KR102196766B1 - Method and system for analysing infant life pattern using artificial intelligence - Google Patents

Abstract

According to one embodiment of the present invention, a method for analyzing a problem of a lifestyle pattern of infants may be provided. At this time, the method for performing analysis of a problem of the lifestyle pattern of infants may comprise the steps of: receiving input information from a user, and converting the received input information into a numerical value; performing data analysis based on the digitized input information; deriving a problem by applying the analyzed data to a first type artificial intelligence system; and providing a solution corresponding to the problem.

Description

A method and system for analyzing infant life patterns using artificial intelligence {METHOD AND SYSTEM FOR ANALYSING INFANT LIFE PATTERN USING ARTIFICIAL INTELLIGENCE}

The present invention relates to a method and system for performing an infant and toddler life pattern analysis using artificial intelligence.

In the modern society where working moms are increasing, there is a need for a solution to the problem of feeding infants or sleep control problems after childbirth. However, at present, the problem of feeding and sleep control is being solved at home through empirical rules, and a system is needed to solve this problem.

It is difficult for many working moms to get advice on how to control infant feeding and sleep, and there are many cases in which infants' lifestyle patterns cannot be properly managed. In addition, considerable difficulties arise in their work life and child-rearing life. Infants show a very large pattern of changes in feeding volume, feeding interval, sleep volume, and sleep time every few weeks after birth. In other words, recommendations to ensure that infants develop properly and have a stable sleep may be set differently.

In consideration of the above points, a method of solving the feeding problem and the sleep control problem without the help of an expert may be required, and a method and system for analyzing infant and toddler life patterns using artificial intelligence are provided below.

It is an object of the present invention to provide a method and system for analyzing infant and toddler life patterns using artificial intelligence (AI).

Another object of the present invention is to provide an application that analyzes the life pattern of infants and toddlers using artificial intelligence.

According to an embodiment of the present invention, it is possible to provide a method of performing an analysis of a problem of an infant's life pattern. In this case, the method of performing the analysis of the infant life pattern problem includes receiving input information from a user, digitizing the received input information, performing data analysis based on the digitized input information, and removing the analyzed data. It may include applying to a type 1 artificial intelligence system to derive a problem and providing a solution corresponding to the problem. At this time, the input information includes at least one or more of first type input information, second type input information, and third type input information, which is text input information by a user, which is basic information about infants, and the second type input When information is applied to the first type artificial intelligence system, a plurality of the second type input information is derived as information classified into a plurality of combinations through the first type artificial intelligence system based on a neural network analysis method, and the The above problem can be derived based on the derived information.

In addition, according to an embodiment of the present invention, an apparatus for performing infant life pattern problem analysis may include a processor for performing infant life pattern problem analysis and a memory for storing the infant life pattern problem analysis information. At this time, the processor receives input information from a user, quantifies the received input information, performs data analysis based on the digitized input information, and applies the analyzed data to a first type artificial intelligence system. Deriving a problem and providing a solution corresponding to the problem, wherein the input information is the first type input information, the second type input information, and the third type input information that is text input information by the user. Including at least one or more of, and when the second type input information is applied to the first type artificial intelligence system, a plurality of the second type input information is the first type artificial intelligence system based on a neural network analysis method. It is derived as information classified into a plurality of combinations through, and the problem may be derived based on the derived information.

In addition, the following items can be commonly applied to methods and apparatus for performing infant analysis.

In addition, according to an embodiment of the present invention, receiving user feedback information on the problem and the solution corresponding to the problem, and the problem and the solution corresponding to the problem based on the user feedback information The method may further include applying a second type artificial intelligence system, and performing relearning on the problem and the solution corresponding to the problem.

In addition, according to an embodiment of the present invention, the user feedback information on the solution is evaluation grade information on the problem and the solution corresponding to the problem, and the second type artificial intelligence system is based on the evaluation grade information. It is determined whether to maintain the problem and the solution corresponding to the problem, but if the solution corresponding to the problem is determined to be below a preset level based on the second type artificial intelligence system, the problem and the problem The solution corresponding to can be deleted.

Further, according to an embodiment of the present invention, the second type artificial intelligence system derives a new problem using at least one of the first type input information, the second type input information, and the third type input information, When solution information on the newly derived problem is obtained using the learning information of the second type artificial intelligence system, the newly derived problem and a solution corresponding thereto may be added.

