Pet health-conscious food recommendation method and computing device and system implementing such method

Abstract

To provide a method of operating an electronic device.SOLUTION: A method of operating an electronic device includes: an operation of acquiring a first dataset indicating a health or disease condition of a pet; an operation of acquiring improvement information of the pet on the basis of the first dataset; an operation of acquiring food information, including nutrient content information, from a food database; an operation of comparing the improvement information with the food information to determine at least one food matching the improvement information; an operation of selecting at least one recommended food on the basis of the at least one food determined; and an operation of providing the at least one recommended food via a user interface. The improvement information includes improvement-function information indicating functions requiring improvement, and improvement-nutrient information indicating nutrients corresponding to the functions requiring improvement.SELECTED DRAWING: Figure 16

Description

[Technical Field]

[0001] The present invention relates to an electronic device that includes an artificial intelligence model, and more particularly to an electronic device that uses an artificial intelligence model to predict the health and disease states of companion animals (pets) and recommend appropriate foods depending on the pet's health and disease. [Background technology]

[0002] As the pet business grows rapidly, the size of the pet healthcare market is also expanding rapidly.

[0003] In particular, many pet illnesses are caused by infectious diseases. Pet illnesses are caused by infection with pathogens (e.g., germs, bacteria, microorganisms, etc.). However, the biggest problem in treating pet illnesses is that pets cannot complain of pain. As a result, pet illnesses are often only treated after a considerable amount of time has passed since the onset of symptoms.

[0004] Recently, the cost of pet care has been increasing rapidly. A major factor is the cost of pet medical care, which accounts for more than half of the total cost of pet care. In this situation, there is a need for technology that can closely monitor the current condition of pets and predict infectious diseases in advance.

[0005] Until now, disease prediction has been done in a very non-quantitative manner, but the purpose of this invention is to utilize data obtained by qualitatively and quantitatively analyzing the genes of pathogens carried by pets to indirectly predict the current health condition and diseases of pets, and then use preventive solutions for those diseases. Summary of the Invention [Problem to be solved by the invention]

[0006] One aspect of the present invention is to analyze PCR test results to determine the disease status of pets.

[0007] Another aspect of the present invention is to accurately assess the health of a pet based on information about the pet.

[0008] A further aspect of the present invention is to recommend suitable foods that reflect the health and disease state of a pet.

[0009] On the other hand, the problems that the present invention aims to solve are not limited to the above-mentioned problems, and problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the inventions included in the present invention belong from this specification and the attached drawings. [Means for solving the problem]

[0010] According to one embodiment of the present invention, a method for operating an electronic device can be provided, comprising the following operations: acquiring quantitative data including a plurality of quantitative values ​​corresponding to each of a plurality of pathogens according to PCR test results by at least one processor included in the electronic device; determining at least one predicted target disease based on the plurality of quantitative values; preprocessing the quantitative data corresponding to the predicted target disease using a first preprocessing algorithm corresponding to the determined predicted target disease; and inputting the preprocessed quantitative data into a first artificial intelligence model corresponding to the determined predicted target disease, and outputting a disease occurrence probability corresponding to the predicted target disease.

[0011] In addition, according to another embodiment of the present invention, a method for operating an electronic device can be provided, which includes the following operations: by at least one processor included in the electronic device, acquiring input data including quantitative data from PCR test results and additional information related to the pet's health or disease; loading a first artificial intelligence model for calculating a health score; and inputting the input data into the first artificial intelligence model to calculate a health score indicating the pet's health condition.

[0012] Furthermore, according to a further embodiment of the present invention, a method for operating an electronic device can be provided, including the following operations: by at least one processor included in the electronic device, acquiring a first dataset indicating the health or disease state of the pet; acquiring improvement information for the pet based on the first dataset (the improvement information includes information on improved functions indicating functions that require improvement, and information on improved nutrients indicating nutrients that correspond to the functions that require improvement); acquiring food information including nutrient content information from a food database; comparing the improvement information with the food information to determine at least one food that matches the improvement information; selecting at least one recommended food based on the determined at least one food; and providing the at least one recommended food through a user interface.

[0013] The solutions to the problems according to various embodiments are not limited to the solutions described above, and solutions not mentioned can be clearly understood by a person having ordinary skill in the art to which the present invention pertains from this specification and the accompanying drawings. [Effects of the Invention]

[0014] An electronic device according to an embodiment of the present invention can analyze PCR test results to analyze a pet's disease status.

[0015] An electronic device according to another embodiment of the present invention can accurately determine the health of a pet based on information about the pet.

[0016] An electronic device according to another embodiment of the present invention can recommend suitable foods that reflect the health and disease state of a pet.

[0017] The effects of the embodiments included in the present invention are not limited to the effects described above, and unmentioned solutions can be clearly understood by a person having ordinary skill in the art to which the present invention pertains from this specification and the accompanying drawings. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 illustrates a pet diagnostic system according to various embodiments. [Figure 2] 1A and 1B are diagrams illustrating the configuration of an electronic device according to various embodiments. [Figure 3] FIG. 1 illustrates an electronic device including an artificial intelligence model trained based on training data related to a pet, according to various embodiments. [Figure 4] FIG. 1 shows an example of PCR quantitative data. [Figure 5] FIG. 1 illustrates how an electronic device uses an artificial intelligence model to obtain output data based on input data, according to various embodiments. [Figure 6] 1 illustrates how an electronic device predicts a pet's likelihood of developing a disease based on PCR data, according to various embodiments. [Figure 7] FIG. 1 shows the correlation between pathogenic microorganisms and diseases. [Figure 8] 1 is a flowchart illustrating an embodiment of an electronic device obtaining a disease score in accordance with various embodiments. [Figure 9] 1 illustrates a specific method by which an electronic device determines a predicted target disease according to various embodiments. [Figure 10] 10 is a flowchart illustrating another embodiment in which an electronic device obtains a disease score, according to various embodiments. [Figure 11] 10 is a flowchart illustrating yet another embodiment in which an electronic device obtains a disease score, according to various embodiments. [Figure 12] FIG. 1 illustrates how an electronic device calculates a health score according to various embodiments. [Figure 13] 1 is a flowchart illustrating a method for an electronic device to obtain a health score according to various embodiments. [Figure 14] FIG. 1 illustrates an embodiment of adjusting weights of an artificial intelligence model based on input data according to various embodiments. [Figure 15] 1 is a flowchart illustrating an embodiment of incrementally training an artificial intelligence model based on input data, according to various embodiments. [Figure 16] 10 is a flowchart illustrating an example of an operation for providing customized food recommendations to a pet, according to various embodiments. [Figure 17] 10A and 10B are diagrams for explaining examples of improvement information and food information according to various embodiments. [Figure 18] 10 is a flowchart illustrating an example of an operation for obtaining improvement information according to various embodiments. [Figure 19] 10 is a flowchart illustrating yet another example of an operation for obtaining remediation information in accordance with various embodiments. [Figure 20] 10 is a flowchart illustrating an example of operations for determining at least one food item according to various embodiments. [Figure 21] 10 is a flowchart illustrating yet another example of operations for determining at least one food item in accordance with various embodiments. [Figure 22] 10 is a flowchart illustrating yet another example of operations for determining at least one food item in accordance with various embodiments. [Figure 23] FIG. 1 is a diagram illustrating an example of a user interface according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0019] The examples described in this specification are intended to clearly explain the concept of the present invention to those skilled in the art to which the present invention pertains, and the present invention is not limited to the examples described in this specification. The scope of the present invention should be construed as including modifications or variations that do not deviate from the concept of the present invention.

[0020] The terms used in this specification are generally used in the widest possible sense, taking into consideration the functions of the present invention. However, these may differ depending on the intentions of those skilled in the art, legal precedents, the emergence of new technology, etc. However, when a specific term is used with a specific definition, the meaning of that term will be described separately. Therefore, the terms used in this specification should be interpreted based on the substantive meaning of the term and the overall content of this specification, rather than simply by the name of the term.

[0021] The drawings attached to this specification are intended to easily explain the present invention, and the shapes shown in the drawings may be exaggerated as necessary to facilitate understanding of the present invention, and therefore the present invention is not limited by the drawings.

[0022] In this specification, each of the statements such as "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" can include any one of the items listed in the statement, or all possible combinations of them.

[0023] In this specification, if it is determined that a specific description of a known configuration or function according to the present invention may obscure the gist of the present invention, the detailed description thereof will be omitted as necessary. Furthermore, numbers (e.g., 1st, 2nd, etc.) used in the description of this specification are merely identification symbols for distinguishing one component from another.

[0024] Furthermore, the suffixes "part" and "section" used in the following description for elements are given or used interchangeably in consideration of making the specification easier to draft, and do not have any distinct meanings or roles.

[0025] The present invention is defined solely by the scope of the claims. The same reference numerals refer to the same elements throughout the specification.

