Method, apparatus and computer program for predicting growth and providing solutions by growth stages using artificial intelligence

Abstract

The present invention relates to a method, an apparatus, and a computer program for predicting growth such as height of a child teenager in a growing period by using an artificial intelligence model based on a growth stage of the child teenager in the growing period, and an exemplary embodiment of the present invention provides a method for predicting growth and providing a solution for the growth stage by using artificial intelligence, comprising the following steps: receiving time-series body information of an evaluation object; a step for classifying, on the basis of the inputted body information of the subject to be evaluated, into any one of a plurality of growth stages; a step for extracting body information of the evaluation subject belonging to the classified growth stage; inputting the extracted body information into a learned neural network, and predicting growth; and a step of providing a growth management solution on the basis of the classified growth stages.

Description

Technical Field

[0001] The present invention relates to a method, device and computer program for predicting the height and other growth of growing children and adolescents using an artificial intelligence model based on the growth stages of growing children and adolescents. Background Art

[0002] Recently, with the development of artificial intelligence technology, artificial intelligence technology is applied to various fields, and methods of generating additional information by extracting features from data through neural network models are being developed and used to replace existing data processing methods.

[0003] Neural network models used in artificial intelligence can learn to detect and identify features in input data faster and more accurately than conventional data processing. Recently, AI technology has moved beyond simple object tracking and detection, and is now being applied to learn from past experiences to predict the future or derive current features that reflect time-series changes.

[0004] Predictive analytics, a technique in statistics and data mining, extracts information from data and uses it to predict trends, behavioral patterns, and other issues. This type of predictive analytics can be applied to any field where decisions must be made based on information derived from data. The core of predictive analytics is to understand the relationships between variables and then predict unknown variables.

[0005] To this end, various approaches are adopted according to the data characteristics and the prediction object.

[0006] Among the various fields that require predictive analysis is the physical development of children and adolescents. Parents and adolescents are very interested in questions such as when and how much height they will grow.

[0007] In the past, methods have been proposed for predicting height by taking X-rays or analyzing the relationship with genetic / environmental factors (Korean Patent No. 10-2075743, Korean Patent No. 10-1866208). In addition, a method has been proposed for preparing the physical data of sample subjects with different measurement periods or measurement times into a format suitable for learning a growth prediction model (Korean Patent No. 10-2198302).

[0008] Because children and adolescents have their own unique growth stages, this needs to be taken into account to improve the reliability of solutions provided by predictive data and analysis. Summary of the Invention

[0009] Technical issues

[0010] Therefore, an object of the present invention is to solve the above-mentioned problems.

[0011] One of the various problems of the present invention is to provide a method, device and computer program for predicting the growth of children and adolescents based on their growth stages using a prediction model generated through artificial intelligence learning, and providing customized solutions for each growth stage.

[0012] Means for solving problems

[0013] In order to solve various embodiments of the problems of the present invention, a method for predicting growth and providing solutions by growth stage using artificial intelligence is provided, which includes: a step of receiving time-series physical information of an evaluation object; a step of classifying the physical information of the evaluation object into any one of a plurality of growth stages based on the input; a step of extracting the physical information of the evaluation object belonging to the classified growth stage; a step of inputting the extracted physical information into a learned neural network and predicting growth; and a step of providing a growth management solution based on the classified growth stage.

[0014] The present invention may be characterized in that the neural network includes a plurality of models, and the plurality of models learns body information extracted separately for a plurality of growth stages as learning data based on time-series body information of a plurality of sample objects.

[0015] A feature of the present invention may be providing a solution for increasing the growth prediction value of the evaluation subject when the evaluation subject is in a general growth stage.

[0016] The present invention may be characterized by providing, when the evaluation subject is in a rapid growth phase, a solution for increasing the duration of the rapid growth phase.

[0017] A feature of the present invention may be that, when the evaluation subject is in a slow growth stage, a solution for adjusting the duration of the slow growth stage is provided.

