feed that information back into the system.

[0273] As a concrete example, a user enters a prompt message into the terminal such as, "My cat Taro has gained a little weight recently, so I would like him to eat a low-calorie diet." The server analyzes this information, generates an appropriate low-calorie nutrition plan for Taro, and presents it to the user. In this way, the present invention realizes optimal nutritional management tailored to each individual organism and supports the maintenance of their health.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] Users log in to the device and input basic health check information, preferences, and dietary history data for their organisms. Specifically, users fill in data such as weight, age, and dietary preferences in an input form, which becomes the input data for the device. This data is converted to JSON format and prepared for secure and safe management.

[0277] Step 2:

[0278] The device sends user-entered information to the server via a secure protocol (e.g., HTTPS). Specifically, the device creates an HTTP POST request, and the data is sent to the specified endpoint on the server. The input is the user's health information, and the output is the completion of data transmission to the server.

[0279] Step 3:

[0280] The server analyzes the received JSON-formatted data and stores the information in a database management system (such as MySQL or MongoDB). Through the storage process, the user's pet information is stored in the database. In this process, the server appropriately organizes the data and formats it in a way that is convenient for use in the AI model.

[0281] Step 4:

[0282] Based on the stored data, the server uses an AI model (such as TensorFlow or PyTorch) to generate an optimal nutrition plan for the individual. Specifically, the AI model takes the data as input, calculates the required amounts of nutrients and appropriate food ingredients, and outputs a recommended plan. This output is used for presentation in the next step.

[0283] Step 5:

[0284] The server notifies the terminal of the generated nutrition plan, and the terminal presents the corresponding plan to the user. The terminal displays the nutrition plan in a visually easy-to-view format on the user interface, enabling the user to confirm specific recommended foods and intake amounts. As a result, the output becomes information for the user's review.

[0285] Step 6:

[0286] The user uses the terminal to review and fine-tune the presented nutrition plan. As a specific task, the user modifies the proposals in the plan according to their own wishes and passes the information back to the server as feedback again. Based on the feedback, the server updates the data and prepares to reflect it in the next plan generation.

[0287] Step 7:

[0288] The server automatically orders food and arranges delivery based on the finalized nutrition plan. Specifically, it uses the API of partner logistics services to send purchase orders and set delivery schedules. This process ensures that the server secures an optimized logistics route so that ingredients reach the user directly.

[0289] Step 8:

[0290] Users can continuously provide feedback via their device about their pet's responses to the plan and changes in its health. In this feedback process, users input changes in their pet's weight and eating response into their device, and this information is sent to a server. The server receives the feedback and uses it for future analysis by AI models.

[0291] (Application Example 1)

[0292] 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."

[0293] There is a need to provide efficient and accurate individualized nutritional management for pets. Traditional systems require manual management of animal health information and provision of nutritional plans, which is time-consuming and labor-intensive, and may not adequately address individual differences. Furthermore, it is difficult to quickly incorporate current feedback into nutritional plans, making sustainable health maintenance challenging.

[0294] 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.

[0295] In this invention, the server includes communication means for receiving animal health check information and intake history data, analysis means for generating an optimal nutrition plan for the individual, and display and adjustment means for presenting the nutrition plan and allowing the user to confirm and fine-tune it. This enables optimal nutritional management based on the individual health condition of the pet.

[0296] "Communication means" refers to the means of securely transmitting animal health check information and feeding history data to a server.

[0297] "Analysis methods" refer to methods for generating an optimized nutrition plan for each animal using AI models or the like, based on the received information.

[0298] "Display and adjustment means" refers to means that present the generated nutrition plan to the user and allow the user to fine-tune its contents as needed.

[0299] "Management methods" refer to methods for automatically ordering food based on a nutrition plan and arranging delivery through partner logistics organizations.

[0300] A "learning method" is a means of receiving feedback on the animal's responses and changes in its health status, and using that feedback to optimize future nutritional plans.

[0301] "Control means" refers to means of managing the operation of the entire system through an independent electronic device or mobile terminal.

[0302] The system for implementing this invention operates through the interaction of a server, a terminal, and a user. The server plays a key role in analyzing animal health check information and intake history data, and generating an optimal nutrition plan using an AI model.

[0303] The terminal is used by users to input health information about their animals. This information is sent from the terminal to the server via a security protocol. The server stores the received data in a database and generates a personalized nutrition plan for each animal using machine learning libraries such as TensorFlow. This allows users to view the personalized nutrition plan on the terminal and make adjustments as needed.

[0304] Through management means, food orders based on the nutrition plan are automatically placed, and the delivery procedures are carried out through the partnered logistics system. This can significantly reduce the user's effort.

[0305] Furthermore, the user can feedback to the server the reactions of the animal and changes in its health status through the terminal. The server collects these feedbacks and reflects them in the optimization of the nutrition plan for subsequent times, enabling continuous individual support.

[0306] As a specific example, when the user inputs information such as the weight, age, and food allergies of a dog, the server uses the generated AI model to generate a nutrition plan such as "200g of chicken-based dog food" and notifies the terminal. An example of the prompt text is "Please propose an optimal nutrition plan based on the dog's health information: weight 20 kg, age 5 years, no allergy information."

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user inputs the animal's health diagnosis information and ingestion history data on the terminal. The information to be input includes the animal's weight, age, food allergies, favorite ingredients, etc. This data serves as the input for the next step.

[0310] Step 2:

[0311] The terminal transmits the input data to the server using a secure protocol. Through this transmission process, the data reaches the server safely. The input here is the data from the terminal, and the output is the data reception by the server.

[0312] Step 3:

[0313] The server stores the received data in a database. During storage, it organizes the information and saves it in a format that allows for efficient searching. The input is the received data, and the output is the organized data.

[0314] Step 4:

[0315] The server uses the stored data to perform analysis using a generative AI model. Specifically, calculations are performed to identify the optimal nutrients and their quantities based on the input data. The input is information from the database, and the output is the generated nutritional plan.

[0316] Step 5:

[0317] The server sends the generated nutrition plan to the terminal. The user can then review the presented nutrition plan via the terminal and make adjustments as needed. The input is the nutrition plan, and the output is the information displayed to the user.

[0318] Step 6:

[0319] Once the user approves their finely tuned nutrition plan, the terminal automatically places an order for food with a partner logistics company via the server. The order data is the input, and the output is the order confirmation information.

[0320] Step 7:

[0321] Users record their pet's reactions and health status on a device and send this information back to the server as feedback. This feedback data is then used as input for generating the next nutrition plan.

[0322] Step 8:

[0323] The server receives feedback and uses it to optimize future nutrition plans. As a result, it provides an improved nutrition plan next time. The input is feedback data, and the output is the improved nutrition plan.

[0324] 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.

[0325] This invention provides a nutritional plan suggestion system that combines a user emotion recognition engine to assist in animal health management. It is implemented by combining a server, a terminal, and the emotion engine.

[0326] First, the user uses a terminal to input information about the animal's health checkup and feeding history. The terminal sends this information to the server, which securely stores the received data in a database.

[0327] Next, the emotion engine installed in the device analyzes the user's voice and facial image to determine the user's emotional state. For example, if the user is feeling stressed, that information is sent to the server.

[0328] The server integrates user emotional information received from the emotion engine with animal health data into an AI analysis module. Based on this integrated data, it generates an optimal nutrition plan for each individual. This plan is adjusted accordingly; for example, if the user is experiencing stress, it will suggest a simpler, quicker meal plan.

[0329] The server then sends the generated nutrition plan to the terminal, which presents it to the user in an appropriate interface. The user reviews the plan, makes any necessary adjustments, and then approves it. The approved plan is sent back to the server, which then handles the automated ordering and delivery management of food.

[0330] The emotion engine also monitors user reactions when reviewing nutrition plans and sends this data to the server, contributing to continuous data accumulation. This feedback information is used when generating future nutrition plans, optimizing the system. For example, if a user expresses satisfaction with a particular meal plan, similar plans will be prioritized in future suggestions.

[0331] This system makes it possible to provide a more personalized experience that takes into account the user's emotions during the process of managing animal health.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] Users use a terminal to input animal health check information and dietary history data. This input includes age, weight, health status, and information about past dietary preferences and allergies.

[0335] Step 2:

[0336] The terminal transmits the entered data to the server via a secure protocol. This data is fundamental to animal health management and therefore its security is ensured.

[0337] Step 3:

[0338] The emotion engine built into the device analyzes the user's voice and facial expressions in real time to determine their emotional state. In this process, it focuses on recognizing emotions such as stress and anxiety.

[0339] Step 4:

[0340] The terminal sends the determined user's emotional information to the server. The server stores this information in a database and uses it in the next analysis step.

[0341] Step 5:

[0342] The server supplies the received animal health data and user sentiment information to the AI ​​analysis module. The AI ​​combines this information to generate an optimal nutrition plan for each individual animal.

