Family characteristic analysis method and device based on member temperament combination information

Abstract

A family characteristic analysis method according to one embodiment of the present disclosure comprises the steps of: receiving a survey response from respective family members; determining a plurality of sub-characteristics on the basis of the survey response; determining an individual temperament type of the respective family members on the basis of the sub-characteristics; and generating family characteristic combination data on the basis of the individual temperament types.

Description

Method and device for analyzing family characteristics based on member temperament combination information

[0001] The content of the present disclosure relates to a method and system for analyzing family characteristics, and specifically to a method and system for determining family temperament characteristics based on a combination of temperament types of family members and providing customized test results.

[0002] Understanding the personality and temperament of each family member and grasping the characteristics of the family as a whole is crucial for forming healthy family relationships. This plays a key role in improving family interactions and preventing potential conflicts.

[0003] Currently, the majority of parents rely on non-professional means to obtain child-rearing information and advice. While internet searches, books, television, and online communities are utilized as primary sources of information, these methods have limitations in that they fail to consider the specific circumstances of individual families. Relatively few parents seek professional parenting counseling, and even this is inaccessible due to the significant investment of time and money required.

[0004] With the advancement of IT technology, various mobile applications related to parenting, psychological testing, and counseling have emerged. However, most of these services remain at the level of simply transferring existing offline counseling methods to a digital platform. This has limitations in that it fails to adequately reflect the unique characteristics and needs of individual families.

[0005] Therefore, an approach is needed that can accurately analyze the characteristics of each family member and comprehensively evaluate the dynamics of the entire family. In particular, there is an urgent need to develop intelligent services capable of providing parenting counseling and guidance tailored to various family structures and situations.

[0006] The present disclosure is conceived in response to the aforementioned background technology and aims to provide a method for analyzing family characteristics based on information regarding the combination of temperaments of family members.

[0007] The present disclosure can provide an intelligent service capable of providing parenting counseling and parenting advice tailored to various family types and situations.

[0008] The present disclosure can predict the likelihood of problems occurring within a family based on the results of an analysis of family characteristics and suggest appropriate countermeasures.

[0009] However, the problems to be solved in this disclosure are not limited to those mentioned above, and other unmentioned problems may be clearly understood based on the description below.

[0010] A method for analyzing family characteristics to realize the aforementioned task includes the steps of receiving survey responses from each of a plurality of family members, determining a plurality of sub-characteristics based on the survey responses, determining the individual temperament type of each of the plurality of family members based on the sub-characteristics, and generating family characteristic combination data based on the individual temperament types.

[0011] Alternatively, the above sub-characteristics include a first sub-characteristic regarding vitality, a second sub-characteristic regarding control, and a third sub-characteristic regarding emotional sensitivity, and the survey responses are characterized by including a first survey set corresponding to the first sub-characteristic, a second survey set corresponding to the second sub-characteristic, and a third survey set corresponding to the third sub-characteristic.

[0012] Alternatively, the step of determining the plurality of sub-attributes is characterized by including the step of generating a first reference value for determining the first sub-attribute in a family attribute database and comparing the response data for the first survey set with the first reference value.

[0013] Alternatively, the plurality of family members includes a father, a mother, and a child, and the family characteristic combination data includes a family type, a conflict factor, a protective factor, and a vulnerability factor, wherein the family type is generated based on a combination of individual temperament types of each of the plurality of family members, the conflict factor includes temperament differences between parents and temperament differences between parents and children generated based on the survey responses, the protective factor includes the child's resilience, the parent's vitality level, and temperament congruence generated based on the survey responses, and the vulnerability factor includes the parent's vulnerability and the child's sensitivity generated based on the survey responses.

[0014] Alternatively, the step of generating the above-mentioned family characteristic combination data is characterized by including a step of predicting the likelihood of problem occurrence based on the above-mentioned family type, conflict factors, protective factors, and vulnerability factors.

[0015] Alternatively, the step of predicting the possibility of the above-mentioned problem occurrence is characterized by including a step of multiplying the above-mentioned conflict factor, protection factor, and vulnerability factor by a preset weight, respectively.