[0026] Terms such as "first" and / or "second" may be used to describe various components, but the components should not be limited by the terms. The terms are used only to distinguish one component from another, for example, a first component can be referred to as a second component, and similarly, a second component can be referred to as a first component, without departing from the scope of the inventive concept.

[0027] When a component is referred to as being "coupled" or "connected" to another component, it should be understood that it may be directly coupled or connected to the other component, but that there may be other components in between. On the other hand, when a component is referred to as being "directly coupled" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationship between components, such as "between" and "immediately between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

[0028] In the figures, each block of the process flowchart and combinations of flowcharts may be implemented by computer program instructions. These computer program instructions may be loaded onto a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that the instructions, executed by the processor of the computer or other programmable data processing device, generate means for performing the functions described in the flowchart blocks. These computer program instructions may also be stored in a computer-usable or computer-readable memory that can direct a computer or other programmable data processing device to implement functions in a particular manner, such that the instructions stored in the computer-usable or computer-readable memory can produce an article of manufacture that includes instruction means for performing the functions described in the flowchart blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable data processing device to create a computer-implemented process, and the instructions executing on the computer or other programmable data processing device provide steps for performing the functions described in the flowchart blocks.

[0029] Furthermore, machine-readable storage media can be provided in the form of non-transitory storage media. Here, "non-transitory" simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and this term does not distinguish between data being stored semi-permanently on the storage medium and data being stored temporarily.

[0030] Furthermore, each block may represent a module, segment, or portion of code that includes one or more executable instructions for performing a particular logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may be performed out of order. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order depending on their corresponding functions. For example, operations performed by a module, program, or other component may be performed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be performed in a different order, omitted, or one or more other operations may be added.

[0031] The term "unit" as used herein refers to a software or hardware component, such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A "unit" performs a specific function, but is not limited to software or hardware. A "unit" may be configured to reside on an addressable storage medium or to implement one or more processors. Thus, in some embodiments, a "unit" includes components such as software components, object-oriented software components, class components, and task class components, as well as processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within components and units may be further separated into components and units that are combined or added into fewer components and units. Moreover, components and units may be implemented to operate one or more CPUs in a device or a security multimedia card. Furthermore, in various embodiments of the present invention, a "unit" may include one or more processors.

[0032] The operating principles of the present invention will be described in detail below with reference to the accompanying drawings. Hereinafter, when describing the present invention, detailed descriptions of related known functions or configurations are omitted if it is determined that such descriptions may unnecessarily obscure the gist of the present invention. Furthermore, the terms used below are defined in consideration of the functions of the present invention, and may vary depending on the intentions or practices of users or operators. Therefore, the definitions should be based on the entire contents of this specification.

[0033] According to one embodiment of the present disclosure, there may be provided a method for operating an electronic device, the method including, by at least one processor included in the electronic device, operations of acquiring a first dataset indicating a health or disease state of a pet, acquiring improvement information for the pet based on the first dataset, acquiring food information including nutrient content information from a food database, comparing the improvement information with the food information to determine at least one food that matches the improvement information, selecting at least one recommended food based on the determined at least one food, and providing the at least one recommended food through a user interface. The improvement information may include improved function information indicating functions that need improvement and improved nutrient information indicating nutrients corresponding to the functions that need improvement.

[0034] The improved nutrient information may include at least one of i) information regarding necessary nutrients and limiting nutrients corresponding to any one of the improved functions, and ii) information regarding improved function information including the first to nth improved functions, the first to nth limiting nutrients, and information regarding overall necessary nutrients and overall limiting nutrients determined based on the first to nth necessary nutrients.

[0035] The operation of acquiring food information may further include an operation of acquiring labeling information indicating the functions to be improved for each of the plurality of foods included in the food database, and the food information may further include labeling information indicating the functions to be improved for each of the plurality of foods.

[0036] The operation of determining a food item may include an operation of selecting one of the improvement functions included in the improvement function information, an operation of obtaining improved nutrient information corresponding to the selected improvement function, an operation of assigning a score to foods containing necessary nutrients included in the improved nutrient information based on priority and content, an operation of excluding foods containing restricted nutrients included in the improved nutrient information, an operation of determining a ranking of the foods based on the assigned score, and an operation of determining at least one food item based on the determined ranking.

[0037] The operation of determining a food item may include an operation of selecting one of the improvement functions included in the improvement function information, an operation of obtaining improved nutrient information corresponding to the selected improvement function, an operation of assigning a score to foods containing necessary nutrients included in the improvement nutrient information based on priority and content, an operation of excluding foods containing restricted nutrients included in the improvement nutrient information, an operation of determining a ranking of the foods based on the assigned score, and an operation of determining at least one food item based on the determined ranking.

[0038] The food selection operation can include the operations of selecting any one of the comprehensive necessary nutrients included in the improved nutrient information, sorting foods containing the selected necessary nutrients, assigning scores to the selected foods based on the content of the necessary nutrients, excluding foods containing a certain amount or more of the comprehensive limiting nutrients included in the improved nutrient information, determining the ranking of the foods based on the assigned scores, and determining at least one food based on the determined ranking.

[0039] Foods that have been assigned a score above a certain value may further include an operation of assigning a score based on predetermined criteria, which may be based on at least one of the food's popularity, the food's calories, pet preferences, or whether or not the food contains an allergenic ingredient.

[0040] Foods that have been given a score above a certain value further include an operation of assigning a score based on predetermined criteria, which may be determined based on at least one of the food's popularity, the food's calories, pet preferences, or whether or not the food contains an allergenic ingredient.

[0041] The operation of determining a food product may include an operation of selecting any one of the improved functions included in the improved function information, and an operation of comparing the selected improved function with the labeling information to determine at least one food product.

[0042] The food providing operation may provide the at least one recommended food by classifying it into recommended food by function or recommended food by nutrient.

[0043] The interface may include at least one of a first interface showing the improvement information, a second interface showing the at least one recommended food item, or a third interface showing the improvement effect of the recommended food item.

[0044] The following describes an electronic device or a system including an electronic device that provides a solution for analyzing pet diseases and health based on diagnostic information about the pet.

[0045] FIG. 1 is a diagram illustrating a pet diagnostic system, according to various embodiments.

[0046] The pet diagnostic system according to the present invention can determine the health status of a companion animal (pet) and recommend food (e.g., pet food) suitable for promoting the pet's health. PCR diagnostic tests can be performed to predict pet diseases. The system can also detect quantitative values ​​for multiple pathogens based on diagnostic kits used for PCR testing. The system can also train an artificial intelligence model based on the multiple quantitative values ​​from the PCR results and clinical data sets for the pet. In this case, the system can train an artificial intelligence model to calculate the probability of disease occurrence based on data related to the pet (e.g., PCR data, age data, etc.).

[0047] The system uses artificial intelligence models to accurately predict the probability of a pet developing a disease, and based on this, can determine the pet's health condition.

[0048] The system can also recommend suitable foods based on the pet's health condition. Specifically, the system can select at least one food suitable for the pet's health condition from a database of pre-stored pet foods and recommend it to the user.

[0049] Referring to FIG. 1, a pet diagnostic system according to the present invention may include an electronic device 100 for analyzing a pet's diagnostic results and a user device 110 that is provided with the pet's diagnostic results.

[0050] The electronic device 100 can receive diagnostic test results for a pet from a user. In this case, the diagnostic test results may refer to a test kit in which a pet sample is collected to perform a PCR test on the pet. The electronic device can analyze the received test results. The electronic device can analyze the received test results in a predetermined manner to determine the pet's health condition. The electronic device can calculate a quantitative value of at least one pathogen contained in the pet sample based on the test results, and can determine the pet's health condition based on the calculated value.

[0051] The electronic device 100 may also provide the determination result to at least one user device 110. The electronic device may provide, but is not limited to, pet health information, disease information, or food recommendation information through the interface of the user device.

[0052] FIG. 2 is a diagram illustrating the configuration of an electronic device according to various embodiments.

[0053] 2, the electronic device may include, but is not limited to, a processor 210, a memory 220, and a communication circuit 230, and may further include other electronic device configurations that would be obvious to one skilled in the art. In this case, the electronic device may include, but is not limited to, a server device.

[0054] The processor 210 may include at least one processor, at least some of which are implemented to provide different functions. For example, the processor 210 may execute software (e.g., a program) to control at least one other component (e.g., a hardware or software component) of the electronic device connected to the processor 210 and perform various data processing or calculations. According to one embodiment, as at least part of the data processing or calculation, the processor 210 may store instructions or data received from other components in the memory 220 (e.g., a volatile memory), process the instructions or data stored in the volatile memory, and store the resulting data in a non-volatile memory. According to one embodiment, the processor 210 may include a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor (e.g., a graphics processing unit, a neural network processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that may operate independently or in conjunction with the main processor. For example, if the electronic device includes a main processor and an auxiliary processor, the auxiliary processor may be configured to use less power than the main processor or to be specialized for designated functions. The auxiliary processor may be implemented separately from or as part of the main processor. The auxiliary processor may, for example, control at least a portion of the functionality or state associated with at least one of the components of the electronic device (e.g., communications circuitry 230) on behalf of the main processor while the main processor is in an inactive (e.g., sleep) state, or together with the main processor while the main processor is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (e.g., image signal processor or communications processor) may be implemented as part of another functionally related component (e.g., communications circuitry 230). In one embodiment, the auxiliary processor (e.g., neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models.The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, within the electronic device on which the artificial intelligence model executes, or through a separate server. The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to these examples. The artificial intelligence model may include multiple artificial neural network layers. The artificial neural network may be one of, but is not limited to, a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more thereof. The artificial intelligence model may additionally or alternatively include a software structure in addition to a hardware structure. Meanwhile, the operations of the electronic device (or computing device) described below can be understood as the operations of the processor 210.