[0343] Step 6:

[0344] The server sends a nutrition plan generated by AI to the device. The device then presents the nutrition plan to the user in an interface that is adjusted based on emotional information. For example, if the user is stressed, the plan is presented in a simpler and easier-to-understand format.

[0345] Step 7:

[0346] Users can review the presented nutrition plan and make adjustments as needed. These adjustments are sent from the device to the server, and the final plan is confirmed.

[0347] Step 8:

[0348] The server automatically orders food and arranges delivery based on the established nutrition plan. This process is handled through partner logistics services, and the user is notified of the delivery schedule.

[0349] Step 9:

[0350] The emotion engine monitors the presentation of the nutrition plan and the user's subsequent reactions, updating the emotion information. This information is sent to the server and used as feedback to generate the next nutrition plan.

[0351] Step 10:

[0352] The server incorporates accumulated feedback data into the AI ​​analysis module, enabling continuous system optimization. This allows for the provision of more accurate and satisfying nutritional plans that reflect user sentiment.

[0353] (Example 2)

[0354] 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".

[0355] In animal health management, it is necessary not only to create standard nutritional plans based on animal physiological data, but also to provide more individualized meal plans that take into account the emotional state of the animal's caretaker. However, current systems have the challenge of not being able to generate flexible plans that reflect both animal data and the caretaker's emotions. Furthermore, continuous optimization of plans based on the caretaker's responses and changes in the animal's health status is also necessary.

[0356] 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.

[0357] In this invention, the server includes communication means for receiving information on the animal's physiological state and feeding history data, emotion analysis means for analyzing the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This makes it possible to provide a personalized nutrition plan that integrates the animal's health data with the caregiver's emotions. Furthermore, it enables continuous optimization of the plan based on feedback.

[0358] "Communication means" refers to a technical device for receiving information on the physiological state of animals and their eating and drinking history data, and transferring it to a server.

[0359] "Analysis methods" refer to technologies used to generate an optimal nutritional plan for an individual based on the received information.

[0360] "Display means" refers to a device or interface for visually presenting the generated nutrition plan to the user.

[0361] "Management measures" refer to technical mechanisms for automating the ordering and delivery procedures of food products based on a plan.

[0362] "Emotional analysis methods" refer to technologies that analyze a user's voice and images to evaluate their emotional state.

[0363] A "learning tool" is a technology that receives feedback based on the user's responses and changes in their health status, and uses this feedback to generate the next nutrition plan.

[0364] A "generative AI model" is an artificial intelligence model that uses machine learning to effectively analyze data and optimize nutritional plans.

[0365] This invention is a system that proposes an individualized nutritional plan for animal health management that takes into account the user's emotions. To implement it, a server, terminal, and emotion analysis function are used in combination.

[0366] The user first uses a terminal to input information about the animal's physiological status and feeding history. This terminal is a commonly used computer or smartphone. The terminal then transmits this information to a server. This server operates using cloud services and securely stores the information using a database.

[0367] The server performs analysis using a generative AI model based on the stored data. The generative AI model integrates the animal's physiological state and feeding history to generate an optimal nutrition plan for each individual. This analysis utilizes data analysis software and machine learning algorithms. After generating the plan, the server sends its contents to the terminal.

[0368] The device is equipped with an emotion analysis function. This emotion analysis uses specialized software to analyze the user's voice and facial images. This software utilizes speech recognition and image processing technologies to evaluate the user's emotions in real time. The results of the emotion analysis are sent to a server and incorporated into the nutrition plan.

[0369] For example, if a pet cat has recently gained weight, and emotional analysis determines that the user is experiencing stress, the server will take that information into consideration and suggest a simpler meal plan.

[0370] A possible example of a prompt message for a specific generative AI model would be something like, "Please suggest a low-calorie meal plan for my cat. The user is stressed."

[0371] This system allows for flexible nutritional planning based on the user's emotional state when managing animal health, providing a highly convenient service for administrators.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user operates the terminal to input information about the animal's physiological state and feeding history. The entered data is collected through a digital form and temporarily stored within the terminal. This data is the foundational information necessary for subsequent analysis and evaluation of emotional state. The terminal packets this data and formats it into a format that the server can receive.

[0375] Step 2:

[0376] The terminal transmits information on the animal's physiological state and feeding history to the server. The server stores the received data in a dedicated database. Here, the data is stored in a structurally optimized format and processed to allow for easy searching and access. This storage process prepares the data for subsequent analysis.

[0377] Step 3:

[0378] The emotion analysis function built into the device acquires the user's voice and facial image as input. This data is analyzed using speech recognition technology and image processing algorithms to determine the user's emotional state. As a result of this process, the user's emotional state (stress, relaxation, etc.) is extracted. The analysis results are then sent from the device to the server.

[0379] Step 4:

[0380] The server inputs data combining animal physiological status information, dietary history, and user emotional state into an AI analysis module. A generative AI model analyzes this data and generates an optimal nutrition plan. This model is optimized through historical data and learning, allowing it to propose flexible plans while considering the user's emotional state. The generated nutrition plan is output as a digital document.

[0381] Step 5:

[0382] The server sends the generated nutrition plan to the terminal. The terminal displays the received plan to the user in a visually easy-to-understand format. The user reviews the displayed plan and makes adjustments as needed. This user interface is designed to be intuitive and easy to use.

[0383] Step 6:

[0384] Once a user approves their nutrition plan, the terminal sends it back to the server. Based on the approved nutrition plan, the server automates the food ordering process and integrates it with the logistics management system. This automation ensures timely food delivery.

[0385] Step 7:

[0386] The device monitors the user's reactions during the nutrition plan usage and acquires new emotional data. This data is sent to the server as feedback and used to generate future nutrition plans. The server continues to learn from this data and improves the overall system performance.

[0387] (Application Example 2)

[0388] 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."

[0389] In animal health management, traditional methods make it difficult to consider the owner's emotions and stress levels, hindering the provision of optimal nutritional plans for animals. Furthermore, the process of determining an appropriate food plan for an animal, ordering it quickly, and having it delivered is complex and time-consuming. Therefore, there is a need for a system that can efficiently and quickly develop nutritional plans and provide necessary supplies while taking the owner's emotional state into account.

[0390] 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.

[0391] In this invention, the server includes communication means for receiving health checkup information and intake history data, data analysis means for detecting the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This enables the generation of an animal nutrition plan that takes the owner's emotions into consideration, and the rapid ordering and delivery of supplies based on that plan.

[0392] "Communication means" refers to the technical means of transmitting animal health check information and feeding history data from a terminal to a server.

[0393] "Analysis tools" refer to technical means for generating an optimal nutrition plan for an individual based on the information received and the user's emotional state.

[0394] The "display means" is an interface for presenting the generated nutrition plan and food ordering and delivery management to the user.

[0395] "Management means" refers to technical means for ordering goods based on a nutrition plan and automating their delivery.

[0396] A "learning tool" is a technical means of receiving feedback on animal responses, changes in health status, and user emotional responses, and optimizing the system to apply this feedback to the next nutritional plan.

[0397] "User's emotional state" refers to the user's mental and emotional state as determined from audio and visual information.

[0398] This invention is a system aimed at proposing an optimal nutritional plan for animal health management, taking into account the emotional state of the user. This system mainly consists of communication means, analysis means, display means, management means, and learning means.

[0399] The server receives animal health check information and feeding history data from the terminal via communication methods. The terminal also uses speech recognition APIs and facial image recognition APIs to understand the user's emotional state. Known technologies such as Google Cloud Vision API and Amazon Polly can be used for this purpose.

[0400] The analysis system on the server uses a generative AI model to generate an optimal nutrition plan for animals based on the received data. It also integrates user emotion data to propose a plan tailored to the user's emotional state. This process involves data computation using TensorFlow and PyTorch.

[0401] The generated nutrition plan is presented to the user on their device via a display device. The user reviews the plan, makes adjustments as needed, and finally approves it. Once approved, the management device automatically orders and delivers the goods based on the plan. For delivery management, APIs such as Uber Eats' API may be used.

[0402] Furthermore, the learning mechanism receives feedback from the user's emotional responses and changes in the animal's health, and uses this feedback to optimize the next nutrition plan.

[0403] For example, imagine a situation where a pet is unwell and the user is stressed but wants to prepare a meal quickly. In this case, the system can suggest nutritious food that can be prepared in a short time and immediately arrange for ordering and delivery.

[0404] An example of a prompt message could be: "The user is currently experiencing stress, so please suggest a nutritious pet food plan that can be prepared quickly."

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The terminal receives animal health check information and feeding history data from the user as input. This data is then transmitted to the server using a communication method. The transmitted data is securely stored in string and numerical format.

[0408] Step 2:

[0409] The device analyzes the user's emotional state using their voice and facial image. This is done using speech recognition APIs (such as Amazon Polly) and facial image recognition APIs (such as Google Cloud Vision API), outputting numerical data that represents the emotional state. This data is also sent to the server.