In addition, according to an embodiment of the present invention, when the first type artificial intelligence system is applied, the information classified into the plurality of combinations derived based on the plurality of second type input information and the first type input The problem may be derived by further considering the information and the third type input information.

In addition, according to an embodiment of the present invention, when the first type artificial intelligence system is applied, the first type input information and the plurality of combinations derived based on the plurality of second type input information are classified. A weight for the information and the third type input information may be determined, and a problem may be derived based on the determined weight.

In addition, according to an embodiment of the present invention, the first type input information is, for infants 12 months or older, gender, age, height, weight, head position, gestational age, sibling relationship, baby food status, baby food Information, health information, sleep and sleep education information, parenting environment, temperament of babies and mothers, and at least one of sleep information are included.In the case of infants under 12 months of age, the feeding method, feeding term, and number of nights are listed above. The second type input information is further included in the first type of input information, and the second type of input information is information determined based on at least one of statistics object information, time classification information, statistical item information, and additional information information for the infant, and the statistics The target information is any one of lactation and sleep, the time classification information is any one of day, night, and total, and the statistical item information is any one of quantity, number, term, and regularity, and the plurality of second types The information classified into the plurality of combinations derived based on the input information is determined by further considering the combination of the statistical object information, the time classification information, and the statistical item information, and the additional information information, and the third type input The information may be specific information related to the second type input information.

In addition, according to an embodiment of the present invention, the problem and the solution corresponding to the problem may be provided to a user through at least one of a smart device, a smart phone, and a tablet.

According to an embodiment of the present invention, it is possible to provide a method and system for analyzing infant and toddler life patterns using artificial intelligence.

According to an embodiment of the present invention, life pattern analysis information may be provided without the help of an expert through an infant life pattern analysis application using artificial intelligence.

According to an embodiment of the present invention, a method of analyzing an infant's life pattern using various types of artificial intelligence may be provided.

1 is a diagram showing an application that provides an analysis of infant life patterns according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating a method of providing an analysis of infant life patterns and feeding analysis according to an exemplary embodiment of the present invention.
3 is a diagram illustrating a method of performing an infant and toddler life pattern analysis using artificial intelligence according to an embodiment of the present invention.
4 is a diagram showing life pattern analysis information according to an embodiment of the present invention.
5 is a diagram illustrating a specific application method of an artificial intelligence system according to an embodiment of the present invention.
6 is a diagram illustrating a method of applying an artificial intelligence system based on a life pattern analysis problem and solution according to an embodiment of the present invention.
7 is a diagram illustrating a method of applying an artificial intelligence system based on a life pattern analysis problem and solution according to an embodiment of the present invention.
8 is a diagram illustrating a method of applying an artificial intelligence system based on a life pattern analysis problem and solution according to an embodiment of the present invention.
9 is a diagram illustrating a method of performing a life pattern analysis based on a fuzzy analysis method according to an embodiment of the present invention.
10 is a diagram illustrating a method of performing a life pattern analysis based on a fuzzy analysis method according to an embodiment of the present invention.
11 is a diagram illustrating a method of performing a life pattern analysis based on a fuzzy analysis method according to an embodiment of the present invention.
12 is a diagram showing a device configuration according to an embodiment of the present invention.

Since the present invention can be modified in various ways and has various embodiments, specific embodiments will be illustrated in the drawings and will be described in detail in specific details for carrying out the invention. However, this is not intended to limit the present invention to a specific embodiment, it is to be understood to include all changes, equivalents, and substitutes included in the spirit and scope of the present invention. In describing each drawing, similar reference numerals have been used for similar elements.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. These terms are used only for the purpose of distinguishing one component from another component. For example, without departing from the scope of the present invention, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. The term and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. Should be. On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present application, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not. Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram showing an application that provides an analysis of infant life patterns according to an embodiment of the present invention.

Referring to FIG. 1, an application for analyzing an infant life pattern (or sleep analysis) may be provided through a terminal. In this case, as an example, an application for analyzing an infant's life pattern may be provided through a terminal or a smart device (e.g. a smartphone, a smart pad, etc.). As another example, the application may be software provided through a computer or a laptop computer, and is not limited to the above-described embodiment. In this case, as an example, the application may provide a method of analyzing an infant's life pattern. In addition, as an example, the application may provide information related to infant life patterns such as infant feeding, meals, and bowel movements. In this case, as an example, the application may check information on a user and provide analysis information based thereon. For example, the user information may be information on infants and toddlers that need to be analyzed. In this case, the user information may be date of birth, weight, height, and other related information for infants and toddlers, and is not limited to the above-described embodiment. That is, the application may receive basic information necessary for analysis from the user.