[0018] An exemplary embodiment of the present invention may provide a program stored in a computer-readable recording medium, including program codes for executing the above-mentioned method of using artificial intelligence to predict growth according to growth stages and provide solutions.

[0019] An exemplary embodiment of the present invention may provide a computer-readable recording medium having recorded therein a program for executing the above-mentioned method of using artificial intelligence to predict growth according to growth stages and provide solutions.

[0020] An exemplary embodiment of the present invention can provide a device for predicting growth and providing solutions according to growth stages using artificial intelligence, which includes: an input unit for receiving time-series physical information of an evaluation object; a growth stage judgment unit for classifying the physical information of the evaluation object input through the input unit into any one of a plurality of growth stages, and extracting the physical information belonging to the classified growth stage; a growth prediction unit for predicting growth by inputting the extracted physical information into a learned neural network; a solution generation unit for generating a growth management solution based on the physical information of the evaluation object belonging to the classified growth stage; and a display unit for displaying the generated growth management solution.

[0021] The present invention may be characterized in that the neural network includes a plurality of models, and the plurality of models learns body information extracted separately for a plurality of growth stages as learning data based on time-series body information of a plurality of sample objects.

[0022] The present invention may be characterized in that when the evaluation object is in a general growth stage, the solution generation unit provides a solution for increasing a growth prediction value of the evaluation object.

[0023] The present invention may be characterized in that, when the evaluation object is in a rapid growth phase, the solution generation unit provides a solution for increasing the duration of the rapid growth phase.

[0024] The present invention may be characterized in that, when the evaluation object is in a slow growth phase, the solution generation unit provides a solution for adjusting the duration of the slow growth phase.

[0025] Unless the features of the above-mentioned embodiments are inconsistent with or exclusive of other embodiments, they may be combined and embodied in other embodiments.

[0026] Effects of the Invention

[0027] According to various embodiments of the present invention, a prediction model generated through artificial intelligence learning can be used to accurately predict the height of children and adolescents taking into account the growth stages, and provide solutions required for height growth taking into account each growth stage.

[0028] The effects of the present invention are not limited to the above-mentioned effects, and those skilled in the art will clearly recognize other effects not mentioned from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a diagram illustrating a system for predicting growth and providing solutions according to growth stages according to an exemplary embodiment of the present invention.

[0030] Figure 2is a graph illustrating predicted height and target height by growth stage according to an exemplary embodiment of the present invention.

[0031] Figure 3 is a diagram illustrating a configuration of a neural network for performing growth prediction by growth stage and providing a solution according to an exemplary embodiment of the present invention.

[0032] Figure 4 is a diagram illustrating a first neural network model according to an exemplary embodiment of the present invention.

[0033] Figure 5 is a diagram illustrating a second neural network model according to an exemplary embodiment of the present invention.

[0034] Figure 6 and Figure 7 is a flowchart illustrating a method for predicting growth and providing solutions by growth stage using artificial intelligence according to various embodiments of the present invention. DETAILED DESCRIPTION

[0035] The following describes specific embodiments of the present invention with reference to the accompanying drawings. The following detailed description is provided to facilitate a comprehensive understanding of the methods, devices, and / or systems described herein. However, these are merely illustrative and the present invention is not limited thereto.

[0036] When describing an embodiment of the present invention, when it is judged that the specific description of the known technology related to the present invention will make the gist of the present invention unclear, its detailed description will be omitted. In addition, the terms described below are terms defined in consideration of the functions in the present invention, which can be changed according to the intention or habit of the user or operator, etc. Therefore, the terms should be defined based on the content in the entire specification. The terms used in the detailed description are only used to describe the embodiments of the present invention and are not restrictive. Unless otherwise clearly used, expressions in the singular include the meaning of the plural form. In this specification, expressions such as "including" or "having" should be interpreted as referring to certain characteristics, numbers, steps, actions, elements, parts or combinations of these, and do not exclude the existence or possibility of one or more other characteristics, numbers, steps, actions, elements, parts or combinations of these other than those mentioned.