[0016] A family characteristic analysis device according to one embodiment of the present disclosure includes an input module that receives survey responses from each of a plurality of family members, a sub-characteristic determination module that determines a plurality of sub-characters based on the survey responses, an individual temperament type determination module that determines an individual temperament type of each of the plurality of family members based on the sub-characters, a family characteristic combination module that generates family characteristic combination data based on the individual temperament types, and a family characteristic database that stores the family characteristic combination data.

[0017] Alternatively, the above sub-characteristics include a first sub-characteristic regarding vitality, a second sub-characteristic regarding control, and a third sub-characteristic regarding emotional sensitivity, and the survey responses are characterized by including a first survey set corresponding to the first sub-characteristic, a second survey set corresponding to the second sub-characteristic, and a third survey set corresponding to the third sub-characteristic.

[0018] Alternatively, the sub-attribute determination module is characterized by generating a first reference value for determining the first sub-attribute based on information stored in the family attribute database.

[0019] Alternatively, the plurality of family members includes a father, a mother, and a child, and the family characteristic combination data includes a family type, a conflict factor, a protective factor, and a vulnerability factor, wherein the family type is generated based on a combination of individual temperament types of each of the plurality of family members, the conflict factor includes temperament differences between parents and temperament differences between parents and children generated based on the survey responses, the protective factor includes the child's resilience, the parent's vitality level, and temperament congruence generated based on the survey responses, and the vulnerability factor includes the parent's vulnerability and the child's sensitivity generated based on the survey responses.

[0020] Alternatively, the system further includes a problem occurrence prediction module that predicts the likelihood of a problem occurring based on the above-mentioned family type, conflict factor, protection factor, and vulnerability factor, wherein the problem occurrence prediction module calculates the probability of a problem occurring by multiplying the conflict factor, protection factor, and vulnerability factor by a preset weight, respectively.

[0021] A computing device for performing family characteristic analysis according to one embodiment of the present disclosure includes a processor comprising at least one core, a memory comprising program codes executable on the processor, and a network unit for acquiring data. The processor is characterized by receiving survey responses from each of a plurality of family members, determining a plurality of sub-characteristics based on the survey responses, determining an individual temperament type of each of the plurality of family members based on the sub-characteristics, and generating family characteristic combination data based on the individual temperament types.

[0022] Through the present disclosure, the temperament of individual family members and the characteristics of the entire family can be accurately analyzed.

[0023] The present disclosure provides parenting counseling and parenting advice tailored to various family types and situations, and can provide services regardless of time and place.

[0024] FIG. 1 is a block diagram of a computing device according to one embodiment of the present disclosure.

[0025] FIG. 2 is a block diagram illustrating a family characteristic analysis device according to one embodiment of the present disclosure.

[0026] FIG. 3 is a table illustrating a method for determining sub-characteristics according to one embodiment of the present disclosure.

[0027] FIG. 4 is a table describing individual substrate types according to one embodiment of the present disclosure.

[0028] FIG. 5 is a flowchart illustrating a family characteristic analysis method according to one embodiment of the present disclosure.

[0029] FIG. 6 is a flowchart illustrating a family trait analysis system according to one embodiment of the present disclosure.

[0030] FIG. 7 is a flowchart illustrating a family trait analysis system according to one embodiment of the present disclosure.

[0031] Embodiments of the present disclosure are described below with reference to the attached drawings so that those skilled in the art (hereinafter, those skilled in the art) can easily implement them. The embodiments presented in the present disclosure are provided to enable those skilled in the art to use or implement the contents of the present disclosure. Accordingly, various modifications to the embodiments of the present disclosure will be apparent to those skilled in the art. That is, the present disclosure may be embodied in various different forms and is not limited to the embodiments below.

[0032] Throughout the specification of the present disclosure, identical or similar reference numerals refer to identical or similar components. Additionally, to clearly explain the present disclosure, reference numerals in the drawings that are unrelated to the description of the present disclosure may be omitted.

[0033] The term “or” as used in this disclosure is intended to mean an implicit “or” rather than an exclusive “or.” That is, unless otherwise specified in this disclosure or its meaning is not clear from the context, “X uses A or B” should be understood to mean one of the natural implicit substitutions. For example, unless otherwise specified in this disclosure or its meaning is not clear from the context, “X uses A or B” may be interpreted as X using A, X using B, or X using both A and B.

