METHOD AND DEVICE FOR PROVIDING MENSTRUAL RELATED INFORMATION

Abstract

According to various embodiments of the present disclosure, a method for providing menstruation-related information, performed by a computing device, is disclosed, which may include: acquiring physiological information including a predetermined plurality of electrocardiogram variable information, the plurality of electrocardiogram variable information including time series data; acquiring user questionnaire information corresponding to the plurality of electrocardiogram variable information; and generating menstrual cycle prediction information based on the physiological information and the user questionnaire information using an analytical model.

Description

[Technical Field]

[0001] The present invention relates to a method and apparatus for providing information using a computer, and more particularly to a method and apparatus for providing menstruation-related information using biometric information including a plurality of electrocardiogram variable information and user questionnaire information. [Background technology]

[0002] A commonly known method for predicting the menstrual cycle predicts the day of ovulation (menstrual period) based solely on a woman's body temperature, but the accuracy of the measurements is not high. Therefore, due to the inaccurate prediction results, there is a problem in that accurate menstrual-related information, such as information on fertile periods and information on preventing and alleviating menstrual disorders, cannot be provided. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Korean Patent Publication No. 10-2020-0026340 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure has been devised in response to the above-mentioned background art, and provides a method and apparatus for providing menstruation-related information using biometric information including a plurality of electrocardiogram variable information and user questionnaire information. [Means for solving the problem]

[0005] To address the above-mentioned challenges, a method for providing menstruation-related information, executed by a computing device, is disclosed. The method may include: acquiring biological information including a predetermined number of electrocardiogram variable information, the predetermined number of electrocardiogram variable information including time series data; acquiring user question-and-answer information corresponding to the predetermined number of electrocardiogram variable information; and generating menstrual cycle prediction information based on the biological information and the user question-and-answer information using an analytical model. Alternatively, the plurality of electrocardiogram variable information may include at least one of RRI (RR Intervals) information, HR (Heart Rate) information, RR (Respiration Rate) information, ST level information, SDNN (standard deviation of all NN intervals) information, RMSSD (square root of the mean of the sum of the squares of differences between adjacent NN intervals), NN50 (number of NN intervals differing by more than 50 ms) information, pNN50 (ratio of NN50) information, and SDSD (standard deviation of differences between adjacent NN intervals) information.

[0006] Alternatively, the user question and answer information may include at least one of questions and answers related to sleep disorders, questions and answers related to depression, questions and answers related to anxiety, or questions and answers related to stress. Alternatively, the analytical model may include an ensemble learning model that uses a combination of multiple predictor variables, each of which includes at least a portion of the plurality of electrocardiogram variable information and the user question-and-answer information.

[0007] Alternatively, the analytical model can be trained to generate information predicting the transition from the follicular phase to the luteal phase if it identifies a decreasing trend in the RRI information based on the RRI information. Alternatively, the analytical model can be trained to generate information predicting a transition from the menstrual phase to the follicular phase when it identifies a trend of sustained decline in HR information, to generate information predicting a transition from the follicular phase to the fertile phase when it identifies a trend of increasing HR information, and to generate information predicting a transition to the luteal phase when it identifies a trend of peak levels in HR information.

[0008] Alternatively, the analytical model can be trained to generate information predicting a transition from the menstrual phase to the follicular phase when it identifies a decreasing trend in the RR information, to generate information predicting a transition to the fertile phase when it identifies a further decreasing trend in the RR information, and to generate information predicting a transition to the luteal phase when it identifies an increasing trend in the RR information. Alternatively, the analytical model can be trained to generate information predicting transition to the follicular phase when it identifies a trend in the maximum level of the ST level information, and to generate information predicting transition to the luteal phase when it identifies a trend in the minimum level of the ST level information.

[0009] Alternatively, the analytical model can be trained to generate information predicting a transition from the menstrual phase to the follicular phase when it identifies a maintaining trend in the SDNN information, to generate information predicting a transition from the follicular phase to the fertile phase when it identifies a decreasing trend in the SDNN information, and to generate information predicting a transition to the luteal phase when it identifies a maximum decreasing trend in the SDNN information. Alternatively, the analytical model can be trained to generate information predicting entry into the luteal phase when it identifies the greatest decreasing trend in the RMSSD information.

[0010] Alternatively, the analytical model can be trained to generate information predicting the transition from the follicular phase to the luteal phase if it identifies a decreasing trend in the NN50 information. Alternatively, the analytical model can be trained to generate information predicting the transition from the follicular phase to the luteal phase if it identifies a decreasing trend in the pNN50 information.

[0011] Alternatively, the analytical model can be trained to generate information predicting the transition from menstrual phase to follicular phase if it identifies a sustained trend in the SDSD information. Alternatively, the user question and answer information may include question and answer information related to sleep disorders, and the analytical model may be trained to generate information predicting transition to the luteal phase when it identifies an increasing trend in the question and answer information related to sleep disorders.

[0012] Alternatively, the user question and answer information may include question and answer information related to depression, and the analytical model may be trained to generate information predicting a transition to the luteal phase if it identifies an increasing trend in the question and answer information related to depression. Alternatively, the user question and answer information may include question and answer information related to anxiety, and the analytical model may be trained to generate information predicting a transition to the luteal phase when it identifies an increasing trend in the question and answer information related to anxiety.

[0013] Alternatively, the user question and answer information may include stress-related question and answer information, and the analytical model may be trained to generate information predicting delayed ovulation if it identifies an increasing trend in the stress-related question and answer information. Alternatively, the step of acquiring user question and answer information corresponding to the plurality of electrocardiogram variable information may include the step of: providing a user interface corresponding to the time when the plurality of electrocardiogram variable information is acquired, and enabling the user question and answer information to be input.

[0014] Alternatively, the step of acquiring user question and answer information corresponding to the plurality of electrocardiogram variable information may include the step of: providing a user interface indicating whether or not the user question and answer information corresponding to the time when the plurality of electrocardiogram variable information is acquired has been entered. Alternatively, the plurality of electrocardiogram variable information may include RRI information, HR information, RR information, ST level information, SDNN information, RMSSD information, NN50 information, pNN50 information, and SDSD information.

[0015] Alternatively, the user question and answer information may include question and answer information related to sleep disorders, question and answer information related to depression, question and answer information related to anxiety, and question and answer information related to stress. Alternatively, the analytical model can use a random forest algorithm.