[0037] In addition, when describing the components of the embodiments of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are only used to distinguish the components from other components and do not limit the nature, order, or sequence of the components.

[0038] In the exemplary embodiment of the present invention, children and adolescents can be understood as a concept including the growth period of the human body. In more detail, the evaluation objects in the exemplary embodiment of the present invention are defined by the meanings of infants, children, adolescents, toddlers and young children.

[0039] Infants are the continuation of the neonatal period and grow up drinking breast milk for 2 years after birth. The nutrition, caressing and excretion experiences during this period will affect the overall tendency of future life. Children usually refer to people between 6 years old and 13 years old, and in a broad sense also include infants (1 to 5 years old).

[0040] Adolescence is the period between childhood and youth, generally referring to people between the ages of 13 and 19. Infants can refer to those between the ages of 1 and 5 in the first year after birth. Children generally refer to those under the age of 15.

[0041] Therefore, the children and adolescents who are the above-mentioned evaluation objects can, in a narrow sense, refer to the period between the ages of 5 and 19 years old, including children and adolescents; in a broad sense, it can include the period from infancy to adolescence, including all general periods of physical growth.

[0042] Figure 1 is a diagram illustrating a system for predicting growth and providing solutions according to growth stages according to an exemplary embodiment of the present invention, Figure 2 is a graph illustrating predicted height and target height by growth stage according to an exemplary embodiment of the present invention.

[0043] Below, we will refer to Figure 1 and Figure 2 Provide explanation.

[0044] The system for predicting growth by growth stage and providing solutions according to an exemplary embodiment of the present invention may include an input unit 10 , a gender determination unit 20 , a growth stage determination unit 30 , a prediction unit 50 , a solution generation unit 70 , and a display unit 90 .

[0045] The system can receive time-series physical information of the evaluation subject through the input unit 10. The physical information of the evaluation subject includes not only basic information such as grade (or age), gender, height, etc., but also additional information such as weight, protein, mineral content, body fat, body water, muscle mass (soft lean mass), fat-free mass, bone tissue, skeletal muscle mass, body mass index (BMI, Body Mass Index), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference and hip circumference. This physical information is only an example to help understand the present invention, and this embodiment is not limited to this. Of course, the type of information constituting the physical information can be changed in various ways according to the embodiment.

[0046] The time series physical information of the evaluation object may be continuous information or discontinuous information, but may be included in the equivalent Figure 2 At least one piece of information during the growth stage.

[0047] More specifically, the collection period and frequency of the time series physical information of the evaluation object vary. For example, the first evaluation object may have physical information measured from the age of 8 to 12, which is part of the childhood and adolescence period. The second evaluation object may have physical information measured irregularly, such as from the age of 8, 10 to 12, 15, etc. In addition, the third evaluation object may have physical information within a certain period ( Figure 2 The body information measured multiple times during any one of the multiple growth stages in the fourth evaluation object may exist within a certain period of time ( Figure 2 Physical information measured only once during any of the multiple growth stages in the body.

[0048] As described above, the physical information of the evaluation subject may be included in the Figure 2 2 or more of the growth stages (the first evaluation object, the second evaluation object), but this may not be the case (the third evaluation object, the fourth evaluation object).

[0049] As for the third evaluation object as described, when there is multiple measurements of physical information during any growth stage of multiple growth stages, the growth stage judgment unit 30 classifies the growth stage corresponding to the physical information of the third evaluation object through the growth stage classification unit 31, and can extract the physical information through the physical information extraction unit 33. Here, the extracted physical information can generally include the physical information of the evaluation object input through the input unit 10.

[0050] However, as in the fourth evaluation object, when the physical information of the evaluation object corresponds only to any one of a plurality of growth stages and there is physical information measured only once during the period, physical information corresponding to any period can be further generated before the physical information of the evaluation object is input into the growth stage judgment unit 30.