FIG. 2 is a diagram illustrating a method of providing an analysis of infant life patterns and feeding analysis according to an exemplary embodiment of the present invention. As a more specific example, referring to FIG. 2, the application may provide related information on a daily basis based on user information. In addition, as an example, the application may receive input information from a user and perform analysis based thereon. For example, in FIG. 2, the application may receive input information from a user. In this case, the input information may be information related to the amount of feeding, such as "breast milk, 20 minutes left". It can also be information related to bowel movements, such as "diapers, poop". In addition, it may be information related to sleep, such as "sleep, 1H 15M". That is, the application can receive information related to the information that needs to be analyzed from the user. In addition, the application may provide analysis information or recommendation information based on input information and user information. For example, the analysis information may be calculated based on information input by the user, and recommendation information, which is recommended information, may be provided based on the calculated analysis information.

As a more specific example, the input information may be information related to infants and toddlers. In this case, the information related to the infant and toddler may be at least one of the infant's name, date of birth, height, and weight. Also, the input information may include infant and toddler routine information. In this case, the routine information may include at least one of lactation information, sleep information, play information, and bowel movement information. Also, as an example, the input information may include basic survey information. In this case, the basic survey information may be information on specific information for each user. For example, disease or other individual characteristic information may exist for each infant. In this case, characteristic information on infants and toddlers may be required in order to provide life pattern analysis or other analysis information, and the information may be basic survey information. In this case, as an example, the application may list items for basic investigation information and provide the items to the user. In addition, as an example, the application may receive the above-described basic investigation information through input information created by the user, but is not limited to the above-described embodiment. Also, as an example, the application may receive memo information from a user. In this case, as an example, the memo information may be real-time information and may be information input by a user through text during a daily routine. That is, the application may receive various types of user input information, and is not limited to the above-described embodiment.

However, as an example, when an application provides the above-described information, the application needs to provide customized information in consideration of each user information and various cases. In more detail, each analysis information may be different for each user. In this case, as an example, in the past, a service for an individual user was provided through a method of providing customized information for each individual user using experts, but a system for this may be required in consideration of an increase in users and a lack of experts. In addition, when the existing system does not use an expert, the system can only provide uniform information, and there is a limitation in providing user-customized information. In consideration of the above points, a method and system for providing life pattern analysis information and other information using artificial intelligence will be described below.

3 is a diagram illustrating a method of performing an infant and toddler life pattern analysis using artificial intelligence according to an embodiment of the present invention.

Referring to FIG. 3, the above-described application may receive input information from a user and quantify it. (S310) In this case, as an example, the input information is sleep related information, lactation related information, and bowel movement related information as described above. And other information, and is not limited to the above-described embodiment. Next, the application may perform data analysis based on the input information. In this case, as an example, the application may automatically perform data analysis based on input information, generate a report according to the performed result, and provide it to the user. In this case, for example, data has been analyzed through data pre-processing and statistical processing in the past, but there is a need to perform data analysis in consideration of artificial intelligence. In this case, as an example, the application may provide result information to the user based on data analysis. As a more specific example, the input information may be information such as “target sleep time”, “moon age” and “gender”. At this time, the user may wish to derive information on the “target sleep time” through the application, and information for this may be input. In this case, as an example, the application may receive breastfeeding and sleep data for the last 3 days, calculate an optimal wake-up time based on the data, and provide it to the user. In this case, the above-described method is only an example, and is not limited to the above-described embodiment.

As another example, the application may derive a problem of life patterns for infants and toddlers through the above-described data analysis. In this case, as an example, a report may be generated and provided to a user through a matching solution based on the derived problem.

In addition, as an example, the application may apply the analyzed data to the first type artificial intelligence system and derive a problem. (S330) Thereafter, the application may provide a solution to the derived problem. (S340)

4 is a diagram showing life pattern analysis information according to an embodiment of the present invention.