[0016] To solve the above-mentioned problems, a computer program stored on a computer-readable medium is disclosed. The computer program includes instructions for causing one or more processors to execute a method for providing menstruation-related information, the method may include: acquiring biological information including a predetermined plurality of electrocardiogram variable information, the plurality of electrocardiogram variable information including time series data; acquiring user question-and-answer information corresponding to the plurality of electrocardiogram variable information; and generating menstrual cycle prediction information based on the biological information and the plurality of user question-and-answer information using an analytical model. To solve the above-mentioned problems, a computing device for performing a method for providing menstruation-related information is disclosed. The computing device includes: a memory including computer-executable components; and a processor for executing the following computer-executable components stored in the memory, wherein the processor is capable of acquiring biological information including a predetermined plurality of electrocardiogram variable information, the plurality of electrocardiogram variable information including time series data; acquiring user question-and-answer information corresponding to the plurality of electrocardiogram variable information; and generating menstrual cycle prediction information based on the biological information and the plurality of user question-and-answer information using an analytical model. [Effects of the Invention]

[0017] The present disclosure can provide a method and apparatus for providing menstruation-related information using biometric information, including multiple electrocardiogram variable information, and user questionnaire information. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a diagram illustrating a menstruation-related information providing system according to several embodiments of the present disclosure. [Figure 2] FIG. 1 is a block diagram of a computing device for performing a method for providing menstruation-related information, according to embodiments of the present disclosure. [Figure 3] FIG. 1 is a schematic diagram illustrating a method for providing menstruation-related information according to embodiments of the present disclosure. [Figure 4] 1 is a diagram illustrating electrocardiogram variable information according to several embodiments of the present disclosure. [Figure 5] 1 is a diagram illustrating user question and answer information according to several embodiments of the present disclosure. [Figure 6] 1 is a diagram illustrating an example of a user application for obtaining user question and answer information, according to embodiments of the present disclosure. [Figure 7] 1 is a diagram illustrating the predictive performance of an analytical model, according to several embodiments of the present disclosure. [Figure 8] 10 is another diagram illustrating the predictive performance of an analytical model, according to embodiments of the present disclosure. [Figure 9] 1 is a flowchart of a method for providing menstruation-related information according to embodiments of the present disclosure. [Figure 10] FIG. 2 is a schematic diagram illustrating network functions according to embodiments of the present disclosure. [Figure 11] FIG. 1 is a block diagram of a computing device according to embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0019] Various embodiments are described below with reference to the drawings. Various descriptions are provided herein to facilitate understanding of the present disclosure. However, it is apparent that such embodiments can be practiced without such specific descriptions. As used herein, terms such as "component," "module," and "system" refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or the execution of software. For example, a component can be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a processor and / or thread of execution. A component can be localized within one computer. A component can be distributed across two or more computers. Such components can also execute on various computer-readable media having various data structures stored therein. Components can communicate via local and / or remote processes, for example, using signals containing one or more data packets (e.g., data and / or signals from one component interacting with other components in a local or distributed system, or data transmitted over a network such as the Internet to other systems).

[0020] Additionally, the terms "or" and "or" are intended to mean the inclusive "or," rather than the exclusive "or." That is, unless otherwise specified or clear from the context, "X utilizes A or B" is intended to mean one of the natural inclusive permutations. That is, if X utilizes A; X utilizes B; or X utilizes both A and B, then "X utilizes A or (or) B" can apply to any of these. Additionally, the term "and / or" as used herein refers to and includes all possible combinations of one or more of the associated listed items. Additionally, the predicate "comprises" and / or the modifier "comprises" should be understood to mean the presence of the feature and / or component in question. However, the predicate "comprises" and / or the modifier "comprises" should be understood not to exclude the presence or addition of one or more other further features, components and / or groups thereof. Additionally, unless a specific number is specified or the context is clear that a singular form is indicated, the singular form in this specification and claims should generally be construed to mean "one or more."

[0021] Furthermore, the term "at least one of A or B" should be interpreted as meaning "when only A is included," "when only B is included," or "when a combination of A and B is included." Those skilled in the art should further appreciate that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in accordance with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the various illustrative components, blocks, components, means, logic, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints of the overall system. Skilled artisans may implement the described functionality in a variety of ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0022] The description of the embodiments set forth herein is provided to enable one of ordinary skill in the art to utilize or practice the present invention. Various modifications to these embodiments will be apparent to those of ordinary skill in the art. The generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present invention is not limited to the embodiments set forth herein. The present invention is to be accorded the broadest scope consistent with the principles and novel features disclosed herein. The present disclosure is not limited to body temperature, which has traditionally been used primarily as a variable for predicting the menstrual cycle, but can provide a method for providing highly accurate menstruation-related information by using electrocardiogram variable information and user question-and-answer information. Specifically, the present disclosure can provide menstruation-related information by identifying patterns of time-series changes in electrocardiogram variable information, which is time-series data collected continuously over a certain period of time. Furthermore, the present disclosure can provide more accurate menstruation-related information by using user question-and-answer information obtained from the user in a question-and-answer format together with electrocardiogram variable information.

[0023] FIG. 1 is a diagram illustrating a menstruation-related information providing system (1000) according to several embodiments of the present disclosure. Referring to Fig. 1, in several embodiments of the present disclosure, a menstruation-related information providing system (1000) may include a user terminal (1010), a biometric information sensing device (1020), and a server (1030). The configuration of the menstruation-related information providing system (1000) shown in Fig. 1 is merely a simplified example. In several embodiments of the present disclosure, other components may be added to the menstruation-related information providing system (1000), and the menstruation-related information providing system (1000) may also be configured using only a portion of the disclosed components.

[0024] Communication between various entities included in the menstrual-related information provision system 1000 can be performed via a wired / wireless network 1040. Here, the wired / wireless network 1040 can refer to a connection structure that allows information exchange between nodes, such as multiple terminals and servers. Examples of such a network 1040 can include a local area network (LAN), a wide area network (WAN), the Internet (WWW), wired and wireless data communication networks, telephone communication networks, wired and wireless television communication networks, etc. Some examples of wireless data communication networks include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth (registered trademark) networks, NFC (Near-Field Communication) networks, satellite broadcasting networks, analog broadcasting networks, DMB (Digital Multimedia Broadcasting) networks, etc. The user terminal 1010 may be embodied as a computer that can connect to a remote server or terminal via a network. Here, the user terminal 1010 may include at least one of a smartphone, a tablet personal computer (PC), a mobile phone, a video phone, an e-book reader, a desktop personal computer (PC), a laptop personal computer, a netbook computer, a workstation, a server, a personal digital assistant (PDA), a portable multimedia player (PMP), or a wearable device (e.g., smart glasses, a head-mounted device (HMD), smart wear, an electronic bracelet, an electronic necklace, an electronic appcessory, a smart mirror, or a smart watch). However, the user terminal 1010 may be embodied as various computers, without being limited thereto.

[0025] According to several embodiments of the present disclosure, a user terminal (1010) may implement the method for providing menstruation-related information of the present disclosure. For example, the user terminal (1010) may provide a user application for providing menstruation-related information. The user terminal (1010) may acquire menstruation-related user information necessary for providing the menstruation-related information through the user application. In several examples, the user terminal (1010) may acquire biometric information by operating the biometric information sensing device (1020) using the user application. In several examples, the user terminal (1010) may acquire biometric information stored on the server (1030). In several examples, the user terminal (1010) may acquire user question and answer information input through a user interface of the user application. In some examples, the user terminal (1010) may generate menstruation-related user information using an analytical model implemented in the user terminal (1010). The user terminal (1010) may also transmit the menstruation-related user information to the server (1030) for processing the acquired data. In this case, the user terminal (1010) may receive the menstruation-related information generated by processing the menstruation-related user information using the analytical model provided by the server (1030). The user terminal (1010) may provide the received menstruation-related information to the user via a user application.