[0034] The term “and / or” as used in this disclosure should be understood to refer to and include all possible combinations of one or more of the enumerated related concepts.

[0035] The terms “comprising” and / or “comprising” as used in this disclosure should be understood to mean the presence of certain features and / or components. However, the terms “comprising” and / or “comprising” should be understood not to exclude the presence or addition of one or more other features, other components and / or combinations thereof.

[0036] Where not otherwise specified in the present disclosure or where it is not clear from the context that the singular form indicates, the singular should generally be interpreted as including “one or more.”

[0037] The term “the N (N is a natural number)” used in this disclosure may be understood as an expression used to distinguish the components of this disclosure from one another according to certain criteria, such as functional perspectives, structural perspectives, or convenience of explanation. For example, components performing different functional roles in this disclosure may be distinguished as a first component or a second component. However, components that are substantially identical within the technical scope of this disclosure but must be distinguished for the convenience of explanation may also be distinguished as a first component or a second component.

[0038] The term “acquisition” as used in this disclosure can be understood to mean not only receiving data through a wired or wireless communication network with an external device or system, but also generating data in an on-device form.

[0039] Meanwhile, the terms "module" or "unit" used in this disclosure may be understood as referring to an independent functional unit that processes computing resources, such as a computer-related entity, firmware, software or a part thereof, hardware or a part thereof, or a combination of software and hardware. In this case, "module" or "unit" may be a unit composed of a single element, or a unit expressed as a combination or set of multiple elements. For example, in a narrow sense, "module" or "unit" may refer to a hardware element of a computing device or a set thereof, an application program that performs a specific function of software, a procedure implemented through software execution, or a set of instructions for program execution. Furthermore, in a broad sense, "module" or "unit" may refer to the computing device itself that constitutes the system, or an application executed on the computing device. However, since the above-described concept is merely an example, the concepts of "module" or "part" may be defined in various ways within the scope understandable to those skilled in the art based on the contents of this disclosure.

[0040] As used in this disclosure, the term "model" may be understood as a system implemented using mathematical concepts and language to solve a specific problem, a set of software units to solve a specific problem, or an abstract model regarding a processing process to solve a specific problem. For example, a neural network "model" may refer to an overall system implemented as a neural network that possesses problem-solving capabilities through learning. In this case, the neural network may possess problem-solving capabilities by optimizing parameters connecting nodes or neurons through learning. A neural network "model" may include a single neural network or a set of neural networks composed of multiple neural networks.

[0041] The term "block" as used in this disclosure can be understood as a set of configurations classified based on various criteria such as type, function, etc. Accordingly, configurations classified as a single "block" may be varied depending on the criteria. For example, a neural network "block" can be understood as a set of neural networks including at least one neural network. In this case, it can be assumed that the neural networks included in the neural network "block" perform specific operations identically.

[0042] The term "operation function" as used in this disclosure can be understood as a mathematical expression for a constituent unit that performs a specific function or processes an operation. For example, the "operation function" of a neural network block can be understood as a mathematical expression representing a neural network block that processes a specific operation. Accordingly, the relationship between the input and output of a neural network block can be expressed as a formula through the "operation function" of the neural network block.

[0043] The explanation of the foregoing terms is intended to aid in understanding the present disclosure. Accordingly, it should be noted that unless a foregoing term is explicitly stated as a matter limiting the content of the present disclosure, it is not to be used in the sense of limiting the technical concept of the content of the present disclosure.

[0044] FIG. 1 is a block diagram of a computing device according to one embodiment of the present disclosure.

[0045] A computing device (100) according to one embodiment of the present disclosure may be a hardware device or part of a hardware device that performs comprehensive processing and computation of data, or it may be a software-based computing environment connected to a communication network. For example, the computing device (100) may be a server that performs intensive data processing functions and is an entity that shares resources, or it may be a client that shares resources through interaction with the server. Additionally, the computing device (100) may be a cloud system in which a plurality of servers and clients interact to comprehensively process data. Since the above description is merely one example regarding the type of computing device (100), the type of computing device (100) may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

[0046] Referring to FIG. 1, a computing device (100) according to one embodiment of the present disclosure may include a processor (110), a memory (120), and a network unit (130). However, since FIG. 1 is merely an example, the computing device (100) may include other configurations for implementing a computing environment. Additionally, only some of the disclosed configurations may be included in the computing device (100).