[0051] More specifically, the input physical information of the evaluation subject may be used to generate time-series physical information corresponding to the evaluation subject in an arbitrary period based on pre-stored time-series physical growth information of a plurality of sample subjects.

[0052] As an example, physical information can be generated based on the physical information of the input evaluation object and the distribution model (similarity) of the time series physical growth information of multiple sample objects stored in advance, and physical information can also be generated based on the Bayesian inference model (conditional probability).

[0053] The growth stage judgment unit 30 can classify the physical information of the evaluation object input through the input unit 10 into any one of multiple growth stages in the growth stage classification unit 31, and can extract the physical information corresponding to the classified growth stage in the physical information extraction unit 33.

[0054] Reference Figure 2 The growth stages of childhood and adolescence can include a normal growth period (301), a rapid growth period (303), a slow growth period (305), and a period without growth plates (307).

[0055] Each growth stage can be classified according to the growth degree. Each growth stage is different, and the height growth each year is also different. Even in the same growth stage, the actual height growth will vary according to the growth type.

[0056] The normal growth period (301) usually refers to the period before puberty when secondary sexual characteristics appear. During this period, the growth plates of children and adolescents are generally open, so depending on the growth environment, the shorter growth type will generally grow 4 to 5 cm per year, while the taller growth type will grow 6 to 7 cm per year.

[0057] The rapid growth phase (303) is when secondary sexual characteristics begin to appear. Women's breasts swell and develop lumps, men's testicles enlarge, pubic hair begins to grow, and the voice changes. The rapid growth phase (303) lasts for about 2 to 3 years after the normal growth phase (301), with an average annual growth of 7 to 10 cm.

[0058] The slow growth phase (305) refers to the period when secondary sexual characteristics are completed. During this period, women can be distinguished by the onset of menstruation, while men can clearly see the changes in their pubic hair, voice, and armpit hair. If the slow growth phase (305) is more rapid than the rapid growth phase (303), the growth rate will drop rapidly, generally lasting about 2 to 3 years, with an average annual growth of about 5 to 6 cm, and then naturally stop growing. The growth plates begin to gradually close after the rapid growth phase (304), and about 6 months after entering the slow growth phase (305), the growth plates are about 50% closed.

[0059] The no-growth-plate period (307) refers to the period when the growth plates have closed. Although the growth period has not yet completely ended, it is a period when natural growth has become difficult. Generally, in the case of women, the no-growth-plate period (307) is entered within about 1 year and 6 months to 2 years, based on the onset of menstruation. In the case of men, the no-growth-plate period (307) is entered within about 1 year and 6 months to 2 years, based on the growth of armpit hair. During the no-growth-plate period (307), although the growth plates have closed and natural growth has stopped, by changing incorrect lifestyle habits and improving physical functions through customized exercises, posture correction, and nutritional intake, it is possible to grow by about 1 to 3 cm.

[0060] exist Figure 2 In the figure, the x-axis represents age and month, and the y-axis represents height (cm). Relatively speaking, the lower dotted line (P) represents the predicted growth of the evaluation object, and the upper solid line (G) represents the target growth of the evaluation object.

[0061] As described above, since the entry point of each growth stage, the growth degree of the growth stage, the end point of the growth stage, etc. are different according to gender, in this embodiment, in order to accurately predict the growth according to the growth stage and generate a solution, the gender can be classified by the gender judgment unit 20 based on the physical information of the evaluation object input through the input unit 10, and then the growth stage can be classified according to the classified gender in the growth stage judgment unit 30. After extracting the physical information, the solution generation unit 70 generates a solution considering the gender and growth stage of the evaluation object.

[0062] The prediction unit 50 is a prediction model that can be implemented using artificial intelligence with a recursive neural network (RNN) structure, so that it can use not only current values but also time series values. For example, the prediction model can be implemented using a recursive neural network, a long short-term memory (LSTM), or a gated recurrent unit (GRU). Of course, in addition to this, various previous artificial intelligence architectures can also be applied to the prediction model of this embodiment, which will be referred to later. Figures 3 to 5 Provide detailed explanation.