In addition, as an example, data preprocessing and statistical analysis methods may be used in connection with data analysis, as described above. In this case, for example, in relation to statistical analysis, data analysis may be performed based on statistical items. As a more specific example, statistics targets may be “nursing” and “sleep”, and time classification may be set to “day”, “night” and “total time”. At this time, the statistical items may be amount/time, number of times, term, and regularity. For example, the regularity may be the same as that of FIG. 4, but is not limited thereto. That is, the information on regularity may be set based on the information as shown in FIG. 4 and may be utilized. At this time, since the above-described statistical objects are 2 types, time classification is 3 types, and statistical items are 4 types, a total of 24 types of statistical data can be derived. In this case, the application may perform data analysis based on statistical data and calculate output information through the analyzed data. As another example, text data-based information may be used based on the above-described basic survey information or memo as well as the above-described statistical analysis data. In this case, as an example, the text data may be specific information such as "Sleep is maintained only when an infant is held, and when it is put down, awakening from the sleep". In this case, as an example, the application may perform additional analysis by reflecting the above-described information. In addition, as an example, the text data may be optional information such as “whether to use an empty nipple: O/X”. In addition, other information used as a memo may be used and is not limited to the above-described embodiment. In this case, when the application further considers text information, the number of possible data may further increase. For example, the application may be able to derive problems and provide solutions in consideration of the above-described case by using stored information or other data, but the above-described method may have limitations.

5 is a diagram illustrating a specific application method of an artificial intelligence system according to an embodiment of the present invention.

As described above, when a problem is derived through an application and a solution is provided, it may be difficult to derive a problem and provide a solution customized based on various information. In consideration of the above points, the application may need a method of performing different data processing in consideration of the type of each input information when digitizing input information. In more detail, referring to FIG. 5, the first type input information may be basic information. For example, it may be basic information that can be objectively quantified, such as age, weight, height, and presence of atopy. In this case, since the first type of input information may be objectified information, it may not be necessary to apply an artificial intelligence system to this. On the other hand, the second type of input information may be input information requiring application of an artificial intelligence system. For example, the input information required by the artificial intelligence system may be input information that varies differently according to each user, such as sleep time, sleep frequency, sleep term, and sleep rules, rather than being constantly expressed as objective values. In this case, as an example, the application may drive an artificial intelligence system that inputs the above-described information. As an example, the artificial intelligence system may learn using the above-described information based on learning. In more detail, the artificial intelligence system may derive a plurality of possible output values based on the plurality of input information described above. For example, possible combinations may be calculated based on a plurality of input information, and the calculated combinations may be classified. In this case, a list of problems may be derived based on the classified combinations. In this case, as an example, reverse application of the artificial intelligence system may be performed to each of the classified combinations. In more detail, each of the classified combinations may be given a grade according to a certain criterion based on learning an artificial intelligence system. In this case, the area in which the most problems are derived may be calculated using information learned by the artificial intelligence system according to each grade. Based on the above, a problem may be derived for each grade, and a solution for this may be matched.

In addition, as a specific example, the artificial intelligence system described above may be an artificial intelligence system that operates based on a fuzzy analysis method. That is, a plurality of input information may be classified into each combination according to a certain condition based on a fuzzy analysis method. In this case, the classified combinations can be made to correspond to the problems that are most often derived according to the grade, and the problems can be derived based on this. That is, a list of problems and a list of solutions may be derived based on the input information.

In addition, the application may receive feedback on the problem list and the solution list from the user. At this time, the application may proceed with the evaluation of the problem list and the solution list. For example, the above-described artificial intelligence system may derive high-accuracy output by learning input information based on a result of a problem list and a solution list as feedback. Also, the third type of input information may be natural language processing information. In this case, the natural language processing information may be the above-described text information. In this case, as an example, the application may derive a problem based on the first type artificial intelligence system based on the above-described first type input information, second type input information, and third type input information. In other words, it is possible to derive a problem list by checking information derived from the second type input information, objective information, and natural language information together.

As a more specific example, input information for a 1-year-old infant may be received as the infant's age information as the first type input information. In addition, as an example, as the second type of input information, input information on sleep time information of a corresponding infant may be received. At this time, as the second type of input information, the sleep time information of the corresponding infant may be analyzed through an artificial intelligence system. In addition, it is possible to receive sleep habit information of the corresponding infant as the third type input information. At this time, the problem is to separate users who may have similar sleeping habits based on information of 1-year-old infants that are objectively compared, and use this together with the second type input information analyzed through the artificial intelligence system to derive the problem. I can. That is, the problem list may be derived through the artificial intelligence system based on the input information. After that, the application can derive a solution corresponding to each problem and provide information about it to the user.