[0047] A processor (110) according to one embodiment of the present disclosure may be understood as a constituent unit comprising hardware and / or software for performing computing operations. For example, the processor (110) may read a computer program to perform survey responses, determine sub-characteristics based on the survey responses, determine individual temperament types of each family member, and generate family characteristic combination data. The processor (110) may read a computer program to perform data processing for machine learning. The processor (110) may process computational processes such as processing input data for machine learning, feature extraction for machine learning, embedding, and error calculation based on backpropagation. A processor (110) for performing such data processing may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), or a field programmable gate array (FPGA). Since the above-described type of processor (110) is merely an example, the type of processor (110) can be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

[0048] A memory (120) according to one embodiment of the present disclosure may be understood as a configuration unit comprising hardware and / or software for storing and managing data processed by a computing device (100). That is, the memory (120) may store data of any form generated or determined by a processor (110) and data of any form received by a network unit (130). For example, the memory (120) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory, RAM (random access memory), SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory, a magnetic disk, and an optical disk. Additionally, the memory (120) may include a database system that controls and manages data in a predetermined system. Since the above-described type of memory (120) is merely an example, the type of memory (120) can be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

[0049] The memory (120) can manage data, combinations of data, and program code executable by the processor (110) by structuring and organizing them for the processor (110) to perform calculations. For example, the memory (120) can store data received through the network unit (130) described later. The memory (120) can store program code for family characteristic analysis and processed data generated as the program code is executed.

[0050] A network unit (130) according to one embodiment of the present disclosure may be understood as a configuration unit that transmits and receives data through any known form of wired or wireless communication system. For example, the network unit (130) may perform data transmission and reception using wired or wireless communication systems such as a local area network (LAN), wideband code division multiple access (WCDMA), long term evolution (LTE), wireless broadband internet (WiBro), 5th generation mobile communication (5G), ultra-wide-band wireless communication, ZigBee, radio frequency (RF) communication, wireless LAN, wireless fidelity (Wi-Fi), near field communication (NFC), or Bluetooth. Since the communication systems described above are merely examples, wired or wireless communication systems for data transmission and reception of the network unit (130) may be applied in various ways other than those described above.

[0051] The network unit (130) can receive data necessary for the processor (110) to perform calculations through wired or wireless communication with any system or any client. Additionally, the network unit (130) can transmit data generated through the calculations of the processor (110) through wired or wireless communication with any system or any client. For example, the network unit (130) can receive data through communication with a neural network model, a database, a cloud server, or a computing device. The network unit (130) can transmit analysis data generated during the family characteristic analysis process and intermediate data and processed data derived during the calculation process of the processor (110) through communication with the aforementioned database, server, or computing device.

[0052]

[0053] FIG. 2 is a block diagram illustrating a family characteristic analysis device according to one embodiment of the present disclosure.

[0054] Referring to FIG. 2, the family trait analysis device (200) may include a processor (210), an input module (220), an output module (230), a database (240), a sub-trait determination module (250), an individual temperament type determination module (260), and a family trait combination module (270).

[0055] The processor (210) can provide a survey to the user to analyze family characteristics and process the received survey responses. The processor (210) can determine sub-characteristics based on the survey responses using a sub-characteristic judgment module (250) and determine the user's individual temperament type through an individual temperament type determination module (260). The processor (210) can generate family characteristic combination data through a family characteristic combination module (270). The processor (210) can store the generated family characteristic combination data in a database (240). The processor (210) can determine the reference values ​​required for determining sub-characteristics by referring to existing test results stored in the database (240).

[0056] The input module (220) may include one or more of a touchscreen, keyboard, keypad, and mouse for receiving survey responses from a user. The input module (220) may include one or more of input devices included in a mobile device such as a laptop, PC, smartphone, or tablet.

[0057] The output module (230) may include one or more of a display and a speaker. The output module (240) may include an output device included in a mobile device such as a laptop, PC, smartphone, or tablet for delivering analysis results to a user.

[0058]

[0059] FIG. 3 is a table illustrating a method for determining sub-characteristics according to one embodiment of the present disclosure.