[0063] The solution generating unit 70 may generate a growth management solution based on the physical information of the evaluation subject corresponding to the classified growth stage.

[0064] More specifically, when the evaluation object is in the general growth stage (301), a solution for increasing the growth prediction value of the evaluation object can be provided. The growth prediction value corresponds to Figure 2The value of the y-axis in the y-axis can provide the evaluation subject with solutions for increasing the growth prediction value through the various solution display units 90 for increasing the target value of the predicted y-axis.

[0065] Examples of solutions provided by the display unit 90 may include current height, predicted height, obesity level, body fat mass, skeletal muscle mass, protein mass, mineral mass, sleep volume, exercise volume, nutritional information, lifestyle habits, posture, etc. Each indicator may be displayed as "caution," "normal," "good," etc., according to a preset range, or may be expressed as a level.

[0066] The current status, customized solutions, and precautions for each indicator can also be displayed. The current status can be displayed by stage or level based on the target value. If it is a customized solution, the information required to achieve the current target value based on the input body information can include protein, total mineral content, body fat, body water, muscle mass (softlean mass), fat-free mass, bone tissue, skeletal muscle mass, body mass index (BMI, Body Mass Index), basal metabolic rate, etc.

[0067] In case of attention, based on the input physical information, it may include information such as the current insufficient protein, total mineral content, body fat, body water, muscle mass (soft lean mass), fat free mass, bone tissue, skeletal muscle mass, body mass index (BMI, Body Mass Index), basal metabolic rate, etc. that need to be adjusted.

[0068] In addition, when the evaluation subject is in the rapid growth stage (303), a solution for increasing the period of the rapid growth stage (303) of the evaluation subject can be provided. Increasing the growth stage period is to increase the period of the rapid growth stage (303). The rapid growth period (303) can generally be defined as a period starting when the secondary sexual characteristics begin to appear and ending when the secondary sexual characteristics are completed as described above. Therefore, when the evaluation subject is in the rapid growth stage (303), a solution for delaying the completion of the secondary sexual characteristics can be provided. In other words, the evaluation subject can be provided with a solution for expanding the period of the rapid growth stage (303) through the display unit 90. Figure 2 Various solutions in the x-axis range belonging to the rapid growth stage (303).

[0069] When the above-mentioned evaluation object belongs to the general growth stage (301), it may include physical information that needs to be considered, especially information on index adjustment such as alleviating abnormal increase of sex hormones.

[0070] In addition, when the evaluation subject is in the slow growth stage (305), a solution for adjusting the period of the slow growth stage (305) of the evaluation subject can be provided. The adjustment of the growth stage period can be divided into a case where the evaluation subject's physical information is in the early stage of the slow growth stage (305) and a case where the slow growth stage (305) is in the middle and late stages of the slow growth stage (305) in the growth stage classified according to the input physical information of the evaluation subject.

[0071] The criteria for distinguishing the early and middle stages of the above slow growth stage (305) can be based on Figure 2 The x-axis is divided based on a predetermined range corresponding to the rapid growth stage (303) to the slow growth stage 305, or when judging whether the secondary sexual characteristics have ended based on the input physical information of the evaluation object, if not, it can be divided into the early stage of the slow growth stage (305), and if it has ended, it can be divided into the middle and late stages of the slow growth stage (305).

[0072] Preferably, it is possible to determine whether the secondary sexual characteristics have ended based on the physical information of the evaluation object input, so as to determine whether the physical information of the current evaluation object is in the early or middle-late stage of the slow growth stage (305). If it is impossible to determine whether the secondary sexual characteristics have been completed based on the physical information of the evaluation object input, it is possible to determine whether the physical information of the evaluation object is in the early or middle-late stage of the slow growth stage (305) based on a predetermined range corresponding to the rapid growth stage (303) to the slow growth stage (305).