[0055] The processor 210 included in the electronic device 200 according to the present invention can provide multiple functions for pets. Specifically, the processor 210 can perform a disease prediction function for analyzing diagnostic test results of the pet to predict the probability of disease occurrence, a health score calculation function for scoring the pet's health condition based on clinical data, or a food recommendation function for recommending food suitable for the pet's health condition. For example, the processor 210 can include, but is not limited to, a disease prediction unit 211, a health score calculation unit 212, and a food recommendation unit 213.

[0056] In the present invention, the processor is described as being divided into multiple units (e.g., "units") based on their functions, but this is for the convenience of explanation only, and each unit is not necessarily realized by separate hardware. In other words, the multiple units included in the processor may be realized by separate hardware, or may be realized by a single piece of hardware.

[0057] The disease prediction unit 211 can calculate the probability of disease occurrence by analyzing the diagnostic test results of the pet (e.g., quantitative data of pathogens from PCR results) using at least one artificial intelligence model. Details of the method for predicting diseases in pets using the disease prediction unit 211 will be described in Figures 7 to 11.

[0058] The health score calculation unit 212 can calculate an overall health score for the pet by analyzing the disease state and / or health state of the pet based on clinical data using at least one artificial intelligence model. Details of the method for calculating the pet's health score using the health score calculation unit 212 will be described in Figures 12 to 15.

[0059] The food recommendation unit 213 can provide at least one recommended food by determining a food suitable for the pet's health condition using a pre-stored food database. Details of the food recommendation method using the food recommendation unit 213 will be described with reference to Figures 16 to 18.

[0060] The memory 220 can store various data output by at least one component of the electronic device (e.g., the processor 210). The data can include, for example, input data or output data for software (e.g., programs) and associated instructions. The memory 220 can include volatile or non-volatile memory. The memory 220 can be configured to store an operating system, middleware or applications, and / or the artificial intelligence models described above.

[0061] Furthermore, the memory 220 included in the electronic device 200 according to the present invention may include at least one database constructed for a specific purpose. Specifically, the electronic device 200 may include at least one database constructed for predicting a pet's health and / or disease state and recommending an appropriate food. For example, the memory 220 may include, but is not limited to, a clinical DB 221 that stores information about a doctor's diagnosis results regarding the pet's health, a PCR DB 222 that stores information about the pet's PCR test results, a food DB 223 that stores information about a plurality of pet foods, or a health record DB 224 that stores information about the pet's health history (e.g., whether or not there is a past medical history, whether or not there is a disease outbreak, etc.).

[0062] The electronic device 200 may also be configured to store at least one of the databases described above using the memory 220, but is not limited to this, and may also be configured to receive data (e.g., medical data about a pet) that has been pre-stored on an external device (e.g., a hospital server).

[0063] The communications circuitry 230 can support establishing a direct (e.g., wired) or wireless communications channel between the electronic device and an external electronic device (e.g., a user device) and performing communications over the established communications channel. The communications circuitry 230 can include one or more communications processors (e.g., communications chips) that operate independently of the processor 210 (e.g., a program processor) and support the direct (e.g., wired) or wireless communications. In one embodiment, the communications circuitry 230 can include a wireless communications module (e.g., a cellular communications module, a short-range wireless communications module, or a global navigation satellite system (GNSS) communications module) or a wired communications module (e.g., a local area network (LAN) communications module, or a power line communications module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (e.g., a mobile device, a wearable device, or a server device) through a first network (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be realized as multiple separate components (e.g., multiple chips). The wireless communication module can identify or authenticate the electronic device within a communication network, such as the first network or the second network, using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identity module. The wireless communication module can support 5G networks, which are the successors of 4G networks, and next-generation communication technologies, such as new radio access technologies (NR).NR access technologies can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine-type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module can support, for example, high frequency bands (e.g., mmWave bands) to achieve high data transmission rates. The wireless communication module can support various technologies to ensure performance in high frequency bands, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, or large scale antennas. According to one embodiment, the wireless communication module can support a peak data rate (e.g., 20 Gbps or more) for implementing eMBB, loss coverage (e.g., 164 dB or less) for implementing mMTC, or U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for implementing URLLC.

[0064] FIG. 3 is a diagram illustrating an electronic device including an artificial intelligence model trained based on training data related to a pet, according to various embodiments.

[0065] FIG. 4 is a diagram showing an example of PCR quantitative data.

[0066] 3, the electronic device can build a database (DB) for model training based on data related to the pet. Here, the term database (DB) can refer to, but is not limited to, a collection of data built according to a predetermined structure, and can also refer to a collection of data grouped according to a specific purpose (e.g., training an artificial intelligence model).

[0067] For example, the electronic device may include health data related to the pet's health and disease data related to the pet's diseases.

[0068] In the present invention, "health data" may be any grouping of at least one data related to the health condition of a pet. For example, the health data may include, but is not limited to, basic data related to basic information about the pet, such as the pet's age, species, and sex, BCS (Body Condition Score) data related to the pet's obesity state, and questionnaire data related to a questionnaire about the pet's health.

[0069] Furthermore, in the present invention, "disease data" may be any grouping of at least one data related to a pet's disease. For example, the disease data may include, but is not limited to, interview data related to the results of an interview regarding the pet's disease, disease history data related to the pet's past and current medical history, or disease score data related to the pet's probability of developing a disease.

[0070] Furthermore, without being limited thereto, the electronic device may further include additional information related to PCR quantitative data based on PCR test results, health score data based on scoring results for the pet's health condition, medical data related to the pet's medical records, or further information about the pet.

[0071] For example, the PCR quantitative data can be represented by the formatted data shown in Figure 4. Specifically, the PCR quantitative data can include multiple quantitative values ​​indicating the degree of detection of multiple pathogens.

[0072] The electronic device can also train an artificial intelligence model based on a training dataset constructed based on the above-mentioned data.

[0073] In this case, the electronic device can build an artificial intelligence model suitable for predicting pet diseases based on a predetermined formula.

[0074] For example, the electronic device can train multiple artificial intelligence models based on specimens associated with pet diseases, and select one artificial intelligence model based on the disease prediction accuracy of the trained models.

[0075] As a specific example, the electronic device can identify oral bacteria associated with periodontal disease, train five machine learning models (logistic regression, random forest, support vector machine, decision tree, and K-nearest neighbor), and then select the machine learning model that can most accurately predict the progression of periodontal disease. Furthermore, the electronic device can input microbial quantitative data from a newly obtained saliva sample into the selected machine learning model to identify the accuracy, specificity, and sensitivity of predicting the progression of periodontal disease.

[0076] The electronic device can collect information about the pet's health and illnesses, as well as doctors' medical records, to build a clinical database, and can derive information about the pet's health and illnesses by training an artificial intelligence model based on the built clinical database.

[0077] [Pet Disease and Health Prediction] FIG. 5 illustrates how an electronic device may use an artificial intelligence model to obtain output data based on input data, according to various embodiments.

[0078] 5, the electronic device can input input data (Input) to the artificial intelligence model. For example, the input data can include, but is not limited to, PCR data, physical information (e.g., age), medical history / health information, etc. The electronic device can also preprocess the input data based on a preprocessing algorithm and input the preprocessed data to the artificial intelligence model.

[0079] The electronic device can obtain output data by processing input data using an artificial intelligence model. For example, the output data can include, but is not limited to, a disease score indicating the probability of a pet developing a disease or a health score indicating the pet's health condition. The electronic device can also obtain output data using an artificial intelligence model trained based on a pre-stored database (e.g., clinical data). Specifically, the electronic device can obtain an artificial intelligence model with at least one weight set based on multiple pieces of information stored in the database, and can process input data using the artificial intelligence model to obtain output data.

[0080] A weight can be assigned to at least one factor that affects the pet's health or disease, and the electronic device can learn the influence of the at least one factor by learning at least one weight of the artificial intelligence model based on learning data that reflects the at least one factor.

[0081] FIG. 6 illustrates how an electronic device predicts a pet's likelihood of developing a disease based on PCR data, according to various embodiments.

[0082] FIG. 7 shows the correlation between pathogenic microorganisms and diseases.