[0060] Referring to FIGS. 3 and 4, the questionnaire may include multiple questionnaire items corresponding to each sub-characteristic to determine the sub-characteristic. For example, the sub-characteristic may include a first sub-characteristic regarding energy (S), a second sub-characteristic regarding regulation / emotion balance (E), and a third sub-characteristic regarding emotion sensitivity (N). The questionnaire may include a stimulus responsiveness (SF1) questionnaire set corresponding to the first sub-characteristic regarding energy (S), a negative influence (SF2) questionnaire set corresponding to the third sub-characteristic regarding emotion sensitivity (N), and a control (SF3) questionnaire set corresponding to the second sub-characteristic regarding regulation / emotion balance (E).

[0061] For example, the stimulus-responsiveness (SF1) questionnaire set may include questions such as, "Does the child squirm or move around when being dressed or undressed? (Q1_1)" and "Does the child laugh when played with actively and excitedly? (Q1_2)". The negative influence (SF2) questionnaire set may include questions such as, "Does the child whine and struggle when tired? (Q2_1)" and "Does the child cling to their parents when a stranger approaches and acknowledges them? (Q2_2)". The control (SF3) questionnaire set may include questions such as, "Does the child enjoy reading books? (Q3_1)" and "Does the child play with toys or objects for 5-10 minutes or more? (Q3_2)".

[0062] Each survey set may include n survey questions necessary to determine sub-characteristics, and the survey questions may be configured differently depending on age, gender, role, etc.

[0063] The family characteristic analysis device can analyze received user-specific survey responses. Survey responses may consist of scores for each survey item. For example, the response to each survey item may be a score between 0 and 7. The family characteristic analysis device can calculate the average score for the responses of the survey set corresponding to each sub-characteristic and determine the sub-characteristic type by comparing it with a reference value.

[0064] For example, the family characteristics analysis device may determine that if the average score of the responses to the stimulus-responsiveness (SF1) questionnaire set is greater than the first threshold value, it corresponds to the first sub-characteristic of energy (S) or is a vitality characteristic. If the average score of the responses to the stimulus-responsiveness (SF1) questionnaire set is less than or equal to the first threshold value, the family characteristics analysis device may determine that it does not correspond to the first sub-characteristic of energy (S) or is a serenity characteristic. The first threshold value may be set based on the average values ​​of existing tests stored in a database. The first threshold value may be set based on group information by classifying groups according to criteria such as the age, gender, role, race, nationality, and culture of the users responding to the questionnaire. For example, the family characteristics analysis device may provide a questionnaire set corresponding to Korean adolescent boys, and the first threshold value may be set by considering the average response value of Korean adolescent boys. The first, second, and third threshold values ​​may each be set differently. The family characteristics analysis device can continuously monitor test and analysis results to update each reference value.

[0065] For example, if the average score of the responses to the Negative Influence (SF2) questionnaire set is greater than the second threshold value, the family trait analysis device may determine that it corresponds to the second sub-trait of regulation / emotion balance (E) or is the calmness trait. If the average score of the responses to the Negative Influence (SF2) questionnaire set is less than or equal to the second threshold value, the family trait analysis device may determine that it does not correspond to the second sub-trait of regulation / emotion balance (E) or is the impulsivity trait.

[0066] For example, if the average score of the responses to the control (SF3) questionnaire set is greater than the third threshold value, the family trait analysis device may determine that it corresponds to the third sub-trait for emotion sensitivity (N) or is a stability happiness trait. If the average score of the responses to the control (SF3) questionnaire set is less than or equal to the third threshold value, the family trait analysis device may determine that it does not correspond to the third sub-trait for emotion sensitivity (N) or is a sensitivity trait.

[0067]

[0068] FIG. 4 is a table describing individual substrate types according to one embodiment of the present disclosure.