[0410] Step 3:

[0411] The server integrates animal health and emotional data received via communication methods and processes it using analytical tools. Specifically, this data is input into an AI model (TensorFlow or PyTorch), and an optimal nutrition plan is generated using statistical methods and machine learning algorithms. This plan is output as a new dataset.

[0412] Step 4:

[0413] The server sends the generated nutrition plan to the terminal via a display device, presenting the plan to the user. The user reviews the plan based on this information and makes adjustments if necessary. The adjusted plan is sent back to the server, and the final approved plan is finalized.

[0414] Step 5:

[0415] The server uses management tools to automatically order necessary items based on the confirmed nutrition plan and arrange delivery. This process uses a delivery service API (e.g., Uber Eats API) to output information when delivery is ready.

[0416] Step 6:

[0417] After the plan is executed, the user records feedback on the animal's health and their own emotional state on the device and sends it to the server. The server inputs this feedback into a learning mechanism and processes and stores the data to optimize the system. In this process, it is used as preliminary data for generating the next nutrition plan.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] The system for implementing this invention operates through the cooperation of a server, terminals, and users. The server plays a central role in this system, securely managing animal health check information and intake history data, and generating nutritional plans based on the analysis results.

[0435] First, the user inputs basic health information, preferences, and dietary history data for their animal through a terminal. The terminal then sends the data to the server using a secure protocol. The server stores the received data in a database and performs analysis using an AI model.

[0436] The server uses analytical tools to create a nutrition plan optimized for each individual animal. This plan includes necessary nutrients, suitable foods, and intake amounts. The generated plan is notified to the terminal and presented to the user.

[0437] Users can review the presented nutrition plan on their device and make adjustments to suit their preferences if necessary. The system then automatically orders food based on the nutrition plan and handles delivery via the server. Delivery is handled by partner logistics services, significantly reducing the user's effort.

[0438] Furthermore, users can send feedback from their device to the server regarding their pet's response to a new nutrition plan and changes in its health. The server uses this feedback to incorporate it into subsequent analyses, continuously optimizing the plan. This ensures that animal nutritional management is tailored to individual changes, providing continuous support for their health.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] Users use a terminal to input information about their animals' health checks, preferences, and past feeding history. This information is described in detail based on each input field.

[0442] Step 2:

[0443] The terminal sends the entered data to the server via a secure protocol. The server receives this data and stores it in a secure database.

[0444] Step 3:

[0445] The server passes the stored data to the AI ​​analysis module. The AI ​​applies machine learning algorithms to calculate the optimal nutrition plan for each individual.

[0446] Step 4:

[0447] The server sends the AI-generated nutrition plan to the device. The device then presents the plan to the user via push notification, allowing them to confirm its contents.

[0448] Step 5:

[0449] Users can review the proposed nutrition plan on their device and make adjustments as needed. The approved plan is then sent to the server.

[0450] Step 6:

[0451] The server automatically orders food based on the approved nutrition plan. It issues delivery instructions to partner logistics services and notifies the user of the delivery schedule.

[0452] Step 7:

[0453] The user receives the delivered food and feeds it to their animal. After eating, they observe the animal's reaction and health condition and send feedback from their device to the server.

[0454] Step 8:

[0455] The server incorporates the received feedback into its AI analysis module, which is then used to create the next nutrition plan. This ensures continuous improvement in nutritional management.

[0456] (Example 1)

[0457] 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."

[0458] In modern times, proper nutritional management is essential for maintaining pet health. However, traditional methods have presented challenges, such as difficulty in creating nutritional plans that accommodate individual pet differences, and the time and effort required of owners to prepare meals according to those plans. Furthermore, there is the problem of not being able to respond instantly to changes in a pet's health condition or preferences.

[0459] 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.

[0460] In this invention, the server includes communication means for receiving diagnostic information and intake history data of an organism, analysis means for generating an optimal nutrition plan for the individual, and presentation means for displaying the nutrition plan and allowing the user to confirm it. This enables optimal nutritional management that takes into account the individual differences of pets, reduces the burden on pet owners, and contributes to maintaining the health of pets.

[0461] "Living organism" refers to the animals that are the subject of this invention, and in particular includes dogs, cats, birds, and other animals kept as pets.

[0462] "Diagnostic information" refers to data that indicates the health status of an organism, and includes weight, age, health checkup results, allergy information, etc.

[0463] "Intake history data" refers to records of foods that an organism has consumed in the past, including the amount and frequency of consumption.

[0464] "Communication means" refers to technical means for sending and receiving data from an information processing device to a server, and includes information and communication technologies such as the Internet Protocol.

[0465] "Analysis means" refers to technical means for generating an optimal nutrition plan based on acquired data, and includes functions for performing analysis using artificial intelligence models, etc.

[0466] "Presentation means" refers to an interface that visually provides the generated nutrition plan to the user, and includes screen display and notification functions.

[0467] "Processing means" refers to the function of automatically ordering food based on a nutrition plan and handling delivery procedures through logistics services.

[0468] "Learning methods" refer to technical means that acquire feedback data on the responses and changes in the health status of organisms and use that data to inform future nutritional plans.

[0469] The term "computer system" refers to a system as a whole that includes a set of programs or hardware for integrating and executing these means.

[0470] The embodiment of this invention primarily utilizes a system in which a server, a terminal, and a user work in cooperation. This system incorporates hardware and software for efficiently managing biological diagnostic information and intake history data, and for generating nutritional plans.

[0471] The server plays a central role in this system, handling data management and processing. The server is equipped with a database management system (such as MySQL or MongoDB) for securely storing data. The server also performs data analysis using artificial intelligence models, employing machine learning frameworks such as TensorFlow and PyTorch. This allows the server to generate nutritional plans optimized for living organisms.

[0472] The terminal functions as an interface for users to input data and view results. Specifically, using an application installed on a smartphone or PC, users input pet health information, preferences, and intake history data into the terminal. The terminal then transmits this data to the server via a secure protocol (e.g., HTTPS).

[0473] Users are the primary beneficiaries of the system's convenience. They register their pet's health status and dietary preferences using a terminal and view the generated nutritional plan. Furthermore, they can fine-tune the plan according to their own and their pet's preferences and feed that information back into the system.

[0474] As a concrete example, a user enters a prompt message into the terminal such as, "My cat Taro has gained a little weight recently, so I would like him to eat a low-calorie diet." The server analyzes this information, generates an appropriate low-calorie nutrition plan for Taro, and presents it to the user. In this way, the present invention realizes optimal nutritional management tailored to each individual organism and supports the maintenance of their health.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] Users log in to the device and input basic health check information, preferences, and dietary history data for their organisms. Specifically, users fill in data such as weight, age, and dietary preferences in an input form, which becomes the input data for the device. This data is converted to JSON format and prepared for secure and safe management.

[0478] Step 2:

[0479] The device sends user-entered information to the server via a secure protocol (e.g., HTTPS). Specifically, the device creates an HTTP POST request, and the data is sent to the specified endpoint on the server. The input is the user's health information, and the output is the completion of data transmission to the server.

[0480] Step 3:

[0481] The server parses the received JSON data and saves the information to a database management system (such as MySQL or MongoDB). During this saving process, the user's pet information is stored in the database. In this process, the server properly organizes the data and prepares it for use in AI models.

[0482] Step 4:

[0483] The server uses an AI model (e.g., TensorFlow or PyTorch) based on stored data to generate an optimal nutrition plan for each individual. Specifically, the AI ​​model takes data as input, calculates nutrient requirements and appropriate foods, and outputs a recommended plan. This output is used in the next step.

[0484] Step 5:

[0485] The server generates a nutrition plan and notifies the terminal, which then presents the plan to the user. The terminal displays the nutrition plan in a visually easy-to-understand format on the user interface, allowing the user to check specific recommended foods and intake amounts. As a result, the output becomes information provided for user review.

[0486] Step 6:

[0487] The user reviews and fine-tunes the presented nutrition plan using their device. Specifically, the user modifies the suggestions in the plan to suit their preferences and then sends that information back to the server as feedback. Based on the feedback, the server updates the data and prepares to reflect it in the next plan generation.

[0488] Step 7:

[0489] The server automatically orders food and arranges delivery based on the finalized nutrition plan. Specifically, it uses the API of partner logistics services to send purchase orders and set delivery schedules. This process ensures that the server secures an optimized logistics route so that ingredients reach the user directly.

[0490] Step 8:

[0491] Users can continuously provide feedback via their device about their pet's responses to the plan and changes in its health. In this feedback process, users input changes in their pet's weight and eating response into their device, and this information is sent to a server. The server receives the feedback and uses it for future analysis by AI models.

[0492] (Application Example 1)

[0493] 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."

[0494] There is a need to provide efficient and accurate individualized nutritional management for pets. Traditional systems require manual management of animal health information and provision of nutritional plans, which is time-consuming and labor-intensive, and may not adequately address individual differences. Furthermore, it is difficult to quickly incorporate current feedback into nutritional plans, making sustainable health maintenance challenging.