[0026] The menstruation-related information may include various information related to menstruation. For example, the menstruation-related information may include predicted menstrual cycles (ovulation date, menstrual date, menstrual period, fertile days, premenstrual syndrome, menopause, etc.), recommended diets, guide information for menstrual management (for example, predicted menstrual disorders and music therapy or exercise therapy for alleviating menstrual disorders), or information related to recommended menstruation-related products and services. The types of menstruation-related information are not limited to the examples described above, and may include various information related to menstruation. In some embodiments of the present disclosure, the server (1030) may implement a method for providing menstruation-related information according to the present disclosure. For example, the server (1030) may train an analytical model using training data related to menstruation. In some examples, the training data related to the menstrual cycle may include various information, including biometric information and user question and answer information, as user information related to menstruation. The server (1030) may generate menstruation-related information by processing the user information related to menstruation using the trained analytical model. As another example, the server (1030) may provide a user application including the trained analytical model to the user terminal (1010). In this case, as described above, the user terminal (1010) may process the user information related to menstruation using the analytical model.

[0027] The biometric sensing device 1020 may include various devices capable of measuring biometric information. For example, the biometric sensing device 1020 may be a device capable of measuring a user's body temperature, blood pressure, pulse rate, skin conductance, electrocardiogram, oxygen saturation, respiratory rate, etc. The biometric sensing device 1020 may transmit the measured biometric information to the user terminal 1010 or the server 1030. The biometric sensing device 1020 may include a portable device or a stationary device. In some examples, the biometric sensing device 1020 may include a wearable device (e.g., a Holter patch) capable of measuring an electrocardiogram. In some examples, the biometric sensing device 1020 may be provided as a module on the user terminal 1010. In the following, a computer device that executes the method for providing menstruation-related information of the present disclosure, which is a user terminal (1010) or a server (1030), will be described according to several embodiments of the present disclosure.

[0028] FIG. 2 is a block diagram of a computing device for performing a method for providing menstruation-related information according to embodiments of the present disclosure. As shown in Figure 2, the computing device (100) may include a processor (110), a memory (130), and a network unit (120). The configuration of the computing device (100) shown in Figure 2 is merely a simplified example. In various embodiments of the present disclosure, the computing device (100) may include other components for implementing the computing environment of the computing device (100), and the computing device (100) may be configured with only some of the disclosed components.

[0029] The processor 110 may be configured with one or more cores and may include a processor for data analysis and processing, deep learning, such as a central processing unit (CPU) of a computing device, a general-purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU). The processor 110 may read a computer program stored in the memory 130 and perform data conversion, calculation, generation, etc. to provide menstruation-related information in various embodiments of the present disclosure. For example, the processor 110 may execute steps to execute a method for providing menstruation-related information described below. In addition, according to various embodiments of the present disclosure, the processor 110 may execute calculations for training a neural network using training data to execute the method for providing menstruation-related information. For example, the processor 110 may generate / train an analytical model using the training data. The processor 110 is capable of performing calculations for neural network training, such as processing input data for deep learning (DL), extracting feature values ​​from input data, calculating errors, and updating weights in a neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor 110 is capable of processing calculations related to a method for providing menstruation-related information. For example, both the CPU and the GPGPU can process calculations related to the method for providing menstruation-related information. In addition, in several embodiments of the present disclosure, processors of several computing devices can be used together to process data conversion, calculation, generation, network function training, and data classification using network functions related to the method for providing menstruation-related information. In addition, the programs executed in the computing devices in several embodiments of the present disclosure can be programs executable by the GPU, GPGPU, and TPU. In one embodiment of the present disclosure, the memory 130 can store any type of information generated or determined by the processor 110 and any type of information received by the network unit 120. For example, the memory 130 can store data generated during the process of the processor 110 executing the method for providing menstruation-related information. The memory 130 can also store data received from an external device during the process of the processor 110 executing the method for providing menstruation-related information. However, the memory 130 is not limited thereto, and can store any information required to execute the method for providing menstruation-related information according to the embodiments of the present disclosure.

[0030] In various embodiments of the present disclosure, the memory 130 may include at least one type of storage medium, such as a flash memory, a hard disk, a micro multimedia card, a card-type memory (e.g., SD or XD memory), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, or an optical disk. The computing device 100 may also operate in conjunction with web storage that performs the storage function of the memory 130 over the Internet. The foregoing description of memory is merely exemplary, and the present disclosure is not limited thereto. In several embodiments of the present disclosure, the network unit 120 can use any known wired or wireless communication system.

[0031] The network unit 120 can transmit and receive information processed by the processor 110, a user interface, and the like, through communication with other terminals. For example, the network unit 120 can provide a user interface generated by the processor 110 to a client (e.g., a user terminal). The network unit 120 can also receive external input from a user to the client and transfer it to the processor 110. In this case, the processor 110 can process operations such as output, correction, modification, and addition of information provided through the user interface based on the external user input received from the network unit 120. Specifically, the network unit 120 can transmit and receive various information for executing the method for providing menstruation-related information according to the embodiments of the present disclosure. For example, the network unit 120 can transmit and receive biometric information and user question and answer information stored in a database. The network unit 120 can also transmit a plurality of data generated in the process of executing the method for providing menstruation-related information described below to an external device for storage in a database.

[0032] Meanwhile, a computing device (100) according to several embodiments of the present disclosure may include a server (1030) as a computing system that transmits and receives information through communication with a client. In this case, the client may be any type of terminal that can access the server. For example, the server computing device (100) may receive a query from a user terminal and generate a single information processing result corresponding to the query. In this case, the server computing device (100) may provide a user interface including the processing result to the user terminal. In this case, the user terminal may output the user interface received from the server computing device (100) and receive and process information input through user interaction. In additional embodiments, the computing device (100) may include any form of user terminal (1010) that receives data resources generated at any server and performs additional information processing.

[0033] Figure 3 is a schematic diagram illustrating a method for providing menstruation-related information according to embodiments of the present disclosure. Figure 4 is a diagram illustrating electrocardiogram variable information according to embodiments of the present disclosure. Figure 5 is a diagram illustrating user question and answer information according to embodiments of the present disclosure. Figure 6 is a diagram illustrating an example of a user application for obtaining user question and answer information according to embodiments of the present disclosure. 3 to 6, exemplary embodiments according to several embodiments of the present disclosure will be specifically described.

[0034] The processor (110) may acquire physiological information including a predetermined plurality of electrocardiogram variable information, where the plurality of electrocardiogram variable information may include time series data. Specifically, the processor 110 can acquire biometric information 10 that can be used to predict a menstrual cycle. For example, the biometric information can include age, height, weight, body temperature, blood pressure, respiratory rate, menstrual date, menstrual period, pulse rate, skin conductivity, electrocardiogram, and oxygen saturation. However, the biometric information 10 is not limited to the above examples and can include various information related to the user.