[0083] 6, the electronic device can obtain quantitative data including multiple quantitative values ​​based on the PCR test results. In this case, the quantitative data can include multiple quantitative values ​​corresponding to the detection levels of multiple pathogens. For example, the quantitative data can include, but is not limited to, a first quantitative value corresponding to the detection level of a first pathogen, a second quantitative value corresponding to the detection level of a second pathogen, and a third quantitative value corresponding to the detection level of a third pathogen.

[0084] The electronic device may also process the quantitative data using an artificial intelligence model to obtain a disease score indicating the probability of the pet developing a disease. Specifically, the electronic device may process the quantitative data based on at least one pre-processing algorithm and at least one disease prediction model to obtain the disease score.

[0085] The electronic device may include a pre-processing algorithm and a disease prediction model corresponding to the disease to be predicted. Specifically, the electronic device may include multiple artificial intelligence models optimized for multiple pet diseases, and may use the artificial intelligence model corresponding to the disease to be predicted.

[0086] For example, the electronic device may include, but is not limited to, a first pre-processing algorithm and a first disease prediction model for predicting a first disease, a second pre-processing algorithm and a second disease prediction model for predicting a second disease, and a third pre-processing algorithm and a third disease prediction model for predicting a third disease, etc.

[0087] The electronic device can input at least one quantitative value corresponding to the predicted target disease based on the PCR quantitative data into the artificial intelligence model.

[0088] Pathogens detected in pets are correlated with at least one disease (or illness). For example, referring to Figure 7, the diseases associated with nine pathogenic microorganisms (Tannerella forsythia, Treponema denticola, Prevotella intermedia, Fusobacterium nucleatum, Prevotella nigrescenes, Camphylobacter rectus, Eubacterium nodatum, Parvimonas micra, and Eikenella) are shown.

[0089] For example, Tannerella forsythia is associated with type 2 diabetes, rheumatoid arthritis, myocardial infarction, atherosclerosis, premature birth, coronary artery disease, chronic obstructive pulmonary disease, chronic kidney disease, and Alzheimer's disease; Treponema denticola is associated with acute myocardial infarction, type 2 diabetes, atherosclerosis, gastric cancer, chronic obstructive pulmonary disease, chronic kidney disease, Alzheimer's disease, and gestational hypertension; Prevotella intermedia is associated with myocardial infarction, premature birth, cardiovascular disease, hypertension, coronary artery disease, acute bronchitis, and Alzheimer's disease; and Fusobacterium nucleatum is associated with type 2 diabetes, atherosclerosis, premature birth, gastric cancer, stillbirth, colon cancer, osteomyelitis, cavernous sinus thrombosis, acute bronchitis, Alzheimer's disease, colon cancer, myocarditis, myopericarditis, and acute encephalopathy. Prevotella nigrescenes is associated with stroke, diabetes, premature birth, and coronary artery disease; Camphylobacter rectus is associated with osteomyelitis, chest empyema, sacroiliitis, sepsis, and coronary artery disease; Eubacterium nodatum is associated with Alzheimer's disease and type 1 diabetes; Parvimonas micra is associated with endocarditis, septic arthritis, and infectious arthritis; and Eikenella corrodens is associated with osteomyelitis, arthritis, liver abscess, endocarditis, parapneumonic pleural effusion, empyema, diabetes, lung infection, abdominal infection, skin infection, skeletal infection, endocarditis, malignancy, coronary artery disease, and pancreatic abscess.

[0090] The electronic device may store a matching table reflecting the correlation of the pathogens to the diseases described above, and may utilize the matching table to identify correlations between the pathogens and the diseases.

[0091] As an example, the electronic device can predict the probability of a pet developing a disease by determining a disease to be predicted based on PCR quantitative data and a matching table.

[0092] FIG. 8 is a flow chart illustrating one embodiment in which an electronic device obtains a disease score, according to various embodiments.

[0093] FIG. 9 illustrates a specific method for an electronic device to determine a predicted target disease, according to various embodiments.

[0094] 8, the electronic device (or at least one processor included in the electronic device) may perform operation S801 to acquire quantitative data including a plurality of quantitative values ​​corresponding to each of a plurality of pathogens according to the PCR test results. In this case, the electronic device may acquire quantitative data including a plurality of quantitative values ​​corresponding to the detection levels of the plurality of pathogens through multiplex PCR analysis of the plurality of pathogens.

[0095] Furthermore, the electronic device may perform an operation of determining at least one predicted target disease based on the plurality of quantitative values ​​by operation S803, where the electronic device may determine the predicted target disease based on a pre-stored matching table that reflects correlations between pathogens and diseases.

[0096] Specifically, referring to FIG. 9, the electronic device can identify at least one quantitative value among the plurality of quantitative values, which has a value equal to or greater than a predetermined standard value, through operation S901. Here, the predetermined standard value may be determined clinically. Specifically, the predetermined standard value may be a reference point set based on an inflammation value (two or more levels above the standard inflammation value) that requires treatment. That is, the electronic device can identify at least one pathogen having an amount at a level that requires treatment and identify at least one quantitative value corresponding to the pathogen.

[0097] Furthermore, the electronic device can identify at least one pathogen corresponding to the identified quantitative value by operation S903. Furthermore, the electronic device can determine at least one predicted target disease corresponding to the identified at least one pathogen based on a pre-stored matching table by step S905. Specifically, the electronic device can determine at least one predicted target disease by identifying pathogens having values ​​equal to or greater than a predetermined standard value and identifying at least one disease corresponding to the identified pathogen.

[0098] In this case, the matching table may further include information about at least one disease that the pathogen affects, and information about the level at which the pathogen affects each pathogen.

[0099] The electronic device can determine the predicted target disease by selecting at least one disease with a high level of impact from the pathogen based on the matching table.

[0100] For example, the electronic device can determine the predicted target disease by selecting at least one disease for which the pathogen has an influence level equal to or greater than a predetermined threshold. Further, for example, the electronic device can determine the predicted target disease by selecting the disease for which the pathogen has the highest influence level.

[0101] Furthermore, when multiple pathogens are identified by operation S903, the electronic device can determine a disease to be predicted based on the frequency of diseases corresponding to the multiple pathogens. Specifically, the electronic device can determine at least one disease commonly corresponding to the multiple pathogens as a disease to be predicted.

[0102] Furthermore, the electronic device can determine the disease to be predicted based on the amount of pathogenic microorganisms determined by the multiple quantitative values ​​and the presence or absence of a disease caused by the pathogenic microorganisms. Specifically, the electronic device can determine the disease to be predicted by setting weights according to the amount of pathogenic microorganisms determined by the multiple quantitative values ​​and the presence or absence of a disease caused by the pathogenic microorganisms.

[0103] 8 again, the electronic device may perform an operation of selecting a preprocessing algorithm and an artificial intelligence model based on the determined disease to be predicted in operation S805. Specifically, the electronic device may select a preprocessing algorithm and an artificial intelligence model suitable for the disease to be predicted from among a plurality of preprocessing algorithms and a plurality of artificial intelligence models. In this case, the selected artificial intelligence model may be a model trained to calculate the occurrence probability of the disease to be predicted.

[0104] In addition, the electronic device may perform an operation of preprocessing the quantitative data corresponding to the disease to be predicted using the selected preprocessing algorithm in step S807. Specifically, the electronic device may preprocess the input data to adjust at least one weight.

[0105] Furthermore, the electronic device can execute operation S809 to input the preprocessed data into the selected artificial intelligence model and output a disease score indicating the disease occurrence probability corresponding to the disease to be predicted. At this time, the electronic device can obtain multiple disease scores corresponding to each of the multiple disease to be predicted for the multiple disease to be predicted.

[0106] The electronic device can improve the accuracy of disease prediction by selecting an optimal artificial intelligence model based on input data and outputting the result value. Furthermore, by performing calculations using the selected artificial intelligence model, the electronic device can reduce calculation costs by not performing unnecessary calculations.

[0107] In another embodiment, the electronic device can predict the probability of a pet developing a disease by analyzing PCR quantitative data based on the determined predicted target disease.

[0108] FIG. 10 is a flow chart illustrating another embodiment in which an electronic device obtains a disease score, according to various embodiments.

[0109] 10, the electronic device can determine the prediction target disease as the first disease by operation S1001. At this time, the electronic device can determine the prediction target disease as the first disease based on a user's input. In addition, the electronic device can determine the prediction target disease as the first disease by inputting input data (e.g., PCR quantification data) into a first pre-processing algorithm corresponding to the first disease.

[0110] Additionally, the electronic device may select at least one pathogen associated with the first disease based on a pre-stored matching table, via operation S1003.

[0111] Additionally, the electronic device may input at least one quantitative value corresponding to at least one pathogen into the first model by operation S1005, where the first model may be trained to calculate a probability of occurrence of the first disease based on the quantitative data.

[0112] Furthermore, the electronic device may, through operation S1007, calculate a first disease score indicating the probability of occurrence of the first disease using at least one layer included in the first model.