[0069] Referring to FIGS. 3 and 4, the family trait analysis device can determine a temperament type based on sub-traits. For example, as described in FIG. 3, if three sub-traits are defined, the temperament type can be determined as one of eight types, which are combinations of the three sub-traits. The family trait analysis device can determine that a user is of the first temperament type (CT1) if, as a result of analyzing the user's survey responses, the responses for the first, second, and third sub-traits are all below a threshold value. The family trait analysis device can determine that a user is of the eighth temperament type (CT8) if, as a result of analyzing the user's survey responses, the responses for the first, second, and third sub-traits are all above a threshold value. For example, the family trait analysis device can determine the first temperament type (CT1) as a relaxed dreamcatcher type or a relaxed explorer type (T1_1). The family trait analysis device can determine the first temperament type (CT1) as the laid-back turtle type (T2_1) associated with animals and provide the user with a first description (AE1) corresponding to the animal traits. The family trait analysis device can explain the temperament traits differently to the user depending on age or role. For example, if the survey response corresponds to the first temperament type (CT1), the family trait analysis device can provide a second description (CE1) to the child and a third description (PE1) to the parent. Additionally, the family trait analysis device can provide the user with other psychological test results corresponding to the temperament type. For example, the family trait analysis device can provide the user with an explanation that considers the MBTI test type characteristics (isfj, infp) corresponding to the first temperament type (CT1) or takes into account vulnerabilities prominent in the child's behavioral trait test results.

[0070]

[0071] FIG. 5 is a flowchart illustrating a family characteristic analysis method according to one embodiment of the present disclosure.

[0072] Referring to FIG. 5, the family characteristic analysis device can receive survey responses from a user (S110). The survey or survey responses may include a first survey set corresponding to a first sub-characteristic, a second survey set corresponding to a second sub-characteristic, and a third survey set corresponding to a third sub-characteristic.

[0073] The survey may include multiple survey questions corresponding to each sub-characteristic to determine the sub-characteristic. For example, the sub-characteristic may include a first sub-characteristic regarding energy (S), a second sub-characteristic regarding regulation / emotion balance (E), and a third sub-characteristic regarding emotion sensitivity (N). The survey may include a stimulus responsiveness (SF1) survey set corresponding to the first sub-characteristic regarding energy (S), a negative influence (SF2) survey set corresponding to the third sub-characteristic regarding emotion sensitivity (N), and a control (SF3) survey set corresponding to the second sub-characteristic regarding regulation / emotion balance (E). The family characteristic analysis device may configure survey questions differently based on age, gender, role, etc. The family characteristic analysis device may present the survey differently for each user, and may provide survey questions with varying numbers or types for the same user for simple tests, full-version tests, and periodic tests.

[0074] The family characteristic analysis device can analyze received user-specific survey responses. Survey responses may consist of scores for each survey item. For example, the response to each survey item may be a score between 0 and 7. The family characteristic analysis device can calculate the average score for the responses of the survey set corresponding to each sub-characteristic and determine the sub-characteristic type by comparing it with a reference value.

[0075] The family characteristic analysis device can determine sub-characteristics based on survey responses (S120). The family characteristic analysis device can determine the type of sub-characteristic by calculating the average score of the survey set corresponding to each sub-characteristic and comparing it with a reference value.

[0076] For example, the family characteristics analysis device may determine that if the average score of the responses to the stimulus-responsiveness (SF1) questionnaire set is greater than the first threshold value, it corresponds to the first sub-characteristic of energy (S) or is a vitality characteristic. If the average score of the responses to the stimulus-responsiveness (SF1) questionnaire set is less than or equal to the first threshold value, the family characteristics analysis device may determine that it does not correspond to the first sub-characteristic of energy (S) or is a serenity characteristic. The first threshold value may be set based on the average values ​​of existing tests stored in a database. The first threshold value may be set based on group information by classifying groups according to criteria such as the age, gender, role, race, nationality, and culture of the users responding to the questionnaire. For example, the family characteristics analysis device may provide a questionnaire set corresponding to Korean adolescent boys, and the first threshold value may be set by considering the average response value of Korean adolescent boys. The first, second, and third threshold values ​​may each be set differently.

[0077] For example, if the average score of the responses to the Negative Influence (SF2) questionnaire set is greater than the second threshold value, the family trait analysis device may determine that it corresponds to the second sub-trait of regulation / emotion balance (E) or is the calmness trait. If the average score of the responses to the Negative Influence (SF2) questionnaire set is less than or equal to the second threshold value, the family trait analysis device may determine that it does not correspond to the second sub-trait of regulation / emotion balance (E) or is the impulsivity trait.