[0035] In some examples, the biometric information 10 may include information generated by one or more biometric sensing devices 1020 (e.g., body temperature, blood pressure, respiratory rate, pulse, electrocardiogram, etc.), and may also include information input by a user (e.g., age, height, weight, etc.). In some examples, the biological information 10 may include electrocardiogram variable information. For example, the electrocardiogram variable information may be time series data of an electrocardiogram (EKG or ECG) measured by an electrocardiogram device such as a Holter monitor. The electrocardiogram variable information may be time series data obtained by a method of continuously measuring an electrocardiogram, rather than cross-sectional measurements.

[0036] According to several embodiments of the present disclosure, the plurality of electrocardiogram variable information may include at least one of RRI (RR Intervals) information, HR (Heart Rate) information, RR (Respiration Rate) information, ST level information, SDNN (standard deviation of all NN intervals) information, RMSSD (square root of the mean of the sum of the squares of differences between adjacent NN intervals) information, NN50 (number of NN intervals differing by more than 50 ms) information, pNN50 (ratio of NN50) information, and SDSD (standard deviation of differences between adjacent NN intervals) information. 4 shows an example of electrocardiogram variable information acquired by Holter electrocardiography. Among these, predictors processed by the analysis model to generate menstrual cycle prediction information may include RRI information, HR information, RR information, ST level information, SDNN information, RMSSD information, NN50 information, pNN50 information, and SDSD information, which have been analyzed to have a high correlation with menstrual cycle prediction. The electrocardiogram variable information is not limited to the above example and may include various information related to electrocardiograms.

[0037] The plurality of electrocardiogram variable information may be information acquired according to a predetermined cycle. For example, the plurality of electrocardiogram variable information may be information measured in the morning and afternoon every day. In this case, the analysis model may generate menstrual cycle prediction information for each predetermined cycle, thereby improving the accuracy of the prediction. However, the plurality of electrocardiogram variable information may be information acquired at various cycles. The processor (110) is capable of obtaining user question and answer information corresponding to the plurality of electrocardiogram variable information.

[0038] Specifically, the processor 110 may receive a plurality of electrocardiogram variable information and user questionnaire information that is processed by the analytical model. The user questionnaire information may include information related to menstrual symptoms. For example, the user questionnaire information may be information input by the user regarding physical changes, mental changes, changes in menstrual cramps, breast pain, or headaches. In several embodiments of the present disclosure, the user question and answer information may include at least one of question and answer information related to sleep disorders, question and answer information related to depression, question and answer information related to anxiety, and question and answer information related to stress.

[0039] FIG. 5 shows an example of user question and answer information. As shown in FIG. 5, the user question and answer information may be information entered in a categorical or numerical format based on various items related to physical changes, mental changes, and changes in menstrual pain, breast pain, and headaches. Among these, the user question and answer information processed by the analysis model to generate menstrual cycle prediction information may include question and answer information related to sleep disorders, depression, and anxiety, which have been analyzed to have a high correlation with menstrual cycle prediction. However, the user question and answer information is not limited to these, and may include various information related to menstrual symptoms obtained from the user. In an embodiment of the present disclosure, when acquiring user question and answer information corresponding to multiple electrocardiogram variable information, the processor (110) can provide a user interface that allows input of multiple user question and answer information corresponding to the time when the multiple electrocardiogram variable information is acquired.

[0040] As described above, the processor 110 can provide a user interface that allows user questions and answers to be input via a user application. Generally, to reduce the amount of data processing, an analytical model generates prediction results using information acquired at several points in time rather than information acquired 24 hours a day. Therefore, electrocardiogram variable information and user questions and answers must be processed together with information acquired during the same period. For example, multiple electrocardiogram variable information measured this morning can be linked to user questions and answers entered about the user's menstrual symptoms this morning. The analytical model can then process the matched multiple electrocardiogram variable information and user questions and answers together to generate menstrual cycle prediction information. Therefore, the user questions and answers can be acquired in accordance with the cycle in which the multiple electrocardiogram variable information is acquired so that they can be processed together. For example, if multiple electrocardiogram variable information is acquired every morning and afternoon, the processor 110 can provide a user interface that allows user questions and answers to be entered every morning and afternoon. In this case, the processor 110 may provide a user interface that allows a user to input user questions and answers along a predetermined cycle in which a plurality of pieces of electrocardiogram variable information is acquired. FIG. 6 shows an example of a user interface that allows a user to input user questions and answers in a user application provided by the user terminal 1010. In some examples, if the cycle in which a plurality of pieces of electrocardiogram variable information is acquired is two times a day, once in the morning and once in the afternoon, the user interface may display whether user questions and answers have been entered for each morning and afternoon on each date on the calendar. When the user interface receives an input to select a date for which user questions and answers have not been entered, it may provide an input window that allows user questions and answers to be entered for that date. Referring to FIG. 6, when the user interface recognizes a user instruction to select a specific date on the calendar, it may display whether user questions and answers have been completed using check marks for the morning and afternoon on that date.

[0041] In some examples, the user interface may provide a display that allows the user to identify how many pieces of user question and answer information have been entered. For example, referring to FIG. 6, the user interface may display an icon indicating the number of pieces of user question and answer information that have been entered for each date on a calendar. However, the user interface may be provided in various forms. According to some embodiments of the present disclosure, the processor (110) can generate menstrual cycle prediction information (30) based on the biometric information (10) and the user questionnaire information (20) using an analytical model.

[0042] The analysis model (200) can generate menstrual cycle prediction information by processing biological information, including multiple electrocardiogram variable information, and multiple user question and answer information. The menstrual cycle can be formed by a cycle of repeating a luteal phase, which is the period before menstruation begins, a menstrual phase, which is the period during menstruation, and a follicular phase, which is the period after menstruation ends. The fertile phase can refer to the period around the time of reproductive "ovulation," which occurs approximately 14 days after menstruation. The analysis model can generate menstrual cycle prediction information (30) that can identify the luteal phase, menstrual phase, follicular phase, and fertile phase by processing input data, including biological information and user question and answer information. The processor 110 can generate menstruation-related information using the generated menstrual cycle prediction information 30. For example, the processor 110 can use the menstrual cycle prediction information 30 to provide, at an appropriate time, information on predicted menstrual cycles (such as ovulation dates, menstrual dates, menstrual periods, fertile days, premenstrual syndrome, and menopause), recommended diets, guide information for menstrual management (such as predicted menstrual disorders and music therapy or exercise therapy to alleviate menstrual disorders), or information on recommended menstruation-related products and services. However, without being limited thereto, the processor 110 can provide various menstruation-related information using the menstrual cycle prediction information 30.

[0043] In accordance with several embodiments of the present disclosure, the analytical model (200) may include an ensemble learning model that uses a combination of several predictor variables, including several electrocardiogram variable information and at least a portion of the user question and answer information. Specifically, because the biometric information 10, including multiple electrocardiogram variable information, and the user question-and-answer information 20 are multiple predictor variables as described above, the analytical model must be implemented using an appropriate algorithm capable of processing multiple predictor variables as input. For example, the analytical model 200 may be an ensemble learning model that derives more accurate predictions by integrating the prediction results of multiple submodels 210 through a prediction information determination module 220. In some examples, the ensemble learning model may be a model that uses a voting, bagging, or boosting algorithm using multiple submodels. Hereinafter, an exemplary analytical model based on some embodiments of the present disclosure will be described as a model implemented using a random forest algorithm, which is a type of bagging algorithm, but the present disclosure is not limited thereto. In the following, an example will be described in which the combination of predictive variables of the analytical model includes only electrocardiogram variable information from the biological information (10), but the combination of predictive variables of the analytical model can also include various biological information (10) other than electrocardiogram variable information, such as respiratory rate and body temperature.