In this case, for example, when a problem is derived based on an artificial intelligence system, a weight of input information for deriving the problem may be set differently. As an example, the weight may be a weight for first type input information, second type input information (or statistical item value to which artificial intelligence is applied), and third type input information. In this case, the artificial intelligence system may determine a weight for each piece of information. As a more specific example, as the third type of input information, the weight for “whether to use a black nipple: O/X” may be different from the information on “Sleep is maintained only when an infant is held, and wakes up from the sleep when put down”. As an example, the artificial intelligence system may perform learning based on a plurality of input information and feedback information for a user, and may determine the above-described weight based on the performed learning information, but is not limited to the above-described embodiment.

6 is a diagram illustrating a method of applying an artificial intelligence system based on a life pattern analysis problem and solution according to an embodiment of the present invention.

As described above, when a problem list is derived based on the first type input information, the second type input information, and the third type input information, the solution list may be mapped to each problem list. That is, a solution corresponding to each problem can be mapped. In this case, as an example, the application may perform artificial intelligence learning for a corresponding problem and solution. That is, artificial intelligence learning for problem derivation and artificial intelligence system for learning “problem-solution” may be implemented separately. As a more specific example, referring to FIG. 7, a problem-solution list may be derived based on input information as described above. At this time, the application may receive feedback information on the problem-solution list from the user. In this case, the feedback information may be accuracy information or satisfaction information measured by the user. In other words, it may be evaluation information for each problem-solution. In this case, the system may perform artificial intelligence learning using the above-described evaluation information. That is, the feedback information for the user may be input information for updating (or relearning) a problem-solution. At this time, the application is a problem? You can update the solution list. That is, the application derives a problem by applying an artificial intelligence system to the input information, matches the solution to the derived problem, and then applies the artificial intelligence system once more with the user's feedback on the solution. Thus, reliability can be increased. At this time, as an example, referring to FIG. 8, a problem-solution list may be updated based on the artificial intelligence system. For example, when the list is updated, a new problem-a solution may be created or an existing problem-a solution may be deleted.

As a more specific example, as described above, user feedback information may be received for a problem-solution. In this case, the user's feedback information may be problem-solution evaluation information or evaluation grade information. For example, the user's feedback information may be at least one or more of score information and grade information. In addition, as an example, the user's feedback information may be text input information or other evaluation information, and is not limited to the above-described embodiment. In this case, as an example, the artificial intelligence system may determine whether to maintain the problem-solution based on the above-described user's feedback information. In this case, as an example, the artificial intelligence system may perform learning based on input information and feedback information, and may set a preset level for a problem-solution based on the learning information. For example, if the problem-solution is determined to be below a preset level based on the artificial intelligence system, the problem-solution may be deleted. That is, as in FIG. 8, the problem-solution can be deleted. Also, as an example, a problem-solution may be newly created. In this case, as an example, the artificial intelligence system may newly derive a problem by using at least one of the aforementioned first type input information, second type input information, and third type input information. In this case, the artificial intelligence system may search for a solution to a newly derived problem through learning about the user's feedback information. At this time, if the problem-solution is searched and matched, the artificial intelligence system may generate a new problem-solution, which may be as shown in FIG. 8.

That is, according to an embodiment of the present invention, the application may apply an artificial intelligence system for deriving a problem based on the first type input information, the second type input information, and the third type input information. In this case, as an example, the artificial intelligence system used to derive a problem may be a first type artificial intelligence system. Also, as described above, the problem? When a solution list is created, the problem-artificial intelligence system can be applied by inputting user feedback information on the solution list. In this case, the problem-solution list updated (or relearned) based on user feedback may be a second type artificial intelligence system. That is, a two-stage artificial intelligence system may be applied to an application that provides analysis of infant life patterns using artificial intelligence, and reliability for providing life pattern analysis may be increased based on this. In this case, as an example, the first type artificial intelligence system and the second type artificial intelligence system described above may be artificial intelligence systems that operate based on the Fuzzy analysis method. Also, as an example, the first type artificial intelligence system and the second type artificial intelligence system may be artificial intelligence systems that operate based on machine learning. In more detail, the artificial intelligence system may perform the above-described data preprocessing based on the input information. In this case, errors (e.g. typos) in input information may be corrected based on data preprocessing, and an important specific value may be derived from the input information. After that, each piece of information can be classified based on a certain category. In this case, as an example, the input information may be numerical information or textual information as described above. In this case, the input information may be classified according to a certain category. Thereafter, the classified input information may be clustered or grouped and stored as related information. For example, when a problem is derived based on the above-described first type artificial intelligence system, similar input information may be grouped to correspond to the same problem. Also, text input may be grouped into each group based on a word or meaning, and is not limited to the above-described embodiment. Thereafter, machine learning may be performed based on the grouped information, and statistical item values may be derived based on the performed machine learning. In this case, as an example, a final problem may be derived by using the derived statistical item values, and is not limited to the above-described embodiment. That is, the problem can be derived from the values of statistical items.