[0073] In addition, if the input physical information of the evaluation subject is at the early stage of the slow growth stage (305), a timing adjustment plan for delaying the entry into the slow growth stage (305) can be provided.

[0074] As described above, the secondary sexual characteristics gradually complete the transition from the rapid growth stage (303) to the slow growth stage (305), and therefore, a solution for delaying the completion point of the secondary sexual characteristics can be provided, which is similar to the solution provided when the evaluation subject belongs to the rapid growth stage (303). In other words, the evaluation subject can be provided with a display unit 90. Figure 2 Various solutions for shifting the range of the x-axis corresponding to the slow growth stage 305 to the right are shown in FIG. In this case, the range of the slow growth stage (305) may be increased according to the physical information of the evaluation subject, or the range may be decreased as the fast growth stage (303) increases.

[0075] In addition, if the input physical information of the evaluation subject is in the middle and late stages of the slow growth stage (305), a period adjustment plan can be provided to increase the period of the slow growth stage 305. As described above, the slow growth stage (305) refers to the period when the growth plate of the evaluation subject is closed. Generally, 6 months after entering the slow growth stage (305), the growth plate is closed by about 50%. When the growth plate is closed and natural growth stops, it enters the no-growth stage (307). Therefore, in this case, a solution for increasing the period of the slow growth stage (305) can be provided. In other words, the display unit 90 can be used to provide the evaluation subject with a solution for increasing the period of the slow growth stage (305). Figure 2 Various solutions for the range of the x-axis corresponding to the slow growth phase (305).

[0076] When the evaluation object belongs to the general growth stage 301, it includes physical information that needs to be considered, and in particular, may include content related to indicators that can be adjusted to alleviate the degree of growth plate closure.

[0077] In addition, when the evaluation subject is in the growth plate-free period (307), solutions such as improving physical functions through lifestyle habits, customized exercises, posture correction, nutritional intake, etc. can be provided based on the input physical information of the evaluation subject.

[0078] During the growth plate-free period (307), the growth plates are closed and natural growth stops. Therefore, solutions to improve physical functions such as lifestyle habits, customized exercises, and posture correction can be provided based on the evaluation subject's weight, body fat, body water, muscle mass, skeletal muscle mass, body mass index (BMI), basal metabolic rate, neck circumference, chest circumference, abdominal circumference, thigh circumference, arm circumference, hip circumference, etc., or solutions to improve physical functions through nutritional intake can be provided based on protein, mineral content, bone tissue (bone density), etc.

[0079] Figure 3 is a diagram illustrating a configuration of a neural network for predicting growth and providing solutions according to growth stages according to an exemplary embodiment of the present invention, Figure 4 is a diagram illustrating a first neural network model according to an exemplary embodiment of the present invention. Figure 5 is a diagram illustrating a second neural network model according to an exemplary embodiment of the present invention.

[0080] Below, refer to Figures 3 to 5 Provide explanation.

[0081] An exemplary embodiment of the present invention may include a first model 50 and a second model 13 , and may construct a pipeline in which at least a portion of an output of the second model 13 is input to the first model 50 .

[0082] More specifically, the first model 50 learns physical information corresponding to at least one of multiple growth stages based on time-series physical information of multiple sample subjects as learning data. The first model 50 includes an LSTM neural network 50 for learning time-series data, and uses the past physical information of multiple sample subjects to learn the LSTM neural network 50. The physical information 11 of the current evaluation subject is then input into the learned LSTM neural network 50, which outputs a predicted growth degree by growth stage.

[0083] LSTM neural network 50 uses at least one of the body information of multiple sample subjects as a default value for learning. For example, for height, training is performed using annual height data from an arbitrary period or a specific growth stage, and predictions for the next year are compared with the actual data. This comparison allows the training set to be moved into the future in units of arbitrary periods or specific growth stages and learned.