[0495] 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.

[0496] In this invention, the server includes communication means for receiving animal health check information and intake history data, analysis means for generating an optimal nutrition plan for the individual, and display and adjustment means for presenting the nutrition plan and allowing the user to confirm and fine-tune it. This enables optimal nutritional management based on the individual health condition of the pet.

[0497] "Communication means" refers to the means of securely transmitting animal health check information and feeding history data to a server.

[0498] "Analysis methods" refer to methods for generating an optimized nutrition plan for each animal using AI models or the like, based on the received information.

[0499] "Display and adjustment means" refers to means that present the generated nutrition plan to the user and allow the user to fine-tune its contents as needed.

[0500] "Management methods" refer to methods for automatically ordering food based on a nutrition plan and arranging delivery through partner logistics organizations.

[0501] A "learning method" is a means of receiving feedback on the animal's responses and changes in its health status, and using that feedback to optimize future nutritional plans.

[0502] "Control means" refers to means of managing the operation of the entire system through an independent electronic device or mobile terminal.

[0503] The system for implementing this invention operates through the interaction of a server, a terminal, and a user. The server plays a key role in analyzing animal health check information and intake history data, and generating an optimal nutrition plan using an AI model.

[0504] The terminal is used by users to input health information about their animals. This information is sent from the terminal to the server via a security protocol. The server stores the received data in a database and generates a personalized nutrition plan for each animal using machine learning libraries such as TensorFlow. This allows users to view the personalized nutrition plan on the terminal and make adjustments as needed.

[0505] Through the management system, food orders are automatically placed based on the nutrition plan, and delivery procedures are carried out through the partnered logistics system. This significantly reduces the effort required from the user.

[0506] Furthermore, users can provide feedback to the server via their devices regarding the animals' reactions and changes in their health. The server collects this feedback and uses it to optimize future nutritional plans, enabling continuous individualized care.

[0507] As a concrete example, when a user inputs information such as the dog's weight, age, and food allergies, the server uses a generated AI model to create a nutrition plan such as "200g of chicken-based dog food" and notifies the terminal. An example of the prompt message is, "Please suggest the optimal nutrition plan based on the dog's health information: weight 20kg, age 5 years, no allergy information."

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The user enters the animal's health check information and dietary history data on a terminal. This information includes the animal's weight, age, food allergies, and preferred foods. This data is then used for the next step.

[0511] Step 2:

[0512] The terminal sends the input data to the server using a secure protocol. This transmission process ensures that the data reaches the server safely. Here, input is the data from the terminal, and output is the data received by the server.

[0513] Step 3:

[0514] The server stores the received data in a database. During storage, it organizes the information and saves it in a format that allows for efficient searching. The input is the received data, and the output is the organized data.

[0515] Step 4:

[0516] The server uses the stored data to perform analysis using a generative AI model. Specifically, calculations are performed to identify the optimal nutrients and their quantities based on the input data. The input is information from the database, and the output is the generated nutritional plan.

[0517] Step 5:

[0518] The server sends the generated nutrition plan to the terminal. The user can then review the presented nutrition plan via the terminal and make adjustments as needed. The input is the nutrition plan, and the output is the information displayed to the user.

[0519] Step 6:

[0520] Once the user approves their finely tuned nutrition plan, the terminal automatically places an order for food with a partner logistics company via the server. The order data is the input, and the output is the order confirmation information.

[0521] Step 7:

[0522] Users record their pet's reactions and health status on a device and send this information back to the server as feedback. This feedback data is then used as input for generating the next nutrition plan.

[0523] Step 8:

[0524] The server receives feedback and uses it to optimize future nutrition plans. As a result, it provides an improved nutrition plan next time. The input is feedback data, and the output is the improved nutrition plan.

[0525] 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.

[0526] This invention provides a nutritional plan suggestion system that combines a user emotion recognition engine to assist in animal health management. It is implemented by combining a server, a terminal, and the emotion engine.

[0527] First, the user uses a terminal to input information about the animal's health checkup and feeding history. The terminal sends this information to the server, which securely stores the received data in a database.

[0528] Next, the emotion engine installed in the device analyzes the user's voice and facial image to determine the user's emotional state. For example, if the user is feeling stressed, that information is sent to the server.

[0529] The server integrates user emotional information received from the emotion engine with animal health data into an AI analysis module. Based on this integrated data, it generates an optimal nutrition plan for each individual. This plan is adjusted accordingly; for example, if the user is experiencing stress, it will suggest a simpler, quicker meal plan.

[0530] The server then sends the generated nutrition plan to the terminal, which presents it to the user in an appropriate interface. The user reviews the plan, makes any necessary adjustments, and then approves it. The approved plan is sent back to the server, which then handles the automated ordering and delivery management of food.

[0531] The emotion engine also monitors user reactions when reviewing nutrition plans and sends this data to the server, contributing to continuous data accumulation. This feedback information is used when generating future nutrition plans, optimizing the system. For example, if a user expresses satisfaction with a particular meal plan, similar plans will be prioritized in future suggestions.

[0532] This system makes it possible to provide a more personalized experience that takes into account the user's emotions during the process of managing animal health.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] Users use a terminal to input animal health check information and dietary history data. This input includes age, weight, health status, and information about past dietary preferences and allergies.

[0536] Step 2:

[0537] The terminal transmits the entered data to the server via a secure protocol. This data is fundamental to animal health management and therefore its security is ensured.

[0538] Step 3:

[0539] The emotion engine built into the device analyzes the user's voice and facial expressions in real time to determine their emotional state. In this process, it focuses on recognizing emotions such as stress and anxiety.

[0540] Step 4:

[0541] The terminal sends the determined user's emotional information to the server. The server stores this information in a database and uses it in the next analysis step.

[0542] Step 5:

[0543] The server supplies the received animal health data and user sentiment information to the AI ​​analysis module. The AI ​​combines this information to generate an optimal nutrition plan for each individual animal.

[0544] Step 6:

[0545] The server sends a nutrition plan generated by AI to the device. The device then presents the nutrition plan to the user in an interface that is adjusted based on emotional information. For example, if the user is stressed, the plan is presented in a simpler and easier-to-understand format.

[0546] Step 7:

[0547] Users can review the presented nutrition plan and make adjustments as needed. These adjustments are sent from the device to the server, and the final plan is confirmed.

[0548] Step 8:

[0549] The server automatically orders food and arranges delivery based on the established nutrition plan. This process is handled through partner logistics services, and the user is notified of the delivery schedule.

[0550] Step 9:

[0551] The emotion engine monitors the presentation of the nutrition plan and the user's subsequent reactions, updating the emotion information. This information is sent to the server and used as feedback to generate the next nutrition plan.

[0552] Step 10:

[0553] The server incorporates accumulated feedback data into the AI ​​analysis module, enabling continuous system optimization. This allows for the provision of more accurate and satisfying nutritional plans that reflect user sentiment.

[0554] (Example 2)

[0555] 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."

[0556] In animal health management, it is necessary not only to create standard nutritional plans based on animal physiological data, but also to provide more individualized meal plans that take into account the emotional state of the animal's caretaker. However, current systems have the challenge of not being able to generate flexible plans that reflect both animal data and the caretaker's emotions. Furthermore, continuous optimization of plans based on the caretaker's responses and changes in the animal's health status is also necessary.

[0557] 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.

[0558] In this invention, the server includes communication means for receiving information on the animal's physiological state and feeding history data, emotion analysis means for analyzing the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This makes it possible to provide a personalized nutrition plan that integrates the animal's health data with the caregiver's emotions. Furthermore, it enables continuous optimization of the plan based on feedback.

[0559] "Communication means" refers to a technical device for receiving information on the physiological state of animals and their eating and drinking history data, and transferring it to a server.

[0560] "Analysis methods" refer to technologies used to generate an optimal nutritional plan for an individual based on the received information.

[0561] "Display means" refers to a device or interface for visually presenting the generated nutrition plan to the user.

[0562] "Management measures" refer to technical mechanisms for automating the ordering and delivery procedures of food products based on a plan.

[0563] "Emotional analysis methods" refer to technologies that analyze a user's voice and images to evaluate their emotional state.

[0564] A "learning tool" is a technology that receives feedback based on the user's responses and changes in their health status, and uses this feedback to generate the next nutrition plan.

[0565] A "generative AI model" is an artificial intelligence model that uses machine learning to effectively analyze data and optimize nutritional plans.

[0566] This invention is a system that proposes an individualized nutritional plan for animal health management that takes into account the user's emotions. To implement it, a server, terminal, and emotion analysis function are used in combination.

[0567] The user first uses a terminal to input information about the animal's physiological status and feeding history. This terminal is a commonly used computer or smartphone. The terminal then transmits this information to a server. This server operates using cloud services and securely stores the information using a database.

[0568] The server performs analysis using a generative AI model based on the stored data. The generative AI model integrates the animal's physiological state and feeding history to generate an optimal nutrition plan for each individual. This analysis utilizes data analysis software and machine learning algorithms. After generating the plan, the server sends its contents to the terminal.