Based on the above-described method, a service based on an analysis of a problem of an infant's life pattern may be provided. For example, a recommended timetable for infants and toddlers may be provided to the user through an application based on the analysis of the problem of infants and toddlers' life patterns. In this case, as an example, the user may input the above-described input information for infants and toddlers and timetable information for infants and toddlers. In this case, the application may provide a recommended timetable to the user through comparison and setting of the wake-up time. In the above-described process, the application analyzes data based on the above-described input information (eg, first type input information, second type input information, and third type input information), and calculates statistical item values based on the artificial intelligence system. Can be derived. As an example, the artificial intelligence system may be the aforementioned artificial intelligence system. At this time, the application may derive a problem and a solution corresponding thereto based on the derived statistical value. For example, a problem and a solution corresponding to the problem may be a plurality of problem-solution lists. In this case, as an example, the application may preferentially provide information on the above-described problem-solution list to the user. In addition, as an example, the application may receive wake-up time, lactation or sleep-related information from the user, and generate a recommended timetable based on the information and provide it to the user. As a more specific example, with respect to the wake-up time setting algorithm, between 6 o'clock and 8 o'clock may be a recommended wake up time. In this case, as an example, the application may determine the wake-up time as the fastest time among the recommended wake-up times. At this time, as an example, when breastfeeding is between 5 o'clock and 6 o'clock, the wake-up time may be set to 6 o'clock. In addition, as an example, for infants and toddlers who are nursing between 6 o'clock and 8 o'clock, the wake-up time may be set by rounding up to 30 minutes. In this case, as described above, the wake-up value may be set to a time with a high frequency rather than an average.

In this case, as an example, information on a problem-solution list derived through an artificial intelligence system may be reflected in the above-described derivation method to set a recommended wake-up time and provided to the user, but is not limited to the above-described embodiment.

In addition, as an example, Table 1 below may be the number in the case of the number of baby foods according to the feeding term and the number of naps. As an example, input information shown in Table 1 below may be combined and provided as statistical information, and a problem-solution list may be determined by applying this to an artificial intelligence system, and recommended information may be generated, as described above.

[Table 1]

9 is a diagram illustrating a method of performing a life pattern analysis based on a fuzzy analysis method according to an embodiment of the present invention.

In addition, as an example, as the artificial intelligence system described above, the Fuzzy analysis method may be applied. However, it is not limited thereto. As an example, the fuzzy analysis method may generate a fuzzy set and generate a rule for the fuzzy set to predict statistical item values. In this case, the application may perform fuzzy inference based on each function/rule and perform defuzzification on it to update existing information. In addition, as an example, not only the above-described method but also other artificial intelligence systems may be applied, and the embodiment is not limited thereto.

As a more specific example, the fuzzy analysis method may not be uniform information of “O/X” for each value, but may be output for various inputs based on a rule. As an example, it is possible to derive output information in consideration of possible combinations of respective inputs based on a rule. In this case, the above-described input information may be information on infants and toddlers in relation to sleep as described above. As a more specific example, a fuzzy analysis method may be applied in consideration of the age of infants and young children to derive a statistical item value, and analysis information may be provided using this. However, as an example, if only the fuzzy analysis method is used, there may be a limit to problem analysis. Therefore, as described above, it is necessary to separately apply input information to which the Fuzzy analysis method (or artificial intelligence system) can be applied and objective input information among the input information, and through this, data analysis can be performed.

10 is a diagram illustrating a method of performing a life pattern analysis based on a fuzzy analysis method according to an embodiment of the present invention.

Referring to FIG. 10, a problem may be derived and a solution may be provided based on the above-described Fuzzy analysis method. In this case, as an example, the input information may be the above-described information. In this case, the application may perform data analysis based on the input information and apply the fuzzy analysis method by functionalizing the data. Thereafter, the application may perform defuzzification based on the derived output, and thereby derive problems and solutions. However, as an example, there may be limitations in providing a problem and a solution to a method for analyzing a life pattern using only the above-described method.