[0084] Furthermore, the LSTM neural network 50 can learn for each growth stage. Therefore, each of the normal growth stage (301), the rapid growth stage (303), the slow growth stage (305), and the no growth stage (307) can be learned as the past physical information 11 of the corresponding growth stage.

[0085] Exemplarily, in this embodiment, the time series physical information of multiple sample objects can be input in sequence according to age or any period as learning data, and the calculation results of the predicted values of the past time points or growth degrees can be transmitted to the growth degree prediction of the next age or any period.

[0086] Therefore, the LSTM neural network 50 not only predicts growth based on the current physical information 11, but can also learn the degree to which the prediction results (50-1, 2, 3, 4) of various indicators at past time points affect the current growth prediction. Thus, the items that have the greatest impact on growth changes can be extracted based on the age or any period in the indicator and reflected in the growth prediction.

[0087] Furthermore, for time series learning, it is necessary to regularly acquire the physical information of multiple sample subjects. However, as mentioned above, it may be difficult to regularly acquire the physical information of multiple sample subjects over an age or arbitrary period. Therefore, it is possible to remove outlier or discontinuous physical information for each unit period and normalize it temporally for use.

[0088] In addition, the second model 13 can derive bone maturity (age) from the hand bone image using a convolutional neural network (CNN) learned using bone maturity data of the evaluation subject as learning data.

[0089] More specifically, the convolutional neural network includes multiple convolution layers and pooling layers, wherein the multiple convolution layers extract feature maps of features in the analysis object image in the hand bone image, and the pooling layer performs sub-sampling between the multiple convolution layers, thereby extracting features at different levels of the analysis object area, probabilistically inferring features through activation functions, or performing regression analysis to derive bone maturity through weight learning between nodes.

[0090] The bone maturity extracted by the second model 13 is input into the LSTM neural network 50 together with at least a portion of the physical information 11 of the evaluation subject, which can improve the growth prediction accuracy of the evaluation subject.

[0091] Figure 6 and Figure 7 is a flowchart illustrating a method of predicting growth by growth stage and providing solutions using artificial intelligence according to various embodiments of the present invention.

[0092] Below, refer to Figure 1 、 Figure 6 and Figure 7 Provide explanation.

[0093] When the physical information of the evaluation object is input through the input unit 10 (S100), the growth stage classification unit 31 of the growth stage judgment unit 30 classifies any one of a plurality of growth stages based on the input physical information of the evaluation object (S310), and the physical information extraction unit 33 can extract the physical information of the evaluation object corresponding to the classified growth stage (S330).

[0094] The prediction unit 50 can predict growth based on the extracted physical information (S350). As described above, the growth prediction step (S350) includes multiple models constructed using physical information extracted individually for multiple growth stages based on time-series physical information of multiple sample subjects as learning data. The solution generation unit 70 can then generate a growth management solution based on the classified growth stages (S370). The generated growth management solution (S370) can then be displayed to the subject via the display unit 90.

[0095] In addition, as described above, in each growth stage, since the criteria for classifying the growth stage may differ depending on the gender of the sample subject, an exemplary embodiment of the present invention may further include a step (S200) of classifying the gender of the evaluation subject based on the physical information input through the gender judgment unit 20 after receiving the physical information of the evaluation subject (S100).

[0096] The present invention has been described above with emphasis on preferred embodiments. All embodiments and conditional examples disclosed in this specification are intended to help those skilled in the art understand the principles and concepts of the present invention. Therefore, it should be understood that those skilled in the art may implement variations without departing from the essential features of the present invention.

[0097] Therefore, the disclosed embodiments should be considered from an illustrative point of view rather than a restrictive point of view. The scope of the present invention should be determined by the scope of the claims rather than the above description, and all differences within the scope of equivalence thereof should be included in the present invention.

[0098] In addition, the methods according to various embodiments of the present invention can be implemented as a program and then provided to a server or device. Therefore, each device can connect to the server or device storing the program and download the program.