[0569] The device is equipped with an emotion analysis function. This emotion analysis uses specialized software to analyze the user's voice and facial images. This software utilizes speech recognition and image processing technologies to evaluate the user's emotions in real time. The results of the emotion analysis are sent to a server and incorporated into the nutrition plan.

[0570] For example, if a pet cat has recently gained weight, and emotional analysis determines that the user is experiencing stress, the server will take that information into consideration and suggest a simpler meal plan.

[0571] A possible example of a prompt message for a specific generative AI model would be something like, "Please suggest a low-calorie meal plan for my cat. The user is stressed."

[0572] This system allows for flexible nutritional planning based on the user's emotional state when managing animal health, providing a highly convenient service for administrators.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] The user operates the terminal to input information about the animal's physiological state and feeding history. The entered data is collected through a digital form and temporarily stored within the terminal. This data is the foundational information necessary for subsequent analysis and evaluation of emotional state. The terminal packets this data and formats it into a format that the server can receive.

[0576] Step 2:

[0577] The terminal transmits information on the animal's physiological state and feeding history to the server. The server stores the received data in a dedicated database. Here, the data is stored in a structurally optimized format and processed to allow for easy searching and access. This storage process prepares the data for subsequent analysis.

[0578] Step 3:

[0579] The emotion analysis function built into the device acquires the user's voice and facial image as input. This data is analyzed using speech recognition technology and image processing algorithms to determine the user's emotional state. As a result of this process, the user's emotional state (stress, relaxation, etc.) is extracted. The analysis results are then sent from the device to the server.

[0580] Step 4:

[0581] The server inputs data combining animal physiological status information, dietary history, and user emotional state into an AI analysis module. A generative AI model analyzes this data and generates an optimal nutrition plan. This model is optimized through historical data and learning, allowing it to propose flexible plans while considering the user's emotional state. The generated nutrition plan is output as a digital document.

[0582] Step 5:

[0583] The server sends the generated nutrition plan to the terminal. The terminal displays the received plan to the user in a visually easy-to-understand format. The user reviews the displayed plan and makes adjustments as needed. This user interface is designed to be intuitive and easy to use.

[0584] Step 6:

[0585] Once a user approves their nutrition plan, the terminal sends it back to the server. Based on the approved nutrition plan, the server automates the food ordering process and integrates it with the logistics management system. This automation ensures timely food delivery.

[0586] Step 7:

[0587] The device monitors the user's reactions during the nutrition plan usage and acquires new emotional data. This data is sent to the server as feedback and used to generate future nutrition plans. The server continues to learn from this data and improves the overall system performance.

[0588] (Application Example 2)

[0589] 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."

[0590] In animal health management, traditional methods make it difficult to consider the owner's emotions and stress levels, hindering the provision of optimal nutritional plans for animals. Furthermore, the process of determining an appropriate food plan for an animal, ordering it quickly, and having it delivered is complex and time-consuming. Therefore, there is a need for a system that can efficiently and quickly develop nutritional plans and provide necessary supplies while taking the owner's emotional state into account.

[0591] 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.

[0592] In this invention, the server includes communication means for receiving health checkup information and intake history data, data analysis means for detecting the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This enables the generation of an animal nutrition plan that takes the owner's emotions into consideration, and the rapid ordering and delivery of supplies based on that plan.

[0593] "Communication means" refers to the technical means of transmitting animal health check information and feeding history data from a terminal to a server.

[0594] "Analysis tools" refer to technical means for generating an optimal nutrition plan for an individual based on the information received and the user's emotional state.

[0595] The "display means" is an interface for presenting the generated nutrition plan and food ordering and delivery management to the user.

[0596] "Management means" refers to technical means for ordering goods based on a nutrition plan and automating their delivery.

[0597] A "learning tool" is a technical means of receiving feedback on animal responses, changes in health status, and user emotional responses, and optimizing the system to apply this feedback to the next nutritional plan.

[0598] "User's emotional state" refers to the user's mental and emotional state as determined from audio and visual information.

[0599] This invention is a system aimed at proposing an optimal nutritional plan for animal health management, taking into account the emotional state of the user. This system mainly consists of communication means, analysis means, display means, management means, and learning means.

[0600] The server receives animal health check information and feeding history data from the terminal via communication methods. The terminal also uses speech recognition APIs and facial image recognition APIs to understand the user's emotional state. Known technologies such as Google Cloud Vision API and Amazon Polly can be used for this purpose.

[0601] The analysis system on the server uses a generative AI model to generate an optimal nutrition plan for animals based on the received data. It also integrates user emotion data to propose a plan tailored to the user's emotional state. This process involves data computation using TensorFlow and PyTorch.

[0602] The generated nutrition plan is presented to the user on their device via a display device. The user reviews the plan, makes adjustments as needed, and finally approves it. Once approved, the management device automatically orders and delivers the goods based on the plan. For delivery management, APIs such as Uber Eats' API may be used.

[0603] Furthermore, the learning mechanism receives feedback from the user's emotional responses and changes in the animal's health, and uses this feedback to optimize the next nutrition plan.

[0604] For example, imagine a situation where a pet is unwell and the user is stressed but wants to prepare a meal quickly. In this case, the system can suggest nutritious food that can be prepared in a short time and immediately arrange for ordering and delivery.

[0605] An example of a prompt message could be: "The user is currently experiencing stress, so please suggest a nutritious pet food plan that can be prepared quickly."

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The terminal receives animal health check information and feeding history data from the user as input. This data is then transmitted to the server using a communication method. The transmitted data is securely stored in string and numerical format.

[0609] Step 2:

[0610] The device analyzes the user's emotional state using their voice and facial image. This is done using speech recognition APIs (such as Amazon Polly) and facial image recognition APIs (such as Google Cloud Vision API), outputting numerical data that represents the emotional state. This data is also sent to the server.

[0611] Step 3:

[0612] The server integrates animal health and emotional data received via communication methods and processes it using analytical tools. Specifically, this data is input into an AI model (TensorFlow or PyTorch), and an optimal nutrition plan is generated using statistical methods and machine learning algorithms. This plan is output as a new dataset.

[0613] Step 4:

[0614] The server sends the generated nutrition plan to the terminal via a display device, presenting the plan to the user. The user reviews the plan based on this information and makes adjustments if necessary. The adjusted plan is sent back to the server, and the final approved plan is finalized.

[0615] Step 5:

[0616] The server uses management tools to automatically order necessary items based on the confirmed nutrition plan and arrange delivery. This process uses a delivery service API (e.g., Uber Eats API) to output information when delivery is ready.

[0617] Step 6:

[0618] After the plan is executed, the user records feedback on the animal's health and their own emotional state on the device and sends it to the server. The server inputs this feedback into a learning mechanism and processes and stores the data to optimize the system. In this process, it is used as preliminary data for generating the next nutrition plan.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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".

[0636] The system for implementing this invention operates through the cooperation of a server, terminals, and users. The server plays a central role in this system, securely managing animal health check information and intake history data, and generating nutritional plans based on the analysis results.

[0637] First, the user inputs basic health information, preferences, and dietary history data for their animal through a terminal. The terminal then sends the data to the server using a secure protocol. The server stores the received data in a database and performs analysis using an AI model.

[0638] The server uses analytical tools to create a nutrition plan optimized for each individual animal. This plan includes necessary nutrients, suitable foods, and intake amounts. The generated plan is notified to the terminal and presented to the user.

[0639] Users can review the presented nutrition plan on their device and make adjustments to suit their preferences if necessary. The system then automatically orders food based on the nutrition plan and handles delivery via the server. Delivery is handled by partner logistics services, significantly reducing the user's effort.

[0640] Furthermore, users can send feedback from their device to the server regarding their pet's response to a new nutrition plan and changes in its health. The server uses this feedback to incorporate it into subsequent analyses, continuously optimizing the plan. This ensures that animal nutritional management is tailored to individual changes, providing continuous support for their health.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] Users use a terminal to input information about their animals' health checks, preferences, and past feeding history. This information is described in detail based on each input field.

[0644] Step 2:

[0645] The terminal sends the entered data to the server via a secure protocol. The server receives this data and stores it in a secure database.

[0646] Step 3:

[0647] The server passes the stored data to the AI ​​analysis module. The AI ​​applies machine learning algorithms to calculate the optimal nutrition plan for each individual.

[0648] Step 4:

[0649] The server sends the AI-generated nutrition plan to the device. The device then presents the plan to the user via push notification, allowing them to confirm its contents.

[0650] Step 5:

[0651] Users can review the proposed nutrition plan on their device and make adjustments as needed. The approved plan is then sent to the server.

[0652] Step 6:

[0653] The server automatically orders food based on the approved nutrition plan. It issues delivery instructions to partner logistics services and notifies the user of the delivery schedule.

[0654] Step 7:

[0655] The user receives the delivered food and feeds it to their animal. After eating, they observe the animal's reaction and health condition and send feedback from their device to the server.