[0044] The random forest algorithm may be an ensemble method that applies training data to multiple decision trees to improve learning performance. For example, the analysis model (200) may include n submodels (210a to 210n) trained with n training data generated by sampling with replacement from the original training data. In this case, each submodel may be trained using a combination of predictor variables that combine only a portion of multiple predictor variables. For example, the first submodel (210a) may be a model that uses a combination of predictor variables including RRI information, HR information, and RR information from multiple electrocardiogram variable information. The second submodel (210b) may be a model that uses a combination of predictor variables including RRI information, ST level information, and user question-and-answer information related to sleep disorders from multiple electrocardiogram variable information. The third submodel (210c) may be a model that uses a combination of predictor variables including NN50 information from multiple electrocardiogram variable information and user question-and-answer information related to sleep disorders and anxiety from user question-and-answer information. Here, the combination of predictor variables used by each sub-model can be a randomly determined combination or a predetermined combination. Among the training data for training the analytical model, training data related to electrocardiogram variable information can be generated by assigning labels related to the menstrual cycle to change patterns identified from electrocardiogram variable information, which is time-series data. Table 1 below is a graph analyzing change patterns identified from RRI information to assign information related to the menstrual cycle.

[0045] [Table 1]

[0046] In some examples, the training data may include labels generated by analyzing correlations between patterns of change identified from each of the plurality of electrocardiogram variable information and the menstrual cycle. Hereinafter, an example of an analytical model (200) generated using training data generated based on the results of analyzing correlations between the plurality of electrocardiogram variable information and the menstrual cycle, according to some embodiments of the present disclosure, will be described. Hereinafter, when the analytical model (200) is an ensemble learning model, training of the analytical model (200) may refer to training of individual submodels (210). According to various embodiments of the present disclosure, for RRI information, the analytical model 200 can be trained to generate information predicting a transition from the follicular phase to the luteal phase if a decreasing trend in the RRI information is identified based on the RRI information. For example, the decreasing trend in the RRI information can include a decrease of approximately 0.15 to 0.40 Hz during the luteal phase compared to the follicular phase.

[0047] According to various embodiments of the present disclosure, for HR information, the analytical model (200) can be trained to generate information predicting a transition from the menstrual phase to the follicular phase if it identifies a trend of sustained decrease in the HR information. For example, a trend of sustained decrease in the HR information can include a decrease of approximately 1.36 bpm or close to the same value in the follicular phase compared to the menstrual phase. Additionally, the analytical model 200 can be trained to generate information predicting a transition from the follicular phase to the fertile phase when an increasing trend in the HR information is identified. For example, the increasing trend in the HR information can include an increase of approximately 0.61 bpm in the fertile phase compared to the follicular phase.

[0048] Additionally, the analytical model (200) can be trained to generate information predicting a transition to the luteal phase when it identifies trends in the highest levels of HR information, which can include an increase of approximately 2.33 bpm in the luteal phase compared to the ovulatory phase, an increase of approximately 1.84 bpm in the luteal phase compared to the fertile phase, and an increase of approximately 2.31 bpm in the luteal phase compared to the menstrual phase. According to various embodiments of the present disclosure, for RR information, the analytical model (200) can be trained to generate information predicting a transition from the menstrual phase to the follicular phase if it identifies a decreasing trend in the RR information. For example, the decreasing trend in the RR information can include a decrease of approximately 0.29 breaths / min in the follicular phase compared to the menstrual phase.

[0049] Additionally, the analytical model 200 can be trained to generate information predicting a transition to the fertile phase when it identifies a further decreasing trend in the RR information, such as a decrease of approximately 0.46 breaths / min during the fertile phase compared to the menstrual phase. Additionally, the analytical model 200 can be trained to generate information predicting a transition from menstrual to luteal phase when an increasing trend in the RR information is identified, which can include an increase of approximately 0.18 breaths / min during the luteal phase compared to menstrual phase.

[0050] According to various embodiments of the present disclosure, for ST level information, the analytical model (200) can be trained to generate information predicting transition to the follicular phase when a trend in the maximum level of the ST level information is identified. For example, the trend in the maximum level of the ST level information can include when the ST level information indicates the highest value. Additionally, the analytical model 200 can be trained to generate information predicting the transition to the luteal phase when a trend in the minimum level of the ST level information is identified. For example, the trend in the minimum level of the ST level information can include the lowest value of the ST level information.

[0051] According to several embodiments of the present disclosure, for SDNN information, the analytical model (200) can be trained to generate information predicting the transition from the menstrual phase to the follicular phase if it identifies a maintenance trend in the SDNN information. Additionally, the analytical model 200 can be trained to generate information predicting a transition from the follicular phase to the fertile phase when a decreasing trend in the SDNN information is identified. For example, the decreasing trend in the SDNN information can include a decrease of approximately 0.04 in the fertile phase compared to the follicular phase.

[0052] Additionally, the analytical model 200 can be trained to generate information predicting a transition to the luteal phase when a trend of maximum decrease in the SDNN information is identified. For example, the trend of maximum decrease in the SDNN information can include a case where the SDNN information has the lowest value. According to various embodiments of the present disclosure, for RMSSD information, the analytical model 200 can be trained to generate information predicting a transition to the luteal phase when it identifies a trend of maximum decrease in the RMSSD information. For example, the trend of maximum decrease in the RMSSD information can include when the RMSSD information exhibits the lowest value.

[0053] According to several embodiments of the present disclosure, for NN50 information, the analytical model (200) can be trained to generate information predicting the transition from the follicular phase to the luteal phase if a decreasing trend in the NN50 information is identified. According to several embodiments of the present disclosure, for pNN50 information, the analytical model (200) can be trained to generate information predicting the transition from the follicular phase to the luteal phase if it identifies a decreasing trend in the pNN50 information.

[0054] According to several embodiments of the present disclosure, for SDSD information, the analytical model (200) can be trained to generate information predicting the transition from the menstrual phase to the follicular phase if a maintenance trend in the SDSD information is identified. In several examples, the training data may further include data generated by analyzing the correlation between the change patterns identified from each of the user question-and-answer information and the menstrual cycle. Below, based on several embodiments of the present disclosure, an example of an analytical model (200) generated using training data generated based on the analysis results of the correlation between the user question-and-answer information and the menstrual cycle is described. Hereinafter, when the analytical model (200) is an ensemble learning model, the training of the analytical model (200) may refer to the training of sub-models (210).

[0055] According to several embodiments of the present disclosure, for sleep disorder related question and answer information, the analytical model (200) can be trained to generate information predicting the transition to the luteal phase when an increasing trend of sleep disorder related question and answer information is identified. According to several embodiments of the present disclosure, for question-and-answer information related to depression, the analytical model (200) can be trained to generate information predicting a transition to the luteal phase when an increasing trend of question-and-answer information related to depression is identified.