[0078] For example, if the average score of the responses to the control (SF3) questionnaire set is greater than the third threshold value, the family trait analysis device may determine that it corresponds to the third sub-trait for emotion sensitivity (N) or is a stability happiness trait. If the average score of the responses to the control (SF3) questionnaire set is less than or equal to the third threshold value, the family trait analysis device may determine that it does not correspond to the third sub-trait for emotion sensitivity (N) or is a sensitivity trait.

[0079] The family trait analysis device can determine individual temperament types based on sub-traits (S130).

[0080] For example, if three sub-characteristics are defined, the temperament type may be determined as any one of eight types, which are combinations of the three sub-characteristics. The family characteristic analysis device may determine that a user is of the first temperament type if, as a result of analyzing the user's survey responses, the responses for the first, second, and third sub-characteristics are all below a threshold value. The family characteristic analysis device may determine that a user is of the eighth temperament type if, as a result of analyzing the user's survey responses, the responses for the first, second, and third sub-characteristics all exceed the threshold value. The number of sub-characteristics or combinations described in this disclosure are for convenience of explanation and are not limited thereto.

[0081] The family characteristic analysis device can generate family characteristic combination data by combining the individual temperament types of each family member (S140).

[0082] The family trait analysis device can classify family types by combining individual temperament types. For example, the family trait analysis device can analyze common characteristics by considering family conflicts, strengths, and weaknesses that may appear depending on each case among all possible combinations of individual temperament types, and classify them into n types. For example, if there are 8 individual temperament types, there are 512 possible combinations of a 3-person family, which can be classified into 45 types. The family trait analysis device can classify family combinations using an artificial neural network model or according to a pre-set algorithm.

[0083] Family characteristic combination data may include family type, conflict factors, protective factors, and vulnerability factors. The family type is generated based on a combination of the individual temperament types of each of multiple family members (e.g., father / mother / child); the conflict factors include temperament differences between parents and temperament differences between parents and children generated based on survey responses; the protective factors include the child's resilience, parent's vitality level, and temperament congruence generated based on survey responses; and the vulnerability factors may include the parent's vulnerability and the child's sensitivity generated based on the aforementioned survey responses.

[0084] Family characteristic combination data may include the predicted likelihood of problem occurrence based on family type, conflict factors, protective factors, and vulnerability factors. The likelihood of problem occurrence can be calculated by multiplying the conflict factors, protective factors, and vulnerability factors by pre-set weights, respectively.

[0085] For example, to calculate the probability of a problem occurring (PP), the family trait analysis device defines conflict elements (C), protective elements (P), and vulnerability elements (V) based on various factors (temperament differences between parents and children, vulnerability vs. resilience, energy levels) arising from family temperament combinations, and determines the probability and weight (α, β, γ) of each role, and can calculate it through the following mathematical formula 1 or multivariate analysis.

[0086] [Mathematical Formula 1]

[0087] PP=α*C+β*P+γV

[0088] C (Conflict Factor): Temperament differences between parents, temperament differences between parents and children (conflict factors)

[0089] P (Conflict Factor): Child resilience, parental energy level, temperament congruence (protective factor)

[0090] V(Vulunerability): Parental vulnerability, child sensitivity (vulnerability factors)

[0091] α, β, γ: Weights for each factor; these can be set by the administrator or the optimal values ​​can be determined through an artificial neural network model.

[0092] The family trait analysis device can calculate and classify PP values ​​by n combination types. For example, the family trait analysis device can classify PP ≥ 0.5 as a conflictual type and PP ≥ 0.3 as a harmonious type.

[0093] The family trait analysis device can determine the 'Energy Rocket Type' among the 45 types as a combination in which the child exhibits high energy level vulnerability (Spark Cheetah Type), the mother exhibits high energy level vulnerability (Spark Cheetah Type), and the father exhibits high energy level vulnerability (Spark Cheetah Type). The family trait analysis device determines this as a combination of vulnerable / vulnerable / vulnerable temperaments and can assign a strong energy conflict between child and parent of 0.8, a vulnerability factor for vulnerable temperament between child and parent of 0.9, a protective factor for mutual understanding due to child-parent temperament match of 0.6, a conflict factor weight of 0.4, a protective factor weight of 0.1, and a vulnerability factor weight of 0.5.