[0656] Step 8:

[0657] The server incorporates the received feedback into its AI analysis module, which is then used to create the next nutrition plan. This ensures continuous improvement in nutritional management.

[0658] (Example 1)

[0659] 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".

[0660] In modern times, proper nutritional management is essential for maintaining pet health. However, traditional methods have presented challenges, such as difficulty in creating nutritional plans that accommodate individual pet differences, and the time and effort required of owners to prepare meals according to those plans. Furthermore, there is the problem of not being able to respond instantly to changes in a pet's health condition or preferences.

[0661] 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.

[0662] In this invention, the server includes communication means for receiving diagnostic information and intake history data of an organism, analysis means for generating an optimal nutrition plan for the individual, and presentation means for displaying the nutrition plan and allowing the user to confirm it. This enables optimal nutritional management that takes into account the individual differences of pets, reduces the burden on pet owners, and contributes to maintaining the health of pets.

[0663] "Living organism" refers to the animals that are the subject of this invention, and in particular includes dogs, cats, birds, and other animals kept as pets.

[0664] "Diagnostic information" refers to data that indicates the health status of an organism, and includes weight, age, health checkup results, allergy information, etc.

[0665] "Intake history data" refers to records of foods that an organism has consumed in the past, including the amount and frequency of consumption.

[0666] "Communication means" refers to technical means for sending and receiving data from an information processing device to a server, and includes information and communication technologies such as the Internet Protocol.

[0667] "Analysis means" refers to technical means for generating an optimal nutrition plan based on acquired data, and includes functions for performing analysis using artificial intelligence models, etc.

[0668] "Presentation means" refers to an interface that visually provides the generated nutrition plan to the user, and includes screen display and notification functions.

[0669] "Processing means" refers to the function of automatically ordering food based on a nutrition plan and handling delivery procedures through logistics services.

[0670] "Learning methods" refer to technical means that acquire feedback data on the responses and changes in the health status of organisms and use that data to inform future nutritional plans.

[0671] The term "computer system" refers to a system as a whole that includes a set of programs or hardware for integrating and executing these means.

[0672] The embodiment of this invention primarily utilizes a system in which a server, a terminal, and a user work in cooperation. This system incorporates hardware and software for efficiently managing biological diagnostic information and intake history data, and for generating nutritional plans.

[0673] The server plays a central role in this system, handling data management and processing. The server is equipped with a database management system (such as MySQL or MongoDB) for securely storing data. The server also performs data analysis using artificial intelligence models, employing machine learning frameworks such as TensorFlow and PyTorch. This allows the server to generate nutritional plans optimized for living organisms.

[0674] The terminal functions as an interface for users to input data and view results. Specifically, using an application installed on a smartphone or PC, users input pet health information, preferences, and intake history data into the terminal. The terminal then transmits this data to the server via a secure protocol (e.g., HTTPS).

[0675] Users are the primary beneficiaries of the system's convenience. They register their pet's health status and dietary preferences using a terminal and view the generated nutritional plan. Furthermore, they can fine-tune the plan according to their own and their pet's preferences and feed that information back into the system.

[0676] As a concrete example, a user enters a prompt message into the terminal such as, "My cat Taro has gained a little weight recently, so I would like him to eat a low-calorie diet." The server analyzes this information, generates an appropriate low-calorie nutrition plan for Taro, and presents it to the user. In this way, the present invention realizes optimal nutritional management tailored to each individual organism and supports the maintenance of their health.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] Users log in to the device and input basic health check information, preferences, and dietary history data for their organisms. Specifically, users fill in data such as weight, age, and dietary preferences in an input form, which becomes the input data for the device. This data is converted to JSON format and prepared for secure and safe management.

[0680] Step 2:

[0681] The device sends user-entered information to the server via a secure protocol (e.g., HTTPS). Specifically, the device creates an HTTP POST request, and the data is sent to the specified endpoint on the server. The input is the user's health information, and the output is the completion of data transmission to the server.

[0682] Step 3:

[0683] The server parses the received JSON data and saves the information to a database management system (such as MySQL or MongoDB). During this saving process, the user's pet information is stored in the database. In this process, the server properly organizes the data and prepares it for use in AI models.

[0684] Step 4:

[0685] The server uses an AI model (e.g., TensorFlow or PyTorch) based on stored data to generate an optimal nutrition plan for each individual. Specifically, the AI ​​model takes data as input, calculates nutrient requirements and appropriate foods, and outputs a recommended plan. This output is used in the next step.

[0686] Step 5:

[0687] The server generates a nutrition plan and notifies the terminal, which then presents the plan to the user. The terminal displays the nutrition plan in a visually easy-to-understand format on the user interface, allowing the user to check specific recommended foods and intake amounts. As a result, the output becomes information provided for user review.

[0688] Step 6:

[0689] The user reviews and fine-tunes the presented nutrition plan using their device. Specifically, the user modifies the suggestions in the plan to suit their preferences and then sends that information back to the server as feedback. Based on the feedback, the server updates the data and prepares to reflect it in the next plan generation.

[0690] Step 7:

[0691] The server automatically orders food and arranges delivery based on the finalized nutrition plan. Specifically, it uses the API of partner logistics services to send purchase orders and set delivery schedules. This process ensures that the server secures an optimized logistics route so that ingredients reach the user directly.

[0692] Step 8:

[0693] Users can continuously provide feedback via their device about their pet's responses to the plan and changes in its health. In this feedback process, users input changes in their pet's weight and eating response into their device, and this information is sent to a server. The server receives the feedback and uses it for future analysis by AI models.

[0694] (Application Example 1)

[0695] 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".

[0696] There is a need to provide efficient and accurate individualized nutritional management for pets. Traditional systems require manual management of animal health information and provision of nutritional plans, which is time-consuming and labor-intensive, and may not adequately address individual differences. Furthermore, it is difficult to quickly incorporate current feedback into nutritional plans, making sustainable health maintenance challenging.

[0697] 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.

[0698] In this invention, the server includes communication means for receiving animal health check information and intake history data, analysis means for generating an optimal nutrition plan for the individual, and display and adjustment means for presenting the nutrition plan and allowing the user to confirm and fine-tune it. This enables optimal nutritional management based on the individual health condition of the pet.

[0699] "Communication means" refers to the means of securely transmitting animal health check information and feeding history data to a server.

[0700] "Analysis methods" refer to methods for generating an optimized nutrition plan for each animal using AI models or the like, based on the received information.

[0701] "Display and adjustment means" refers to means that present the generated nutrition plan to the user and allow the user to fine-tune its contents as needed.

[0702] "Management methods" refer to methods for automatically ordering food based on a nutrition plan and arranging delivery through partner logistics organizations.

[0703] A "learning method" is a means of receiving feedback on the animal's responses and changes in its health status, and using that feedback to optimize future nutritional plans.

[0704] "Control means" refers to means of managing the operation of the entire system through an independent electronic device or mobile terminal.

[0705] The system for implementing this invention operates through the interaction of a server, a terminal, and a user. The server plays a key role in analyzing animal health check information and intake history data, and generating an optimal nutrition plan using an AI model.

[0706] The terminal is used by users to input health information about their animals. This information is sent from the terminal to the server via a security protocol. The server stores the received data in a database and generates a personalized nutrition plan for each animal using machine learning libraries such as TensorFlow. This allows users to view the personalized nutrition plan on the terminal and make adjustments as needed.

[0707] Through the management system, food orders are automatically placed based on the nutrition plan, and delivery procedures are carried out through the partnered logistics system. This significantly reduces the effort required from the user.

[0708] Furthermore, users can provide feedback to the server via their devices regarding the animals' reactions and changes in their health. The server collects this feedback and uses it to optimize future nutritional plans, enabling continuous individualized care.

[0709] As a concrete example, when a user inputs information such as the dog's weight, age, and food allergies, the server uses a generated AI model to create a nutrition plan such as "200g of chicken-based dog food" and notifies the terminal. An example of the prompt message is, "Please suggest the optimal nutrition plan based on the dog's health information: weight 20kg, age 5 years, no allergy information."

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The user enters the animal's health check information and dietary history data on a terminal. This information includes the animal's weight, age, food allergies, and preferred foods. This data is then used for the next step.

[0713] Step 2:

[0714] The terminal sends the input data to the server using a secure protocol. This transmission process ensures that the data reaches the server safely. Here, input is the data from the terminal, and output is the data received by the server.

[0715] Step 3:

[0716] The server stores the received data in a database. During storage, it organizes the information and saves it in a format that allows for efficient searching. The input is the received data, and the output is the organized data.

[0717] Step 4:

[0718] The server uses the stored data to perform analysis using a generative AI model. Specifically, calculations are performed to identify the optimal nutrients and their quantities based on the input data. The input is information from the database, and the output is the generated nutritional plan.