[0056] According to several embodiments of the present disclosure, for anxiety-related question-and-answer information, the analytical model (200) can be trained to generate information predicting a transition into the luteal phase when an increasing trend in anxiety-related question-and-answer information is identified. According to several embodiments of the present disclosure, for stress-related question-and-answer information, the analytical model (200) can be trained to generate information predicting delayed ovulation if an increasing trend in stress-related question-and-answer information is identified.

[0057] FIG. 7 is a diagram illustrating the predictive performance of an analytical model according to several embodiments of the present disclosure. FIG. 7 shows the predictive performance of an analytical model based on a combination of electrocardiogram variable information and user question-and-answer information. Referring to FIG. 7, it shows that "Random Forest Model 2," implemented using both electrocardiogram variable information and user question-and-answer information, has higher predictive performance than "Random Forest Model 1," implemented using only electrocardiogram variable information. Specifically, the predictive performance (test_ecg) of "Random Forest Model 1" is lower than the predictive performance (test_ecg_cli) of "Random Forest Model 2." Therefore, it can be seen that the predictive performance of the analytical model in the present disclosure is further improved by using both electrocardiogram variable information and user question-and-answer information as predictors.

[0058] FIG. 8 is another diagram illustrating the predictive performance of an analytical model, according to embodiments of the present disclosure. FIG. 8 shows the prediction performance of a combination of sleep problem variables (e.g., questions and answers related to sleep disorders) and mental health variables (e.g., questions and answers related to depression, anxiety, and stress) from the user question-and-answer information. Referring to FIG. 8, the prediction performance result (RandomForest-AUC1) using only the sleep problem variables from the user question-and-answer information together with electrocardiogram variable information is lower than the prediction performance result (RandomForest-AUC2) using the sleep problem variables and mental health variables from the user question-and-answer information together with electrocardiogram variable information. Therefore, it can be seen that the prediction performance of the analytical model according to the present disclosure is further improved by using both the sleep problem variables and mental health variables as predictors from the user question-and-answer information.

[0059] FIG. 9 is a flowchart of a method for providing menstruation-related information according to embodiments of the present disclosure. According to some embodiments of the present disclosure, a method for providing menstruation-related information may include acquiring (s100) physiological information including a predetermined number of electrocardiogram variable information, where the predetermined number of electrocardiogram variable information may include time-series data.

[0060] According to some embodiments of the present disclosure, a method for providing menstruation-related information may include obtaining (s200) user question-and-answer information corresponding to a plurality of electrocardiogram variable information. Alternatively, the step (s200) of acquiring user question and answer information corresponding to the plurality of electrocardiogram variable information may include a step of providing a user interface indicating whether or not user question and answer information corresponding to the time when the plurality of electrocardiogram variable information is acquired has been entered.

[0061] According to several embodiments of the present disclosure, a method for providing menstruation-related information may include generating (s300) menstrual cycle prediction information based on biometric information and user questionnaire information using an analytical model. The steps of the method for providing menstrual-related information described above are presented for illustrative purposes only, and some steps may be omitted, other steps may be added, and the steps may be performed in any order.

[0062] FIG. 9 is a schematic diagram illustrating network functions according to several embodiments of the present disclosure. Throughout this specification, the terms network model, computational model, neural network, network function, and neural network may be used interchangeably. A neural network may be composed of a collection of interconnected computational units generally called nodes. Such nodes may also be referred to as neurons. A neural network is composed of at least one or more nodes. The nodes (or neurons) that make up a neural network may be interconnected by one or more links.

[0063] In a neural network, one or more nodes connected via links can have a relative relationship of an input node and an output node. The concepts of input node and output node are relative, and any node that is an output node for one node can be an input node for another node, and vice versa. As mentioned above, the relationship between input node and output node can be established around links. One or more output nodes can be connected to one input node via links, and vice versa.

[0064] In a relationship between an input node and an output node connected via a link, the value of the data of the output node can be determined based on the data input to the input node. In this case, the link interconnecting the input node and the output node can have a weight. The weight can be variable, and can be changed by a user or an algorithm to perform a function required by the neural network. For example, when one or more input nodes are interconnected to one output node by respective links, the output node can determine the value of the output node based on the value input to the input node connected to the output node and the weight set for the link corresponding to each input node.

[0065] As described above, a neural network has one or more nodes interconnected via one or more links, forming a relationship between an input node and an output node within the neural network. In a neural network, the characteristics of the neural network can be determined by the number of nodes and links, the correlation between the nodes and links, and the weight value assigned to each link. For example, if there are two neural networks that have the same number of nodes and links but different link weight values, the two neural networks can be recognized as different.

[0066] A neural network can be composed of a set of one or more nodes. A subset of the nodes in a neural network can be organized into layers. Some of the nodes in a neural network can be organized into layers based on their distance from a first input node. For example, a set of nodes that are n distances from a first input node can be organized into n layers. The distance from the first input node can be defined based on the minimum number of links that must be traversed to reach that node from the first input node. However, this definition of a layer is arbitrary for the sake of convenience, and the position of a layer in a neural network can be defined differently from the above description. For example, the layer of a node can be defined based on its distance from the final output node.

[0067] A first input node may refer to one or more nodes in a neural network that directly input data without passing through a link to other nodes. Alternatively, a first input node may refer to a node in a neural network that does not have other input nodes connected via links in a link-based node-to-node relationship. Similarly, a final output node may refer to one or more nodes in a neural network that do not have output nodes in a link-based node relationship. Furthermore, a hidden node may refer to a node that is neither a first input node nor a final output node and that constitutes a neural network.

[0068] In one embodiment of the present disclosure, the neural network may be a neural network in which the number of nodes in the input layer is the same as the number of nodes in the output layer, and the number of nodes decreases once and then increases again as the network progresses from the input layer to the hidden layer. In another embodiment of the present disclosure, the neural network may be a neural network in which the number of nodes in the input layer can be fewer than the number of nodes in the output layer, and the number of nodes decreases as the network progresses from the input layer to the hidden layer. In another embodiment of the present disclosure, the neural network may be a neural network in which the number of nodes in the input layer is greater than the number of nodes in the output layer, and the number of nodes increases as the network progresses from the input layer to the hidden layer. In another embodiment of the present disclosure, the neural network may be a neural network that combines the above-mentioned neural networks.

[0069] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Deep neural networks can be used to understand the latent structures of data, such as the latent structure of photos, text, video, audio, and music (e.g., what objects are in the photo, what is the content and emotion of the text, what is the content and emotion of the audio, etc.). Deep neural networks can include convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), Q-networks, U-networks, Siamese networks, generative adversarial networks (GANs), and transformers. The above-mentioned deep neural networks are merely examples and the present disclosure is not limited thereto.

[0070] Neural networks can be trained using at least one of supervised, unsupervised, semisupervised, or reinforcement learning. Training a neural network can be a process of applying knowledge to the neural network to make it perform a specific operation.