[0113] FIG. 11 is a flow chart illustrating yet another embodiment in which an electronic device obtains a disease score, according to various embodiments.

[0114] Referring to FIG. 11, the electronic device can perform an operation to obtain quantitative data according to the PCR test result by operation S1101.

[0115] Furthermore, the electronic device may perform an operation of acquiring additional information (e.g., physical / medical history / health / disease information) related to the pet based on the user's input, in operation S1103. At this time, the additional information may include data related to the pet's health or disease. Specifically, the additional information may include basic information such as the pet's age, sex, and species. The additional information may also include questionnaire data related to the pet's health and medical history data related to the pet's disease. Furthermore, the additional information may include disease history information related to the pet's disease history.

[0116] Furthermore, the electronic device may acquire additional information corresponding to the disease to be predicted. Specifically, the electronic device may store additional information necessary for predicting a specific disease in advance and use the stored information to predict the specific disease. In this case, the additional information may include medical information medically confirmed as necessary for predicting the specific disease.

[0117] For example, the electronic device can use dental images (e.g., dental X-rays) as additional information to predict periodontal disease. Also, for example, the electronic device can use endoscopic examination information, parasite examination information, stool examination information, etc. as additional information to predict digestive diseases. Also, for example, the electronic device can use dementia interview information as additional information to predict dementia.

[0118] Furthermore, the electronic device can execute an operation of calculating a disease score corresponding to at least one disease based on the quantitative data and the additional information by operation S1105. The electronic device can calculate a disease score indicating the probability of disease occurrence based on the disease occurrence risk according to the PCR quantitative data and the disease occurrence risk according to the additional information.

[0119] The electronic device can optimize the AI ​​model using various information related to the disease to be predicted, and can output highly accurate results using an AI model that has learned the influence of quantitative data from PCR results, pet health, and various data related to the disease.

[0120] The electronic device according to the present invention can calculate an overall health score of a pet, reflecting the pet's health status and disease scores for multiple diseases.

[0121] FIG. 12 illustrates how an electronic device calculates a health score, according to various embodiments.

[0122] Referring to FIG. 12, the electronic device may obtain a health score that indicates the overall health status of the pet based on data related to the pet's health or disease.

[0123] Specifically, the electronic device can use a health score calculation model (artificial intelligence model) to calculate a health score based on input data related to the pet.

[0124] In this case, the input data may include PCR quantitative data corresponding to the PCR diagnostic test results. The PCR quantitative data may include multiple quantitative values ​​corresponding to the detection levels of multiple pathogens. A detailed description of the quantitative data has been provided above, so it will be omitted here.

[0125] Additionally, the input data may include health data related to the pet's health or disease data related to the pet's disease.

[0126] For example, the electronic device may obtain the health or disease data based on a questionnaire about the pet's health or the results of a medical interview about the pet, or by receiving information about the pet from a medical database.

[0127] Health or disease data may include, but is not limited to, basic data such as the pet's age, BCS data, disease history data, and the like.

[0128] The electronic device can train a health score calculation model based on a pre-stored clinical database (DB). In this case, the clinical database can store pet health data / disease data and corresponding health scores. Specifically, the electronic device can train an artificial intelligence model to calculate a health score based on the pet's health data and disease data.

[0129] The electronic device can also adjust the weights of the health score calculation unit based on the acquired health or disease data, and calculate the health score by assigning the adjusted weights to the input data (or feature values ​​extracted based on the input data).

[0130] The electronic device can also update the clinical database and further train the health score calculation model based on the updated data set. Specifically, the database can be updated by acquiring information about the pet's health data / disease data and the corresponding health scores, thereby further training the health score calculation model.

[0131] This allows for an optimized artificial intelligence model to be obtained that can calculate a health score that reflects the health of a pet based on pet data in various environments and corresponding medical findings.

[0132] As a specific example, the electronic device can obtain a health score calculation model with adjusted weights based on input data including PCR quantitative data, basic data, BCS data, and disease history data, and obtain a health score through at least one output layer included in the health score calculation model. The electronic device can also re-train the health score calculation model based on the input data and the output health score.

[0133] FIG. 13 is a flowchart illustrating a method for an electronic device to obtain a health score, according to various embodiments.

[0134] 13, the electronic device can perform an operation of acquiring quantitative data based on the PCR test result in operation S1301. Details of operation S1301 have been described above and will not be repeated here.

[0135] In addition, the electronic device may perform an operation of acquiring additional information related to the pet based on the user's input, in operation S1303. In this case, the additional information may include health data related to the pet's health and disease data related to the pet's disease. For example, the electronic device may acquire the pet's age data, BCS data, disease history data, etc.

[0136] Additionally, without limitation, the electronic device may retrieve additional information related to the pet from at least one database stored in the memory.

[0137] Furthermore, the electronic device may execute an operation of calling a first artificial intelligence model for calculating a health score, via operation S1305. In this case, the first artificial intelligence model may be an artificial intelligence model trained to output a health score based on the pet's health / disease data. Specifically, the first artificial intelligence model may include parameters including a plurality of weights corresponding to data included in the training data. The first artificial intelligence model may include at least one weight for extracting at least one feature value corresponding to the input data.

[0138] Additionally, the electronic device may input the input data to a first artificial intelligence model to calculate a health score indicating the health status of the pet, via operation S1307. Specifically, the electronic device may apply at least one weight based on the input data through at least one layer of the artificial intelligence model, and may output a health score through at least one layer of the artificial intelligence model.

[0139] At this time, the electronic device can adjust the weights based on the input data, for example, the weights corresponding to the amount of pathogen determined by PCR quantification data included in the input data, the presence or absence of a disease caused by the pathogen, age, BCS, disease history, etc.

[0140] FIG. 14 illustrates an embodiment of adjusting the weights of an artificial intelligence model based on input data, according to various embodiments.

[0141] 14, the electronic device can obtain a health score indicating the health of the pet by processing input data using an artificial intelligence model 1400. In this case, the health score may be a score evaluated based on a doctor's diagnostic score, with 100 points set as the healthiest standard, but is not limited thereto, and may be at least one index for relatively indicating the health of the pet.

[0142] The electronic device can calculate a first risk level associated with the occurrence of a disease based on the PCR quantitative data. Specifically, the electronic device can calculate the first risk level caused by a pathogenic microorganism by performing a regression analysis on the quantitative values ​​for each pathogen included in the PCR quantitative data. For example, the electronic device can calculate the first risk level caused by a pathogenic microorganism by comparing the quantitative value with a standard amount of the microorganism (a clinically determined value, a reference point for inflammation values ​​requiring treatment), but this is not limiting.

[0143] Furthermore, the electronic device can calculate a second risk level related to the pet's health based on additional information related to the pet's health and disease. Specifically, the electronic device can calculate the second risk level by analyzing information related to the pet's health and disease (e.g., age, BCS, disease history, etc.) and scoring the influence of each piece of additional information. For example, but not limited to, the electronic device can calculate an age-based risk score based on the pet's age, a BCS-based risk score based on the pet's BCS, and a disease history based on the pet's disease history.

[0144] Furthermore, the electronic device may adjust at least one weight of the artificial intelligence model 1400 based on the calculated risk level, and the electronic device may obtain a health score reflecting the health status of the pet by assigning the adjusted weight.

[0145] 13 , the electronic device may store the input data and the health score in a database, per operation S1309. Additionally, the electronic device may retrain the first artificial intelligence model based on the stored data and adjust at least one weight, per operation S1311.

[0146] Through this, the electronic device can learn an artificial intelligence model so that even if the quantitative values ​​of pathogenic microorganisms are similar, the higher the risk based on the pet's health data and disease data, the higher the probability of disease occurrence will be output.

[0147] FIG. 15 is a flow chart illustrating an embodiment of incrementally training an artificial intelligence model based on input data, according to various embodiments.

[0148] 15, the electronic device may perform an operation of acquiring quantitative data corresponding to a PCR test result in operation S1501. The electronic device may also perform an operation of acquiring additional information related to the pet based on a user's input in operation S1503.

[0149] Furthermore, the electronic device can use the artificial intelligence model to calculate a first risk indicating the probability of occurrence of the first disease based on the quantitative data, by operation S1505. Furthermore, the electronic device can use the artificial intelligence model to calculate a second risk indicating the probability of occurrence of the first disease based on the additional information, by operation S1507.

[0150] Furthermore, the electronic device can obtain, by operation S1509, a health score related to the pet's health condition based on the first risk level and the second risk level using the artificial intelligence model. Furthermore, by operation S1511, the electronic device can adjust at least one weight related to the quantitative data and the additional information based on the input values ​​and output values ​​of the artificial intelligence model. That is, the artificial intelligence model included in the electronic device can learn the influence of the obtained data related to the pet's health and disease on the probability of disease occurrence and the associated health score.

[0151] This allows electronic devices to improve the accuracy of their artificial intelligence models as they accumulate databases.