[0719] Step 5:

[0720] The server sends the generated nutrition plan to the terminal. The user can then review the presented nutrition plan via the terminal and make adjustments as needed. The input is the nutrition plan, and the output is the information displayed to the user.

[0721] Step 6:

[0722] Once the user approves their finely tuned nutrition plan, the terminal automatically places an order for food with a partner logistics company via the server. The order data is the input, and the output is the order confirmation information.

[0723] Step 7:

[0724] Users record their pet's reactions and health status on a device and send this information back to the server as feedback. This feedback data is then used as input for generating the next nutrition plan.

[0725] Step 8:

[0726] The server receives feedback and uses it to optimize future nutrition plans. As a result, it provides an improved nutrition plan next time. The input is feedback data, and the output is the improved nutrition plan.

[0727] 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.

[0728] This invention provides a nutritional plan suggestion system that combines a user emotion recognition engine to assist in animal health management. It is implemented by combining a server, a terminal, and the emotion engine.

[0729] First, the user uses a terminal to input information about the animal's health checkup and feeding history. The terminal sends this information to the server, which securely stores the received data in a database.

[0730] Next, the emotion engine installed in the device analyzes the user's voice and facial image to determine the user's emotional state. For example, if the user is feeling stressed, that information is sent to the server.

[0731] The server integrates user emotional information received from the emotion engine with animal health data into an AI analysis module. Based on this integrated data, it generates an optimal nutrition plan for each individual. This plan is adjusted accordingly; for example, if the user is experiencing stress, it will suggest a simpler, quicker meal plan.

[0732] The server then sends the generated nutrition plan to the terminal, which presents it to the user in an appropriate interface. The user reviews the plan, makes any necessary adjustments, and then approves it. The approved plan is sent back to the server, which then handles the automated ordering and delivery management of food.

[0733] The emotion engine also monitors user reactions when reviewing nutrition plans and sends this data to the server, contributing to continuous data accumulation. This feedback information is used when generating future nutrition plans, optimizing the system. For example, if a user expresses satisfaction with a particular meal plan, similar plans will be prioritized in future suggestions.

[0734] This system makes it possible to provide a more personalized experience that takes into account the user's emotions during the process of managing animal health.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] Users use a terminal to input animal health check information and dietary history data. This input includes age, weight, health status, and information about past dietary preferences and allergies.

[0738] Step 2:

[0739] The terminal transmits the entered data to the server via a secure protocol. This data is fundamental to animal health management and therefore its security is ensured.

[0740] Step 3:

[0741] The emotion engine built into the device analyzes the user's voice and facial expressions in real time to determine their emotional state. In this process, it focuses on recognizing emotions such as stress and anxiety.

[0742] Step 4:

[0743] The terminal sends the determined user's emotional information to the server. The server stores this information in a database and uses it in the next analysis step.

[0744] Step 5:

[0745] The server supplies the received animal health data and user sentiment information to the AI ​​analysis module. The AI ​​combines this information to generate an optimal nutrition plan for each individual animal.

[0746] Step 6:

[0747] The server sends a nutrition plan generated by AI to the device. The device then presents the nutrition plan to the user in an interface that is adjusted based on emotional information. For example, if the user is stressed, the plan is presented in a simpler and easier-to-understand format.

[0748] Step 7:

[0749] Users can review the presented nutrition plan and make adjustments as needed. These adjustments are sent from the device to the server, and the final plan is confirmed.

[0750] Step 8:

[0751] The server automatically orders food and arranges delivery based on the established nutrition plan. This process is handled through partner logistics services, and the user is notified of the delivery schedule.

[0752] Step 9:

[0753] The emotion engine monitors the presentation of the nutrition plan and the user's subsequent reactions, updating the emotion information. This information is sent to the server and used as feedback to generate the next nutrition plan.

[0754] Step 10:

[0755] The server incorporates accumulated feedback data into the AI ​​analysis module, enabling continuous system optimization. This allows for the provision of more accurate and satisfying nutritional plans that reflect user sentiment.

[0756] (Example 2)

[0757] 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".

[0758] In animal health management, it is necessary not only to create standard nutritional plans based on animal physiological data, but also to provide more individualized meal plans that take into account the emotional state of the animal's caretaker. However, current systems have the challenge of not being able to generate flexible plans that reflect both animal data and the caretaker's emotions. Furthermore, continuous optimization of plans based on the caretaker's responses and changes in the animal's health status is also necessary.

[0759] 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.

[0760] In this invention, the server includes communication means for receiving information on the animal's physiological state and feeding history data, emotion analysis means for analyzing the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This makes it possible to provide a personalized nutrition plan that integrates the animal's health data with the caregiver's emotions. Furthermore, it enables continuous optimization of the plan based on feedback.

[0761] "Communication means" refers to a technical device for receiving information on the physiological state of animals and their eating and drinking history data, and transferring it to a server.

[0762] "Analysis methods" refer to technologies used to generate an optimal nutritional plan for an individual based on the received information.

[0763] "Display means" refers to a device or interface for visually presenting the generated nutrition plan to the user.

[0764] "Management measures" refer to technical mechanisms for automating the ordering and delivery procedures of food products based on a plan.

[0765] "Emotional analysis methods" refer to technologies that analyze a user's voice and images to evaluate their emotional state.

[0766] A "learning tool" is a technology that receives feedback based on the user's responses and changes in their health status, and uses this feedback to generate the next nutrition plan.

[0767] A "generative AI model" is an artificial intelligence model that uses machine learning to effectively analyze data and optimize nutritional plans.

[0768] This invention is a system that proposes an individualized nutritional plan for animal health management that takes into account the user's emotions. To implement it, a server, terminal, and emotion analysis function are used in combination.

[0769] The user first uses a terminal to input information about the animal's physiological status and feeding history. This terminal is a commonly used computer or smartphone. The terminal then transmits this information to a server. This server operates using cloud services and securely stores the information using a database.

[0770] The server performs analysis using a generative AI model based on the stored data. The generative AI model integrates the animal's physiological state and feeding history to generate an optimal nutrition plan for each individual. This analysis utilizes data analysis software and machine learning algorithms. After generating the plan, the server sends its contents to the terminal.

[0771] The device is equipped with an emotion analysis function. This emotion analysis uses specialized software to analyze the user's voice and facial images. This software utilizes speech recognition and image processing technologies to evaluate the user's emotions in real time. The results of the emotion analysis are sent to a server and incorporated into the nutrition plan.

[0772] For example, if a pet cat has recently gained weight, and emotional analysis determines that the user is experiencing stress, the server will take that information into consideration and suggest a simpler meal plan.

[0773] A possible example of a prompt message for a specific generative AI model would be something like, "Please suggest a low-calorie meal plan for my cat. The user is stressed."

[0774] This system allows for flexible nutritional planning based on the user's emotional state when managing animal health, providing a highly convenient service for administrators.

[0775] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0776] Step 1:

[0777] The user operates the terminal to input information about the animal's physiological state and feeding history. The entered data is collected through a digital form and temporarily stored within the terminal. This data is the foundational information necessary for subsequent analysis and evaluation of emotional state. The terminal packets this data and formats it into a format that the server can receive.

[0778] Step 2:

[0779] The terminal transmits information on the animal's physiological state and feeding history to the server. The server stores the received data in a dedicated database. Here, the data is stored in a structurally optimized format and processed to allow for easy searching and access. This storage process prepares the data for subsequent analysis.

[0780] Step 3:

[0781] The emotion analysis function built into the device acquires the user's voice and facial image as input. This data is analyzed using speech recognition technology and image processing algorithms to determine the user's emotional state. As a result of this process, the user's emotional state (stress, relaxation, etc.) is extracted. The analysis results are then sent from the device to the server.

[0782] Step 4:

[0783] The server inputs data combining animal physiological status information, dietary history, and user emotional state into an AI analysis module. A generative AI model analyzes this data and generates an optimal nutrition plan. This model is optimized through historical data and learning, allowing it to propose flexible plans while considering the user's emotional state. The generated nutrition plan is output as a digital document.

[0784] Step 5:

[0785] The server sends the generated nutrition plan to the terminal. The terminal displays the received plan to the user in a visually easy-to-understand format. The user reviews the displayed plan and makes adjustments as needed. This user interface is designed to be intuitive and easy to use.

[0786] Step 6:

[0787] Once a user approves their nutrition plan, the terminal sends it back to the server. Based on the approved nutrition plan, the server automates the food ordering process and integrates it with the logistics management system. This automation ensures timely food delivery.

[0788] Step 7:

[0789] The device monitors the user's reactions during the nutrition plan usage and acquires new emotional data. This data is sent to the server as feedback and used to generate future nutrition plans. The server continues to learn from this data and improves the overall system performance.

[0790] (Application Example 2)

[0791] 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".

[0792] In animal health management, traditional methods make it difficult to consider the owner's emotions and stress levels, hindering the provision of optimal nutritional plans for animals. Furthermore, the process of determining an appropriate food plan for an animal, ordering it quickly, and having it delivered is complex and time-consuming. Therefore, there is a need for a system that can efficiently and quickly develop nutritional plans and provide necessary supplies while taking the owner's emotional state into account.