[0071] Neural networks can be trained to minimize output errors. In neural network training, training data is repeatedly input to the neural network, the error between the neural network's output and the target for the training data is calculated, and the neural network error is backpropagated from the output layer to the input layer of the neural network to update the weights of each node in the neural network in order to reduce the error. In supervised learning, training data in which each individual training data is labeled with a correct answer (i.e., labeled training data) can be used. In unsupervised learning, training data in which each individual training data is not labeled with a correct answer can be used. For example, in supervised learning for data classification, training data can be data in which each training data is labeled with a category. Labeled training data is input to the neural network, and the error can be calculated by comparing the neural network's output (category) with the label of the training data. As another example, in unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network's output. The calculated error is backpropagated in the reverse direction in the neural network (i.e., from the output layer to the input layer), and the connection weights of each node at each layer of the neural network can be updated through backpropagation. The amount of change in the connection weights of each node to be updated can be determined by the learning rate. The neural network calculation for input data and backpropagation of the error can constitute a learning cycle (epoch). The application method of the learning rate can change depending on the number of iterations of the neural network learning cycle. For example, in the early stages of neural network learning, the learning rate can be increased to allow the neural network to quickly achieve a certain level of performance, thereby improving efficiency, and in the later stages of learning, the learning rate can be decreased to improve accuracy.

[0072] In neural networks, various data augmentation methods can be used to increase the amount of training data required for training. For example, data augmentation can be performed through two-dimensional transformations such as rotation, scaling, shearing, reflection, and translation. Data augmentation can also be performed using noise insertion, color and brightness changes, etc.

[0073] In neural network training, the training data is typically a subset of the actual data (i.e., the data to be processed using the trained neural network). Therefore, there may be a training cycle in which the error associated with the training data decreases but the error associated with the actual data increases. Overfitting is a phenomenon in which excessive learning on the training data increases the error associated with the actual data. Overfitting can increase the error of machine learning algorithms. Various optimization methods can be used to prevent overfitting. Methods that can be applied include increasing the training data, regularization, dropout (which deactivates some nodes in the network during the training process), and the use of batch normalization layers.

[0074] FIG. 11 is a block diagram of a computing device according to some embodiments of the present disclosure. FIG. 11 is a simplified general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure can be implemented.

[0075] While the present disclosure has been described above as generally being embodied in a computing device, those skilled in the art will appreciate that the present disclosure can also be embodied in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers and / or as a combination of hardware and software. Generally, modules herein include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Those skilled in the art will also appreciate that the methods of the present disclosure can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which can operate in conjunction with one or more associated devices.

[0076] The embodiments described in this disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. A computer includes a variety of computer-readable media. Any medium accessible by a computer can be computer-readable, including volatile and nonvolatile media, transitory and non-transitory media, and portable and non-portable media. By way of example and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, portable and non-portable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage devices, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store information.

[0077] Computer-readable transmission media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Any combination of any of the foregoing media should also be included within the scope of computer-readable transmission media.

[0078] An exemplary environment (1100) for implementing various aspects of the present disclosure is shown, including a computer (1102) including a processing unit (1104), a system memory (1106), and a system bus (1108). The system bus (1108) couples system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) can be any of a variety of commercially available processors. Dual processors and other multi-processor architectures can also be utilized as the processing unit (1104). The system bus (1108) can be any of several types of bus structures that can be further interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). The basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM. The BIOS contains the basic routines that support the exchange of information between the various components within the computer (1102), such as during startup. The RAM (1112) can also include high-speed RAM, such as static RAM, for caching data.

[0079] The computer 1102 also includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 can also be configured for external use in a suitable chassis (not shown)—a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from and writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., for reading from a CD-ROM disk 1122 or for reading from and writing to other high-capacity optical media such as DVDs). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 can be connected to the system bus 1108 by a hard disk drive interface 1124, a magnetic disk drive interface 1126, and an optical drive interface 1128, respectively. The interface (1124) for implementing an external drive includes, for example, at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies. These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of computer 1102, the drives and media accommodate storing any data in a suitable digital format. While the foregoing description of computer-readable storage media refers to hard disk drives, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate that other types of computer-readable storage media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., can also be used in the exemplary operating environment, and that any such media can contain computer-executable instructions for performing the methods of the present disclosure.

[0080] A number of program modules, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136, may be stored on the drives and in RAM 1112. All or portions of the operating system, applications, modules, and / or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented with various commercially available operating systems or combinations of operating systems. A user can enter commands and information into the computer 1102 through one or more wired or wireless input devices, such as a keyboard 1138 and a pointing device such as a mouse 1140. Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit 1104 through an input device interface 1142 connected to the system bus 1108, but may also be connected through other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0081] A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface, such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown), such as speakers, printers, etc. The computer 1102 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and / or wireless communications. The remote computer(s) 1148 can be a workstation, a server computer, a router, a personal computer, a handheld computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and typically includes many or all of the components described for the computer 1102, although for simplicity, only a memory storage device 1150 is shown. The logical connections shown include wired and wireless connections in a local area network (LAN) 1152 and / or larger networks, e.g., a long-range network (WAN) 1154. Such LAN and WAN networking environments are commonplace in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global computer network, e.g., the Internet.

[0082] When used in a LAN networking environment, the computer 1102 connects to the local network 1152 through a wired and / or wireless communication network interface or adapter 1156. The adapter 1156 can facilitate wired or wireless communication to the LAN 1152, which may also include a wireless access point attached thereto for communicating with the wireless adapter 1156. When used in a WAN networking environment, the computer 1102 can include a modem 1158 or other means for establishing communications over the WAN 1154, such as connecting to a communications server on the WAN 1154 or through the Internet. The modem 1158, which can be internal or external and can be a wired or wireless device, connects to the system bus 1108 through the serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory / storage device 1150. It will be readily appreciated that the network connections shown are exemplary and other means of establishing a communications link between two or more computers may be used. The computer 1102 is operable to communicate with any wireless device or unit configured and operating in a wireless manner, such as printers, scanners, desktop and / or handheld computers, portable data assistants (PDAs), communications satellites, any equipment or location associated with a radio-detectable tag, and telephones. This includes at least Wi-Fi and Bluetooth® wireless technologies. Thus, communication can be in a predefined structure, such as a traditional network, or simply ad hoc communication between at least two devices.

[0083] Wi-Fi (Wireless Fidelity) allows devices to connect to the Internet without being wired. Wi-Fi is a wireless technology similar to cell phones, allowing such devices, such as computers, to send and receive data indoors and outdoors—anywhere within the coverage area of ​​a base station. Wi-Fi networks use IEEE 802.11 (a, b, g, etc.) radio technology to provide secure, reliable, and fast wireless connections. Wi-Fi can be used to connect computers to each other, the Internet, and wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 or 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual bands).