In this case, referring to FIG. 11, various types of information among the input information may be used to derive problems. As an example, in FIG. 10, the application applied a fuzzy analysis method through “standard stage” information, “statistical analysis” information, and “text analysis” information based on input information, and de-fuzzyed it to derive a problem. . However, in order to increase the reliability of problem derivation, other information can be further used when deriving a problem. For example, the “standard step” information may be the above-described first type input information. In other words, in the process of deriving a problem, other information may be further used together with the information that is defuzzed. In addition, as an example, the “text analysis” information may be third type input information. That is, the input information used for application to the fuzzy analysis method can be used once more to derive a problem, and through this, the reliability of problem-solution derivation can be increased.

That is, as described above, a problem may be derived by applying an artificial intelligence system (or a Fuzzy analysis method) to input information, and reliability may be increased by applying the artificial intelligence system to the problem-solution list once more. In this case, as an example, in the case of applying the artificial intelligence system, the first type input information or the third type input information may be used as described above, and the embodiment is not limited thereto.

12 is a diagram showing a device configuration for the operation of the present invention. Referring to FIG. 12, the device 1200 may include a processor 1210 and a memory 1220. In this case, as an example, the above-described application may operate based on the processor 1210 of the device 1200. In this case, the processor 1210 may receive the above-described input information and store the information thereon in the memory 1220. In addition, as an example, the processor 1210 may store the artificial intelligence system in the memory 1220 and may also store information for related operations in the memory 1220. That is, the above-described application may be operated based on the corresponding device 1200 and is not limited to the above-described embodiment.

The embodiments of the present invention described above can be implemented through various means. For example, embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.

In the case of hardware implementation, the method according to embodiments of the present invention includes one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), and Programmable Logic Devices (PLDs). , Field Programmable Gate Arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and the like.

In the case of implementation by firmware or software, the method according to the embodiments of the present invention may be implemented in the form of a module, procedure, or function that performs the functions or operations described above. The software code may be stored in a memory unit and driven by a processor. The memory unit may be located inside or outside the processor, and may exchange data with the processor through various known means.

Detailed descriptions of the preferred embodiments of the present invention disclosed as described above are provided to enable those skilled in the art to implement and implement the present invention. Although the above has been described with reference to a preferred embodiment of the present invention, those skilled in the art will variously modify and change the present invention within the scope not departing from the spirit and scope of the present invention described in the following claims. You will understand that you can do it. Thus, the invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. In addition, although the preferred embodiments of the present specification have been illustrated and described above, the present specification is not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the present specification claimed in the claims. In addition, various modifications may be possible by those of ordinary skill in the art, and these modifications should not be individually understood from the technical spirit or prospect of the present specification.

In addition, in this specification, both the product invention and the method invention are described, and the description of both inventions may be supplementally applied if necessary.

1200: device
1210: processor
1220: memory

Claims (16)