[0793] 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.

[0794] In this invention, the server includes communication means for receiving health checkup information and intake history data, data analysis means for detecting the user's emotional state, and analysis means for generating an optimal nutrition plan for the individual. This enables the generation of an animal nutrition plan that takes the owner's emotions into consideration, and the rapid ordering and delivery of supplies based on that plan.

[0795] "Communication means" refers to the technical means of transmitting animal health check information and feeding history data from a terminal to a server.

[0796] "Analysis tools" refer to technical means for generating an optimal nutrition plan for an individual based on the information received and the user's emotional state.

[0797] The "display means" is an interface for presenting the generated nutrition plan and food ordering and delivery management to the user.

[0798] "Management means" refers to technical means for ordering goods based on a nutrition plan and automating their delivery.

[0799] A "learning tool" is a technical means of receiving feedback on animal responses, changes in health status, and user emotional responses, and optimizing the system to apply this feedback to the next nutritional plan.

[0800] "User's emotional state" refers to the user's mental and emotional state as determined from audio and visual information.

[0801] This invention is a system aimed at proposing an optimal nutritional plan for animal health management, taking into account the emotional state of the user. This system mainly consists of communication means, analysis means, display means, management means, and learning means.

[0802] The server receives animal health check information and feeding history data from the terminal via communication methods. The terminal also uses speech recognition APIs and facial image recognition APIs to understand the user's emotional state. Known technologies such as Google Cloud Vision API and Amazon Polly can be used for this purpose.

[0803] The analysis system on the server uses a generative AI model to generate an optimal nutrition plan for animals based on the received data. It also integrates user emotion data to propose a plan tailored to the user's emotional state. This process involves data computation using TensorFlow and PyTorch.

[0804] The generated nutrition plan is presented to the user on their device via a display device. The user reviews the plan, makes adjustments as needed, and finally approves it. Once approved, the management device automatically orders and delivers the goods based on the plan. For delivery management, APIs such as Uber Eats' API may be used.

[0805] Furthermore, the learning mechanism receives feedback from the user's emotional responses and changes in the animal's health, and uses this feedback to optimize the next nutrition plan.

[0806] For example, imagine a situation where a pet is unwell and the user is stressed but wants to prepare a meal quickly. In this case, the system can suggest nutritious food that can be prepared in a short time and immediately arrange for ordering and delivery.

[0807] An example of a prompt message could be: "The user is currently experiencing stress, so please suggest a nutritious pet food plan that can be prepared quickly."

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The terminal receives animal health check information and feeding history data from the user as input. This data is then transmitted to the server using a communication method. The transmitted data is securely stored in string and numerical format.

[0811] Step 2:

[0812] The device analyzes the user's emotional state using their voice and facial image. This is done using speech recognition APIs (such as Amazon Polly) and facial image recognition APIs (such as Google Cloud Vision API), outputting numerical data that represents the emotional state. This data is also sent to the server.

[0813] Step 3:

[0814] The server integrates animal health and emotional data received via communication methods and processes it using analytical tools. Specifically, this data is input into an AI model (TensorFlow or PyTorch), and an optimal nutrition plan is generated using statistical methods and machine learning algorithms. This plan is output as a new dataset.

[0815] Step 4:

[0816] The server sends the generated nutrition plan to the terminal via a display device, presenting the plan to the user. The user reviews the plan based on this information and makes adjustments if necessary. The adjusted plan is sent back to the server, and the final approved plan is finalized.

[0817] Step 5:

[0818] The server uses management tools to automatically order necessary items based on the confirmed nutrition plan and arrange delivery. This process uses a delivery service API (e.g., Uber Eats API) to output information when delivery is ready.

[0819] Step 6:

[0820] After the plan is executed, the user records feedback on the animal's health and their own emotional state on the device and sends it to the server. The server inputs this feedback into a learning mechanism and processes and stores the data to optimize the system. In this process, it is used as preliminary data for generating the next nutrition plan.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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."

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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 as being incorporated by reference.

[0842] The following is further disclosed regarding the embodiments described above.

[0843] (Claim 1)

[0844] A communication means for receiving animal health check information and intake history data,

[0845] An analytical means that generates an optimal nutrition plan for an individual based on the information received by the aforementioned communication means,

[0846] A display means for presenting the nutrition plan generated by the aforementioned analysis means,

[0847] A management system for ordering food based on the aforementioned nutrition plan and automating its delivery,

[0848] A learning method for receiving feedback on the animal's response and changes in health status and applying it to the next nutrition plan,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein the animal owner inputs animal health check information and intake history data via a terminal, and the terminal transmits them via the communication means.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the analysis means continuously optimizes the nutrition plan using a machine learning model.

[0854] "Example 1"

[0855] (Claim 1)

[0856] A communication means for receiving diagnostic information and intake history data of organisms,

[0857] An analysis means that generates an optimal nutrition plan for an individual based on the information acquired by the aforementioned communication means,

[0858] A presentation means that displays the nutrition plan generated by the analysis means and allows the user to confirm it,

[0859] A processing means for ordering food based on the aforementioned nutrition plan and automating its delivery,

[0860] A learning mechanism that receives feedback on the responses and changes in the health status of the organism and applies it to the next nutrition plan,

[0861] A computer system including [this].

[0862] (Claim 2)

[0863] The computer system according to claim 1, wherein the owner of an organism inputs diagnostic information and intake history data of the organism via an information processing device, and the information processing device transmits them via the communication means.

[0864] (Claim 3)

[0865] The computer system according to claim 1, wherein the analysis means continuously optimizes the nutrition plan using an artificial intelligence model.

[0866] "Application Example 1"

[0867] (Claim 1)

[0868] A communication means for receiving animal health check information and intake history data,

[0869] An analytical means that generates an optimal nutrition plan for an individual based on the information received by the aforementioned communication means,

[0870] A display and adjustment means presents the nutrition plan generated by the aforementioned analysis means and allows the user to review and fine-tune it,

[0871] A management system that orders food based on the aforementioned nutrition plan and automates delivery through a partner logistics organization,

[0872] A learning method for receiving feedback on the animal's response and changes in health status and applying it to the next nutrition plan,

[0873] Control means for operating the system through an independent electronic device or mobile terminal,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, which enables an animal owner to input animal health check information and feeding history data via a mobile terminal, the terminal to transmit them via the communication means, and further to provide feedback information on the mobile terminal.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the analytical means uses a generative AI model to continuously optimize the generated nutrition plan.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] A communication means for receiving information on the physiological state of animals and their eating and drinking history data,

[0882] An analytical means that generates an optimal nutrition plan for an individual based on the information received by the aforementioned communication means,

[0883] A display means for presenting the nutrition plan generated by the aforementioned analysis means,

[0884] A management system for ordering food based on the aforementioned nutrition plan and automating its delivery,

[0885] An emotional analysis means that analyzes the user's emotional state and reflects it in the nutrition plan,

[0886] A learning mechanism that receives feedback on a series of user responses and changes in health status, and applies it to the next nutrition plan,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein an animal manager inputs information on the animal's physiological state and feeding history data via a terminal, and the terminal transmits them via the communication means.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein the analysis means continuously optimizes the nutrition plan using a generative AI model.

[0892] "Application example 2 when combining with an emotional engine"

[0893] (Claim 1)

[0894] A communication means for receiving animal health check information and intake history data,

[0895] An analysis means that generates an optimal nutrition plan for an individual based on information received by the aforementioned communication means and data obtained by a method that detects the user's emotional state,

[0896] A display means that presents the nutrition plan and food ordering and delivery management generated by the aforementioned analysis means,

[0897] A management system for ordering goods based on the aforementioned nutrition plan and automating delivery,

[0898] A learning method that receives feedback on the animal's reactions, changes in health status, and the user's emotional responses, and applies it to the next nutrition plan.

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein the animal owner inputs the animal's health check information and intake history data via a terminal, further detects the user's emotional state obtained from their voice or image, and the terminal transmits them via the communication means.

[0902] (Claim 3)

[0903] The system according to claim 1, wherein the analysis means continuously optimizes the nutrition plan by integrating the user's emotional state and the animal's health data using a machine learning model. [Explanation of Symbols]

[0904] 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 communication means for receiving animal health check information and intake history data, An analytical means that generates an optimal nutrition plan for an individual based on the information received by the aforementioned communication means, A display means for presenting the nutrition plan generated by the aforementioned analysis means, A management system for ordering food based on the aforementioned nutrition plan and automating its delivery, A learning method for receiving feedback on the animal's response and changes in health status and applying it to the next nutrition plan, A system that includes this.

2. The system according to claim 1, wherein the animal owner inputs animal health check information and intake history data via a terminal, and the terminal transmits them via the communication means.

3. The system according to claim 1, wherein the analysis means continuously optimizes the nutrition plan using a machine learning model.

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