[0084] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referred to in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields, etc. or particles, optical fields, etc. or particles, or any combination thereof. Those skilled in the art will appreciate that the various illustrative logic blocks, modules, processors, means, circuits, and algorithm steps described in the description of the embodiments disclosed herein can be implemented with electronic hardware, various forms of program or design code (for convenience, referred to herein as "software"), or a combination of all of these. To clearly illustrate this interoperability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been described above generally by focusing on their functionality. Whether such functionality is implemented in hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art will appreciate that the described functionality can be implemented in various ways for each particular application, and such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0085] Various embodiments described herein may be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes any computer program, carrier, or media accessible by a computer-readable device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Additionally, various storage media described herein include one or more devices and / or other machine-readable media for storing information. It should be understood that the specific order or hierarchy of steps in the processes depicted is an example of an exemplary approach. Based on design priorities, it should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy depicted.

[0086] The description of the illustrated embodiments is provided to enable any person skilled in the art to which the disclosure pertains to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited by the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. 1. A method for providing menstruation-related information performed by a computing device, comprising: acquiring physiological information including a predetermined plurality of electrocardiogram variable information, the plurality of electrocardiogram variable information including time series data; obtaining user question and answer information corresponding to the plurality of electrocardiogram variable information; and generating menstrual cycle prediction information based on the biological information and the user question and answer information using an analytical model; Including, method.

2. In claim 1, The plurality of electrocardiogram variable information includes RRI (RR intervals) information, HR (Heart Rate) information, RR (Respiration Rate) information, ST level information, SDNN (standard deviation of all NN intervals) information, RMSSD (square root of the mean of the sum of the squares of differences between adjacent NN intervals) information, NN50 (number of NN intervals differing by more than 50 ms) information, pNN50 (ratio of NN50) information, SDSD (standard deviation of differences between adjacent NN intervals) information, method.

3. In claim 1 or claim 2, The user question and answer information includes at least one of question and answer information related to sleep disorders, question and answer information related to depression, question and answer information related to anxiety, and question and answer information related to stress. method.

4. In claim 2, the analytical model includes an ensemble learning model using a combination of a plurality of predictor variables, each of which includes at least a portion of the plurality of electrocardiogram variable information and the user question-and-answer information; method.

5. In claim 1 or claim 4, The analytical model is When a decreasing trend of the RRI information is identified based on the RRI information, learning is performed to generate information predicting a transition from the follicular phase to the luteal phase. method.

6. In claim 1 or claim 4, The analytical model is If a trend of sustained decrease in HR information is identified, the system is trained to generate information predicting the transition from the menstrual phase to the follicular phase; If an increasing trend in HR information is identified, it is trained to generate information predicting the transition from the follicular phase to the fertile phase; and When a trend in the highest level of HR information is identified, it is trained to generate information predicting the transition to the luteal phase. method.

7. In claim 1 or claim 4, The analytical model is When a decreasing trend of the RR information is identified, the method is trained to generate information predicting a transition from the menstrual phase to the follicular phase, if it identifies a further downward trend in the RR information, it is trained to generate information predictive of transition to fertility; and When an increasing trend of the RR information is identified, the method is trained to generate information predicting a transition to the luteal phase. method.

8. In claim 1 or claim 4, The analytical model is and if a trend in the maximum level of the ST level information is identified, the ST level information is trained to generate information predicting transition to the follicular phase; and When a trend of the minimum level of the ST level information is identified, learning is performed to generate information predicting the transition to the luteal phase. method.

9. In claim 1 or claim 4, The analytical model is When a maintenance tendency of the SDNN information is identified, the SDNN information is trained to generate information predicting a transition from the menstrual phase to the follicular phase, if a decreasing trend in the SDNN information is identified, it is trained to generate information predictive of a transition from the follicular phase to the fertile phase; and When the maximum decreasing trend of the SDNN information is identified, the SDNN information is trained to generate information predicting the transition to the luteal phase. method.

10. In claim 1 or claim 4, The analytical model is and training the RMSSD information to generate information predicting a transition to the luteal phase when the maximum decreasing trend of the RMSSD information is identified. method.

11. In claim 1 or claim 4, The analytical model is When a decreasing trend of the NN50 information is identified, the method is trained to generate information predicting the transition from the follicular phase to the luteal phase. method.

12. In claim 1 or claim 4, The analytical model is When a decreasing trend of the pNN50 information is identified, the method is trained to generate information predicting a transition from the follicular phase to the luteal phase. method.

13. In claim 1 or claim 4, The analytical model is When a tendency for the SDSD information to be maintained is identified, the information is trained to generate information for predicting a transition from the menstrual phase to the follicular phase. method.

14. In claim 1 or claim 4, the user question and answer information includes question and answer information related to sleep disorders, The analytical model is When an increasing trend of the question and answer information related to the sleep disorder is identified, the device is trained to generate information predicting the transition to the luteal phase. method.

15. In claim 1 or claim 4, the user question and answer information includes question and answer information related to depression, The analytical model is When an increasing trend of the question-and-answer information related to the depressed mood is identified, the system is trained to generate information predicting a transition to the luteal phase. method.

16. In claim 1 or claim 4, The user question and answer information includes question and answer information related to anxiety, The analytical model is When an increasing trend of the question-and-answer information related to the anxiety is identified, learning is performed to generate information predicting a transition to the luteal phase. method.

17. In claim 1 or claim 4, the user question and answer information includes question and answer information related to stress, The analytical model is When an increasing trend of the question-and-answer information related to stress is identified, the system is trained to generate information for predicting delayed ovulation. method.

18. In claim 1 or claim 4, The step of obtaining user question and answer information corresponding to a plurality of electrocardiogram variable information includes: providing a user interface for inputting the user question and answer information corresponding to a time point when the plurality of electrocardiogram variable information is acquired; Including, method.

19. In claim 18, The step of obtaining user question and answer information corresponding to a plurality of electrocardiogram variable information includes: providing a user interface indicating whether the user question and answer information corresponding to a time point when the plurality of electrocardiogram variable information is acquired has been input; Including, method.

20. In claim 1, The plurality of electrocardiogram variable information includes RRI information, HR information, RR information, ST level information, SDNN information, RMSSD information, NN50 information, pNN50 information, and SDSD information. method.

21. In claim 20, The user question and answer information includes question and answer information related to sleep disorders, question and answer information related to depression, question and answer information related to anxiety, and question and answer information related to stress. method.

22. In claim 4, The analytical model uses a random forest algorithm. method.

23. 1. A computer program stored on a computer-readable medium, the computer program comprising instructions for causing one or more processors to perform a method for providing menstrual-related information, the method comprising: acquiring physiological information including a predetermined plurality of electrocardiogram variable information, the plurality of electrocardiogram variable information including time series data; obtaining user question and answer information corresponding to the plurality of electrocardiogram variable information; and generating menstrual cycle prediction information based on the biological information and the plurality of user question-and-answer information using an analytical model; A computer program stored on a computer-readable medium.

24. 1. A computing device that performs a method for providing menstrual-related information, the computing device comprising: memory containing computer-executable components; a processor executing the following computer-executable components stored in memory: The processor: Acquire biological information including a predetermined plurality of electrocardiogram variable information, the plurality of electrocardiogram variable information including time series data; obtaining user question and answer information corresponding to the plurality of electrocardiogram variable information; and generating menstrual cycle prediction information based on the biological information and the plurality of user question-and-answer information using an analytical model; Computing equipment.

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