In the method of performing an infant and toddler life pattern problem analysis,
Receiving input information from a user and quantifying the received input information;
Performing data analysis based on the digitized input information;
Applying the analyzed data to a first type artificial intelligence system to derive a problem; And
Including; providing a solution corresponding to the problem;
The input information includes at least one or more of first type input information, second type input information, and third type input information, which is text input information by a user, which is basic information on infants and toddlers,
When the second type input information is applied to the first type artificial intelligence system, the plurality of second type input information is classified into a plurality of combinations through the first type artificial intelligence system based on a neural network analysis method. Is derived, and the problem is derived based on the derived information,
Receiving user feedback information on the problem and the solution corresponding to the problem; And
Further comprising applying a second type artificial intelligence system to the problem and the solution corresponding to the problem based on the user feedback information, and performing relearning on the problem and the solution corresponding to the problem; and,
User feedback information for the solution is evaluation grade information for the problem and the solution corresponding to the problem,
The second type artificial intelligence system determines whether to maintain the problem and the solution corresponding to the problem based on the evaluation grade information,
When the solution corresponding to the problem is determined to be below a preset level based on the second type artificial intelligence system, the problem and the solution corresponding to the problem are deleted.
delete delete The method of claim 1,
The second type artificial intelligence system derives a new problem by using at least one of the first type input information, second type input information, and third type input information,
In the case of obtaining solution information for the newly derived problem using the learning information of the second type artificial intelligence system, the newly derived problem and a solution corresponding thereto are added.
The method of claim 1,
When the first type artificial intelligence system is applied, the information classified into the plurality of combinations derived based on the plurality of second type input information, the first type input information, and the third type input information are further considered. Thus, the problem is derived, infant life pattern problem analysis method.
The method of claim 5,
When the first type artificial intelligence system is applied, the first type input information, information classified into the plurality of combinations derived based on the plurality of second type input information, and a weight for the third type input information Decide,
A method of analyzing a problem of a life pattern of infants and young children to derive a problem based on the determined weight.
The method of claim 1,
The first type input information is, for infants 12 months of age or older, the gender, age, height, weight, head position, gestational age, sibling relationship, baby food status, baby food information, health information, sleep and sleep education information, Including at least one or more of parenting environment, baby and mother's temperament, and sleep information,
In the case of infants and toddlers under 12 months of age, the feeding method, feeding term, and number of nights are further included in the first type input information,
The second type input information is information determined based on at least one of statistics target information, time classification information, statistical item information, and additional information information for the infant,
The statistical target information is any one of lactation and sleep,
The time classification information is any one of day, night, and total,
The statistical item information is any one of quantity, number, term, and regularity,
The information classified into the plurality of combinations derived based on the plurality of second type input information is determined by further considering the combination of the statistics object information, the time classification information, and the statistical item information and the additional information, ,
The third type of input information is specific information related to the second type of input information.
The method of claim 1,
The problem and the solution corresponding to the problem is provided to a user through at least one of a smart device, a smart phone, and a tablet.
In the device for analyzing the problem of infant and toddler life patterns,
A processor that performs an analysis of a problem of an infant's life pattern; And
Including; a memory for storing the problem analysis information of the infant life pattern,
The processor,
Receive input information from the user, quantify the received input information,
Perform data analysis based on the digitized input information,
Applying the analyzed data to a first type artificial intelligence system to derive a problem, and
Provide a solution corresponding to the above problem,
The input information includes at least one or more of first type input information, second type input information, and third type input information, which is text input information by a user, which is basic information on infants and toddlers,
When the second type input information is applied to the first type artificial intelligence system, the plurality of second type input information is classified into a plurality of combinations through the first type artificial intelligence system based on a neural network analysis method. Is derived, and the problem is derived based on the derived information,
The processor,
Receiving user feedback information on the problem and the solution corresponding to the problem,
Applying a second type artificial intelligence system to the problem and the solution corresponding to the problem based on the user feedback information, and relearning the problem and the solution corresponding to the problem,
User feedback information for the solution is evaluation grade information for the problem and the solution corresponding to the problem,
The second type artificial intelligence system determines whether to maintain the problem and the solution corresponding to the problem based on the evaluation grade information,
When the solution corresponding to the problem is determined to be below a preset level based on the second type artificial intelligence system, the problem and the solution corresponding to the problem are deleted. .
delete delete The method of claim 9,
The second type artificial intelligence system derives a new problem by using at least one of the first type input information, second type input information, and third type input information,
When obtaining solution information for the newly derived problem by using the learning information of the second type artificial intelligence system, an apparatus for performing infant life pattern problem analysis by adding the newly derived problem and a solution corresponding thereto .
The method of claim 9,
When the first type artificial intelligence system is applied, the information classified into the plurality of combinations derived based on the plurality of second type input information, the first type input information, and the third type input information are further included. In consideration of the problem is derived, an apparatus for performing a problem analysis of infant life pattern.
The method of claim 13,
When the first type artificial intelligence system is applied, the information classified into the plurality of combinations derived based on the first type input information, the plurality of second type input information, and the third type input information Determine the weights,
An apparatus for performing a problem analysis of an infant's life pattern, which derives a problem based on the determined weight.
The method of claim 9,
The first type input information is, for infants 12 months of age or older, the gender, age, height, weight, head position, gestational age, sibling relationship, baby food status, baby food information, health information, sleep and sleep education information, Including at least one or more of parenting environment, baby and mother's temperament, and sleep information,
In the case of infants and toddlers under 12 months of age, the feeding method, feeding term, and number of nights are further included in the first type input information,
The second type input information is information determined based on at least one of statistics target information, time classification information, statistical item information, and additional information information for the infant,
The statistical target information is any one of lactation and sleep,
The time classification information is any one of day, night, and total,
The statistical item information is any one of quantity, number, term, and regularity,
The information classified into the plurality of combinations derived based on the plurality of second type input information is determined by further considering the combination of the statistics object information, the time classification information, and the statistical item information and the additional information, ,
The third type input information is specific information related to the second type input information.
The method of claim 9,
The problem and the solution corresponding to the problem are provided to a user through at least one of a smart device, a smart phone, and a tablet.

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