[0099] In addition, the methods according to various embodiments of the present invention described above can be implemented by programs and stored in various non-transitory computer readable media. Non-transitory computer readable media refers to media that semi-permanently stores data and can be read by a device, rather than media that store data for a short period of time, such as registers, caches, and memories. Specifically, the various applications or programs described above can be stored and provided on non-transitory computer readable media, such as CDs, DVDs, hard disks, Blu-ray discs, USBs, memory cards, ROMs, and the like.

[0100] Although preferred embodiments of the present invention are illustrated above, the present invention is not limited to the above-mentioned specific embodiments. A person skilled in the art may make various modifications without departing from the spirit of the present invention as claimed in the claims, and these modifications should not be understood separately from the technical ideas or prospects of the present invention.

Claims

1. A method for predicting growth and providing solutions based on growth stages using artificial intelligence, characterized in that: include: The step of receiving time series physical information of the evaluation subject; a step of classifying the subject's physical information into any one of a plurality of growth stages based on the input; a step of extracting physical information of the evaluation subject belonging to the classified growth stage; Inputting the extracted body information into a learning neural network and predicting growth steps; and Based on the classified growth stages, steps for growth management solutions are provided.

2. The method of using artificial intelligence to predict growth and provide solutions according to growth stages according to claim 1, characterized in that: The neural network includes a plurality of models that learn body information individually extracted for a plurality of growth stages as learning data based on time-series body information of a plurality of sample subjects.

3. The method of using artificial intelligence to predict growth and provide solutions according to growth stages according to claim 2, characterized in that: When the evaluation subject is in a general growth stage, a solution for increasing the growth prediction value of the evaluation subject is provided.

4. The method of using artificial intelligence to predict growth and provide solutions according to growth stages according to claim 2, characterized in that: When the evaluation subject is in a rapid growth phase, a solution for increasing the duration of the rapid growth phase is provided.

5. The method for predicting growth and providing solutions based on growth stages using artificial intelligence according to claim 2, characterized in that: When the evaluation subject is in a slow growth stage, a solution for adjusting the duration of the slow growth stage is provided.

6. A program stored in a computer-readable recording medium, comprising program codes for executing the method for predicting growth and providing solutions according to growth stages using artificial intelligence according to any one of claims 1 to 5.

7. A computer-readable recording medium having recorded thereon a program for executing the method for predicting growth and providing solutions according to growth stages using artificial intelligence according to any one of claims 1 to 5.

8. A device that uses artificial intelligence to predict growth and provide solutions according to growth stages, characterized in that: include: an input unit for receiving time series physical information of an evaluation subject; a growth stage determination unit that classifies the physical information of the evaluation subject input through the input unit into any one of a plurality of growth stages, and extracts the physical information belonging to the classified growth stage; a growth prediction unit that predicts growth by inputting the extracted body information into a learned neural network; a solution generating unit for generating a growth management solution based on the physical information of the evaluation subject belonging to the growth stage of the classification; as well as The display unit is used to display the generated growth management solution.

9. The device for predicting growth and providing solutions by using artificial intelligence according to growth stages according to claim 8, characterized in that: The neural network includes a plurality of models that learn body information individually extracted for a plurality of growth stages as learning data based on time-series body information of a plurality of sample subjects.

10. The device for predicting growth and providing solutions by using artificial intelligence according to growth stages according to claim 9, characterized in that: When the evaluation object is in a general growth stage, the solution generation unit provides a solution for increasing a growth prediction value of the evaluation object.

11. The device for predicting growth and providing solutions by using artificial intelligence according to growth stages according to claim 9, characterized in that: When the evaluation object is in a rapid growth phase, the solution generation unit provides a solution for increasing a duration of the rapid growth phase.

12. The device for predicting growth and providing solutions based on growth stages using artificial intelligence according to claim 9, characterized in that: When the evaluation object is in a slow growth phase, the solution generation unit provides a solution for adjusting a period of the slow growth phase.

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