[0152] [Food Recommendations] For the health of pets, it is important to provide them with food that is appropriate for their health or disease state.

[0153] An electronic device according to an embodiment of the present invention may provide a function of recommending foods suitable for improving the health of a pet, taking into account the health and disease state of the pet. In this case, the electronic device may pre-store information about various foods, select at least one food suitable for improving the health of the pet from the pre-stored database, and provide the selected recommended food to the user device.

[0154] The pet food recommendation function of the electronic device will be described in detail below.

[0155] 16 is a flowchart illustrating an example of operations for providing customized food recommendations to a pet according to various embodiments. Operations may be performed out of the order shown and / or described, and more and / or fewer operations may be performed.

[0156] 16, in operation S2101, the electronic device may obtain a first data set based on the pet's health or disease state. For example, the first data set may include the pet's health or disease data described above.

[0157] According to various embodiments, in operation S2103, the electronic device can obtain improvement information of the pet based on the first data set. In the present invention, "improvement information" can be used as a term to mean information about areas (e.g., functions, nutrients) that need improvement for the health of the pet. More details regarding the operation of obtaining improvement information will be described in the description of Figures 17 to 19.

[0158] FIG. 17 is a diagram illustrating an example of improvement information and food information according to various embodiments.

[0159] For example, referring to FIG. 17 , the improvement information may include at least one of information on an improved function and information on an improved nutrient. In the present invention, the term "improved function" may refer to a health function of a pet that needs improvement (e.g., at least a part of a function of the pet's body) and may be replaced with terms such as "function to be improved" or "function requiring improvement" depending on the situation. For example, the information on an improved function may include information on at least one improved function. Examples of functions to be improved include, but are not limited to, oral health, intestinal health, skin and coat health, eye health, diet, bone health, immune health, blood circulation health, and joint health. In the present invention, the term "improved nutrient" refers to a nutrient related to the improvement of a pet's health or disease, and may include nutrients that require intake for functional improvement and nutrients whose intake is restricted for functional improvement. For example, the information on an improved nutrient may include information on at least one improved nutrient. Examples of nutrients include, but are not limited to, crude protein, crude fat, crude fiber, crude ash, calcium, phosphorus, vitamin A, vitamin D, zinc, omega-6, omega-3, metabolic energy, etc. For example, the information on improved nutrients may include information on limiting nutrients and information on necessary nutrients. In the present invention, the term "limiting nutrients" can be used to mean nutrients whose intake is limited for functional improvement. In the present invention, the term "necessary nutrients" can be used to mean nutrients whose intake is necessary for functional improvement. For example, examples of limiting nutrients and necessary nutrients are shown in Table 1 below.

[0160] [Table 1]

[0161] For example, when the function to be improved is the urinary system, vitamin A, vitamin B2, vitamin C, vitamin E, saponin, etc. may be essential nutrients that need to be taken to improve urinary function. However, sodium, phosphate, excess protein, etc. may be limiting nutrients whose intake is restricted to improve urinary function.

[0162] According to various embodiments, the information on the function to be improved may include information on multiple functions to be improved, and the information on the nutrients to be improved may include comprehensive nutrient information based on information on nutrients corresponding to each of the multiple functions to be improved. For example, if the functions to be improved are the urinary and respiratory systems, vitamin C, vitamin E, etc. may be comprehensive essential nutrients that need to be ingested to improve urinary and respiratory functions. However, sodium may be a comprehensive limiting nutrient whose intake is restricted to improve urinary and respiratory functions.

[0163] According to various embodiments, the electronic device may include a food database (DB) that stores information about various foods. Alternatively, according to various embodiments, the electronic device may receive information from an externally constructed food database (DB). For example, the food database may be configured in the form of Table 2 below.

[0164] [Table 2]

[0165] The food database may include food information about multiple foods (first food, second food, etc.). For example, the food information may include nutrient content information for each of the multiple foods. The nutrient content information may include information such as nutritional components, ingredient content, and calories. According to various embodiments, the food information may further include labeling information indicating a function to be improved. According to various embodiments, the food information may further include information such as a brand name that provides the food, a product name that can identify the food, and a product image.

[0166] By storing various food information in the food database, the electronic device can use the pre-stored food information to determine recommended foods that meet the user's needs, which has the effect of reducing the amount of processing power required by the processor. Also, since the database is created for foods that are actually sold, the user can actually purchase and use the recommended foods.

[0167] 18 is a flowchart illustrating an example of an operation S2103 for obtaining remediation information according to various embodiments. Operations may be performed out of the order shown and / or described, and more and / or fewer operations may be performed.

[0168] According to various embodiments, referring to FIG. 18 , in operation S2301, the electronic device can determine a vulnerable function of the pet based on the first data set. In operation S2303, the electronic device can acquire information on an improved function based on the determined vulnerable function. In the present invention, the term "vulnerable function" can be used to refer to a vulnerable health function of the pet (e.g., at least a part of the function of the pet's body). For example, the electronic device can determine one or more vulnerable functions based on the first data set. As a result, the acquired information on the improved function can include information on one or more improved functions. For example, if the information on multiple improved functions is included, the electronic device can determine a priority among the multiple improved functions. Here, the electronic device can determine the priority based on the risk level of the improved function and the probability of disease occurrence. By determining the priority among the improved functions in this manner, a prioritized improved function for the user's pet can be determined, which can provide the user with efficient food recommendations.

[0169] According to various embodiments, in operation S2305, the electronic device can determine limiting nutrients and required nutrients corresponding to any one of the improvement functions based on the information of the improvement function. For example, the limiting nutrients and required nutrients corresponding to any one of the improvement functions can be determined as one or more nutrients.

[0170] According to various embodiments, if the limiting nutrients and / or required nutrients are determined as multiple nutrients, in operation S2307, the electronic device may determine the priority of the multiple nutrients for each of the limiting nutrients and / or required nutrients. For example, if the limiting nutrients corresponding to one of the improvement functions are determined as nutrients A, B, and C, the priority of nutrients A through C may be determined. For example, if the required nutrients corresponding to one of the improvement functions are determined as nutrients D, E, and F, the priority of nutrients D through F may be determined. For example, the electronic device may determine the priority based on the influence each of the multiple nutrients has on the improvement function. For example, a nutrient having a greater influence on the improvement function may have a higher priority. By determining the priority of the multiple nutrients for each of the limiting nutrients and required nutrients in this manner, the improved nutrients prioritized for the user's pet can be determined, which enables the electronic device to provide the user with efficient food recommendations.

[0171] According to various embodiments, in operation S2309, the electronic device can acquire information on improved nutrients based on the determined limiting nutrients and necessary nutrients. According to various embodiments, in operation S2311, the electronic device can acquire improvement information including information on the improved function and information on the improved nutrients. The improvement information can be used to determine the recommended foods when the electronic device, described below, provides recommended foods by function. By acquiring information on improved nutrients based on limiting nutrients and necessary nutrients in this way, it is possible to provide the user's pet with information on nutrients corresponding to a specific improved function, which has the effect of providing recommended foods by function and recommending foods for improving a specific function.

[0172] 19 is a flowchart illustrating a further example of an operation for obtaining remediation information (S2103) according to various embodiments. Operations may be performed out of the order shown and / or described, and more and / or fewer operations may be performed.

[0173] According to various embodiments, referring to FIG. 19 , in operation S2401, the electronic device may determine a vulnerable feature of the pet based on a first data set. In operation S2403, the electronic device may obtain information on a plurality of improved features based on the determined vulnerable feature. For example, the electronic device may determine a plurality of vulnerable features based on the first data set. As a result, the obtained improved feature information may include information on a plurality of improved features. For example, the electronic device may determine a priority among the plurality of improved features. Otherwise, operations S2401 and S2403 may be performed similarly to operations S2301 and S2303 described above, and therefore, redundant description will be omitted.

[0174] According to various embodiments, in operation S2404, the electronic device can obtain information on a first improved nutrient based on a first limited nutrient and a first required nutrient corresponding to the first improvement function, and can obtain information on a second improved nutrient based on a second limited nutrient and a second required nutrient corresponding to the second improvement function. Operation S2404 can be performed in the same manner as S2305 and S2307 above, and therefore a redundant description will be omitted. According to various embodiments, operation S2404 can further obtain information on an nth improved nutrient based on an nth limited nutrient and an nth required nutrient corresponding to an nth improvement function (where n is a natural number greater than or equal to 3).

[0175] According to various embodiments, in operation S2405, the electronic device can determine the overall limiting nutrients and required nutrients based on the information on the improvement function, the information on the first improved nutrient, and the information on the second improved nutrient. In the present invention, the terms "overall limiting nutrients" and "overall required nutrients" do not correspond to only one of the pet's improvement functions, but can be used to refer to the corresponding limiting nutrients and required nutrients taking into account all of the multiple improvement functions included in the improvement function information. For example, the electronic device can determine the overall limiting nutrients and the overall required nutrients based on the priority of the multiple improvement functions. Here, the higher the priority of an improvement function, the more likely it is that the limiting nutrients and required nutrients corresponding to that function will be determined as the overall limiting nutrients and the overall required nutrients. For example, the electronic device can determine the overall limiting nutrients and the overall required nutrients based on the frequency of the limiting nutrients and the required nutrients corresponding to multiple improvement functions. Here, the limiting nutrients and the required nutrients corresponding to multiple improvement functions may be common, but the higher the frequency of correspondence between the limiting nutrients and the required nutrients, the more likely it is that the limiting nutrients and the required nutrients will be determined as the overall limiting nutrients and the overall required nutrients. For example, the total limiting nutrient and the total required nutrient may each be determined as one or more nutrients. According to various embodiments, operation S2405 may determine the total limiting nutrient and the total required nutrient based on the information of the improvement function, information of a first improved nutrient, information of a second improved nutrient, ..., and n improved nutrients (where n is a natural number greater than or equal to 3).

[0176] According to various embodiments, if the limiting nutrients and / or required nutrients are determined as multiple nutrients, in operation S2407, the electronic device may determine the priority among the multiple nutrients for each of the limiting nutrients and required nutrients. For example, if the overall limiting nutrients are determined as nutrients A, B, and C, the priority among nutrients A to C may be determined. For example, if the overall required nutrients are determined as nutrients D, E, and F, the priority among nutrients D to F may be determined. For example, the electronic device may determine the priority based on the influence each of the multiple nutrients has on the improvement function. For example, a nutrient that has a greater influence on the improvement function may have a higher priority. For example, the electronic device may determine the priority based on the frequency with which each of the multiple nutrients corresponds to multiple improvement functions. For example, a nutrient that frequently corresponds to multiple improvement functions may have a higher priority. By determining the priority among the multiple nutrients for each of the overall limiting nutrients and overall required nutrients in this manner, it is possible to determine the improved nutrients that are prioritized for the user's pet, which enables the user to efficiently provide recommended foods.

[0177] According to various embodiments, in operation S2409, the electronic device may acquire information on improved nutrients based on the determined overall limiting nutrients and overall required nutrients. According to various embodiments, in operation S2411, the electronic device may acquire improvement information including information on improved nutrients. The improvement information can be used to determine recommended foods when the electronic device categorizes and provides recommended foods by nutrient, as described below. By acquiring information on improved nutrients based on the overall limiting nutrients and overall required nutrients in this manner, it is possible to provide information on nutrients that need to be improved for the user's pet, taking into account all of the user's pet's various improvement functions, rather than simply focusing on nutrients corresponding to a specific improvement function. This has the effect of providing recommended foods by nutrient, thereby providing recommended foods for more comprehensive pet health rather than specific functional improvements.

[0178] In various embodiments, referring again to FIG. 16 , in operation S2105, the electronic device can obtain labeling information indicating the target function of each of the multiple foods included in the food database. This may require labeling each of the multiple foods included in the food database to indicate the target function. This operation is for matching food information with the improvement function information in operation S2109, which determines foods to be matched, as described below. This has the effect of reducing the amount of calculation, since matching with food information is possible without obtaining information on the improved nutrients corresponding to the improvement function information. Specific details of this operation will be described below with reference to FIG. 20.

[0179] According to various embodiments, in operation S2107, the electronic device may obtain food information from a food database. Here, the food database may include information about foods to be recommended. The food database has been described with reference to FIG. 17 , so a detailed description thereof will be omitted. According to various embodiments, the food database may be input with information from food producers. For example, the food database may be input with information about foods collected directly by an operator of the electronic device of the present invention. For example, the food database may be input with information known as food labels, etc.

[0180] According to various embodiments, in operation S2109, the electronic device can compare the improvement information and the food information to determine at least one food that matches the improvement information. Referring again to Figure 17, the electronic device can compare the pet's improvement information 2220 and the food database 2210 to determine at least one recommended food.

[0181] Specifically, the electronic device can compare the information on the improvement functions in the pet improvement information with the labeled improvement target functions in the food information in the food database to determine at least one recommended food. For example, the electronic device can determine, as a recommended food, a food labeled with an improvement target function corresponding to at least one improvement function included in the improvement function information. For example, the information on the improvement functions may be the information acquired in S2309 described above.

[0182] The electronic device can also compare the improved nutrient information in the pet's improvement information with the contained nutrient information in the food information in the food database to determine at least one recommended food. For example, the electronic device can determine, as a recommended food, a food that contains at least one nutrient included in the required nutrient information in the improved nutrient information from among multiple foods in the food database. In various embodiments, by comparing the contained nutrient information, the electronic device can recommend an optimal food taking into account the restricted nutrients and required nutrients in the pet's improvement information. For example, the improved nutrient information can be the information acquired in S2309 described above. For example, the improved nutrient information can be the information acquired in S2409 described above.

[0183] A specific embodiment for determining recommended foods based on function and nutrients will be further described below through the description of FIGS. 20 to 22.

[0184] 20 is a flow chart illustrating an example of an operation S2109 of determining at least one food item, according to various embodiments. Operations may be performed out of the order shown and / or described, and more and / or fewer operations may be performed.

[0185] In various embodiments, in operation S2501, the electronic device may obtain labeling information for a plurality of foods included in the food database, the labeling information indicating the function to be improved for each of the plurality of foods. Operation S2501 may be performed in the same manner as operation S2105 described above, and therefore a redundant description will be omitted.

[0186] According to various embodiments, in operation S2503, the electronic device may select one of the improvement functions included in the improvement function information. According to various embodiments, in operation S2505, the electronic device may compare the selected improvement function with labeling information to determine at least one food. For example, in operation S2501, the electronic device may obtain labeling information in which the function to be improved is "eye function" for foods containing carrots in the food database. Thereafter, if the electronic device selects "eye function" as one of the functions that needs to be improved according to the improvement function information in operation S2503, the improvement function information and food information may be matched in operation S2505, and a food containing carrots may be determined as a recommended food. According to various embodiments, in operation S2113 described below, the electronic device may classify the recommended foods into recommended foods by function and provide them.

[0187] 21 is a flow chart illustrating another example of an operation S2109 for determining at least one food item, according to various embodiments. Operations may be performed out of the order shown and / or described, and more and / or fewer operations may be performed.

[0188] According to various embodiments, in operation S2601, the electronic device may select one of the improvement functions included in the information on the improvement functions. Since operation S2601 may be performed in the same manner as operation S2503 described above, a redundant description will be omitted.

[0189] According to various embodiments, in operation S2603, the electronic device can acquire information on improved nutrients corresponding to the selected improvement function. For example, the electronic device can perform operations similar to those of operations S2305 and S2309 described above. Specifically, the electronic device can determine limiting nutrients and required nutrients corresponding to any one of the selected improvement functions. Then, information on improved nutrients can be acquired based on the determined limiting nutrients and required nutrients.

[0190] According to various embodiments, in operation S2605, the electronic device may assign scores to foods containing the necessary nutrients included in the improved nutrient information based on the priority and content of the necessary nutrients. Here, the priority of the necessary nutrients may be determined in operation S2307 described above. For example, if any one necessary nutrient has a high priority determined in operation S2307 described above, the electronic device may assign a high score to the food containing the necessary nutrient. For example, if any one food has a high content of the necessary nutrient, the electronic device may assign a high score to the food.

[0191] According to various embodiments, in operation S2607, the electronic device can exclude foods that contain a certain amount or more of the limiting nutrients included in the information on the improving nutrients. For example, the certain amount can be set based on an amount that is limited in intake with respect to the improving function. For example, the certain amount can represent an effective amount that adversely affects the improving function.

[0192] According to various embodiments, in operation S2609, the electronic device may assign a score to foods that have been assigned a score above a certain value based on predetermined criteria. For example, foods that have been assigned a score above a certain value may be foods that effectively have a positive impact on the improvement function due to the necessary nutrients contained in the foods. For example, the predetermined criteria may be determined based on the popularity of the food, the calories of the food, the pet's preferences (e.g., texture, dosage form, size, etc.), whether or not the food contains an allergen, etc. For example, if a food is highly popular, the electronic device may assign a high score to the food. For example, the electronic device may assign a score to the food based on the calories of the food and the pet's improvement information. For example, if the pet's improvement information is related to obesity, the electronic device may assign a high score to a low-calorie food. For example, if a food matches the pet's preferences (e.g., texture, dosage form, size, etc.), the electronic device may assign a high score to the food. For example, if a food contains an allergen that the pet is allergic to, the electronic device may exclude the food. This has the effect of providing recommended foods that meet the user's needs by providing recommended foods based on not only information on improved functions or improved nutrients but also various other criteria.

[0193] According to various embodiments, in operation S2611, the electronic device may determine a ranking of the foods based on the assigned scores. According to various embodiments, in operation S2613, the electronic device may determine at least one food as a recommended food based on the determined ranking. According to various embodiments, in operation S2113 described below, the electronic device may provide the recommended foods by categorizing them into recommended foods by function.

[0194] 22 is a flow chart illustrating another example of operation S2109 of determining at least one food item, according to various embodiments. Operations may be performed out of the order shown and / or described, and more and / or fewer operations may be performed.

[0195] According to various embodiments, in operation S2701, the electronic device can select any one of the comprehensive required nutrients included in the information on improved nutrients.

[0196] According to various embodiments, in act S2703, the electronic device may sort foods that contain the selected necessary nutrients. According to various embodiments, in act S2705, the electronic device may assign scores to the sorted foods based on the content of the necessary nutrients. For example, if a food has a high content of the necessary nutrients, the electronic device may assign a high score to the food.

[0197] According to various embodiments, in operation S2707, the electronic device can exclude foods that contain a certain value or more of comprehensive limiting nutrients included in the information on improving nutrients. For example, the certain value can be set based on an amount that is limited in intake in terms of the improving function. For example, the certain value can represent an effective amount that adversely affects the improving function.

[0198] According to various embodiments, in operation S2709, the electronic device may assign a score based on predetermined criteria to foods that have been assigned a score above a certain value. Operation S2709 may be performed in the same manner as operation S2609 described above, and therefore a redundant description will be omitted.

[0199] According to various embodiments, in operation S2711, the electronic device may determine a ranking of the foods based on the assigned scores. According to various embodiments, in operation S2713, the electronic device may determine at least one food as a recommended food based on the determined ranking. According to various embodiments, in operation S2113 described below, the electronic device may provide the recommended foods by categorizing them into recommended foods by nutrient. Since operations S2711 and S2713 can be performed in the same manner as S2611 and S2613 described above, redundant description will be omitted.

[0200] According to various embodiments, and referring again to FIG. 16 , in operation S2111, the electronic device may select at least one recommended food based on the determined at least one food. According to various embodiments, in operation S2113, the at least one recommended food may be provided to the user through a user interface. For example, the electronic device may provide the at least one recommended food separately as a recommended food by function or a recommended food by nutrient. For example, the electronic device may provide the at least one recommended food determined in operation S2505 as a recommended food by function. For example, the electronic device may provide the at least one recommended food determined in operation S2613 as a recommended food by function. For example, the electronic device may provide the at least one recommended food determined in operation S2713 as a recommended food by nutrient. By providing recommended foods separately as recommended foods by function or recommended foods by nutrient, the electronic device may provide recommended foods customized to the user's needs, thereby satisfying the user and providing efficient services.

[0201] An electronic device according to an embodiment of the present invention may provide information through a display device of a user device. Specifically, the electronic device may provide an interface including information related to a food recommendation function.

[0202] 23 is a diagram illustrating an example of a user interface according to various embodiments. For example, the interface may include at least one of a first interface 2801 showing improvement information, a second interface 2802 showing at least one recommended food, and a third interface 2803 showing the improvement effect of the recommended food.

[0203] For example, the improvement information displayed on the first interface 2801 may include information on an improvement function or information on an improvement nutrient. The information on the improvement function may be information on an improvement function acquired by the above-described operation S2309. The information on the improvement nutrient may be information on an improvement nutrient including information on comprehensive required nutrients acquired by the above-described operation S2409. This has the effect of allowing the electronic device to provide the user with the information acquired by the above-described operations, so that the user not only receives recommended foods but also obtains improvement information on their pet, thereby obtaining more accurate and specific information about their pet's health.

[0204] For example, the recommended foods displayed on the second interface 2802 may be the recommended foods determined in the above-described operations S2505, S2613, and S2713. For example, the second interface may further display information such as a product image of the recommended food, a product name that can identify the food, and a brand name that provides the food. This allows the electronic device to further provide various information about the recommended food, thereby enabling the user to easily understand the information about the recommended food and helping to actually purchase it.

[0205] For example, the improvement effects of the recommended foods shown in the third interface 2803 may include information about the health improvement effects expected when consuming the recommended foods. This allows the electronic device to provide information about the health improvement effects, making it easier for the user to understand the health improvement effects of the recommended foods, and allowing the user to more clearly recognize the effects of improving the health of their pet.

[0206] Although the embodiments have been described above with reference to limited embodiments and drawings, those skilled in the art will appreciate that various modifications and variations may be made from the above description. For example, the techniques described may be performed in an order different from that described, and / or the components of the described systems, structures, devices, circuits, etc. may be combined or combined in a manner different from that described, or may be substituted with other components or equivalents, and still achieve suitable results.

[0207] Therefore, other embodiments, examples, and equivalents of the claims are also within the scope of the following claims. [Explanation of symbols]

[0208] 100: Electronic Devices 210: Processor 220: Memory 230: Communication circuit

Claims

1. 1. A method of operating an electronic device, comprising: by at least one processor included in the electronic device; obtaining a first data set indicative of a health or disease state of the pet; obtaining pet improvement information based on the first data set; An operation of obtaining food information including contained nutrient information from a food database; comparing the improvement information and the food product information to determine at least one food product that matches the improvement information; selecting at least one recommended food item based on the determined at least one food item; providing the at least one recommended food item through a user interface; the improvement information includes improved function information indicating a function that needs to be improved and improved nutrient information indicating a nutrient corresponding to the function that needs to be improved; The improved nutrient information includes: i) information on the required nutrients and limiting nutrients corresponding to any one of the improved functions; and ii) includes at least one of information on improved function information including the first to nth improved functions, information on the first to nth limiting nutrients, and information on comprehensive required nutrients and comprehensive limited nutrients determined based on the first to nth required nutrients; The food determining operation includes: An operation of selecting any one of the improvement functions included in the improvement function information; obtaining improved nutrient information corresponding to the selected improved function; An operation of assigning a score to foods containing the necessary nutrients included in the improved nutrient information based on priority and content; An operation of excluding foods containing the restricted nutrients included in the improved nutrient information; determining the ranking of the foods based on the assigned scores; and determining at least one food item based on the determined ranking. How electronic devices work.

2. The operation of acquiring the food information further includes an operation of acquiring labeling information indicating target functions to be improved for each of a plurality of foods included in the food database; The method of claim 1 , wherein the food information further includes labeling information indicating a target function to be improved for each of a plurality of foods.

3. The food determining operation includes: An operation of selecting any one of the comprehensive necessary nutrients included in the improved nutrient information; selecting food containing the selected necessary nutrients; assigning a score to the selected food based on the content of essential nutrients; An operation of excluding foods that contain a certain value or more of comprehensive limiting nutrients included in the improved nutrient information; determining a ranking of the foods based on the assigned scores; and determining at least one food item based on the determined ranking.

4. The method further includes an operation of assigning a score based on a predetermined criterion to a food item that has been assigned a score equal to or greater than a certain value; The method of claim 1 , wherein the criteria are determined based on at least one of the following: popularity of the food, calories of the food, pet preference, or whether the food contains an allergen.

5. The method further includes an operation of assigning a score based on a predetermined criterion to a food item that has been assigned a score equal to or greater than a certain value; The method of claim 3 , wherein the criteria are determined based on at least one of the following: popularity of the food, calories of the food, preference of pets, or whether the food contains an allergen.

6. The food determining operation includes: An operation of selecting any one of the improvement functions included in the improvement function information; and comparing the selected improvement feature and the labeling information to determine at least one food item.

7. The food providing operation includes: The method of claim 1 , wherein the at least one recommended food is provided by categorizing the recommended food by function or by nutrient.

8. The user interface includes: The method of claim 7 , further comprising at least one of a first interface showing the improvement information, a second interface showing the at least one recommended food, and a third interface showing the improvement effect of the recommended food.

9. Memory and at least one processor electronically connected to the memory; The at least one processor obtaining a first data set indicative of a health or disease state of the pet; obtaining pet improvement information based on the first data set; Obtaining food information including nutrient content information from a food database; comparing the improvement information and the food product information to determine at least one food product that matches the improvement information; selecting at least one recommended food item based on the determined at least one food item; providing the at least one recommended food item through a user interface; the improvement information includes improved function information indicating a function that needs to be improved and improved nutrient information indicating a nutrient corresponding to the function that needs to be improved; The improved nutrient information includes: i) information on the required nutrients and limiting nutrients corresponding to any one of the improved functions; and ii) includes at least one of information on improved function information including the first to nth improved functions, information on the first to nth limiting nutrients, and information on comprehensive required nutrients and comprehensive limited nutrients determined based on the first to nth required nutrients; The at least one processor, in determining the food product, Selecting any one of the improvement functions included in the improvement function information, Obtaining improved nutrient information corresponding to the selected improved function; Scoring foods containing the necessary nutrients included in the improved nutrient information based on priority and content; Excluding foods containing limiting nutrients included in the improved nutrient information; The ranking of foods is determined based on the assigned scores. determining at least one food item based on the determined ranking; Computing devices.

10. A server comprising the computing device of claim 9; and at least one user device including an interface that displays information for at least one recommended food item based on a communication connection with the server.

Images