[0094] PP=α*C+β*P+γV = 0.5*0.8 + 0.1*0.6+0.4*0.9 = 0.4+0.06+0.36 = 0.7

[0095] The family characteristic analysis device can calculate the probability of problems occurring within the family of an energy rocket-type family as 70% and provide this to the user.

[0096] Criteria for problem occurrence may include parent-child conflict factors, vulnerability factors, protective factors, and buffering functions.

[0097] The family trait analysis device can assign weights to trait disparity (when the traits of parents and children are extremely different) and trait similarity (when both parents and children possess vulnerable traits) among the conflict factors between parents and children.

[0098] The family trait analysis device can assign weights to vulnerability factors in cases where both spouses possess vulnerable traits (e.g., sensitivity, negative emotional levels, etc.) or where coordination is difficult due to a lack of balance in dispositions (e.g., one spouse is active and spontaneous, while the other is calm and stable).

[0099] The family trait analysis device can determine that, regarding protective factors and buffering, if a child possesses resilient traits, conflicts may be buffered even if the parents possess vulnerable traits; and if one parent possesses stable and calm traits, the family's emotional balance can be maintained through buffering even if the child exhibits vulnerable traits, and can set weights for these protective factors.

[0100] For example, if the threshold range for the probability of a problem occurring is 10–20%, the family trait analysis device may determine that there is strong mutual complementarity in the combination of family traits and sufficient protective factors. If the threshold range is 20–40%, the device may determine that harmonious interaction is possible despite trait differences, and that children flexibly mediate differences in parental tendencies. If the threshold range is 40–60%, the device may determine that there is a high possibility of conflict due to trait differences, or that the family possesses both high sensitivity and spontaneity, and that both parents and children have high vitality, resulting in a lack of control. If the threshold range for the probability of a problem occurring is 60% or higher (very high), the device may determine that all family members are overly sensitive or that there are almost no conflict buffering factors, and that the family exhibits high vulnerability, such as both children and parents being irritable, neurotic, and uncontrollable.

[0101] FIG. 6 is a flowchart illustrating a family trait analysis system according to one embodiment of the present disclosure.

[0102] Referring to FIG. 6, the family characteristics analysis system can perform family linkage (S210) and a basic app login check (S220) after an individual member joins the system. The family characteristics analysis system can analyze individual temperament characteristics, classify family characteristic combination types, and provide parenting counseling data from an AI-based system (S230). The family characteristics analysis system can predict the likelihood of problems based on family characteristic types (S240). The family characteristics analysis system can provide a family-tailored daily training service (S250). The family characteristics analysis system can conduct parenting counseling (S270) through an AI parenting agent (S260). The family characteristics analysis system can provide a couples scheduler service that saves parenting records (S280). The family characteristics analysis system can accumulate AI counseling data and reflect it in the system (S290).

[0103] FIG. 7 is a flowchart illustrating a family trait analysis system according to one embodiment of the present disclosure.

[0104] Referring to FIG. 7, the family characteristics analysis system can accumulate AI counseling data by storing childcare records (S310) stored in a couple scheduler, storing results of recognizing emotions (S340) using an AI model on a child photo drawing (S330), and storing results of analyzing the quality of interaction using an AI model in a parent-child interaction video (S350) (S320).

[0105] The various embodiments of the present disclosure described above may be combined with additional embodiments and modified to the extent understandable to those skilled in the art in light of the detailed description above. The embodiments of the present disclosure are illustrative in all respects and should be understood as not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form. Accordingly, all modifications or variations derived from the meaning, scope, and equivalents of the claims of the present disclosure should be interpreted as being included within the scope of the present disclosure.

[0106]

Claims

1. A method for analyzing family characteristics performed by a computing device comprising at least one processor, wherein A step of receiving survey responses from each of the multiple family members; A step of determining multiple sub-characteristics based on the above survey responses; A step of determining the individual temperament type of each of the plurality of family members based on the above sub-characteristics; and Characterized by including the step of generating family characteristic combination data based on the above individual temperament types. Family Characteristics Analysis Method 2. In Paragraph 1, The above sub-characteristics include a first sub-characteristic regarding vitality, a second sub-characteristic regarding control, and a third sub-characteristic regarding emotional sensitivity, and The above survey response is characterized by including a first survey set corresponding to the first sub-attribute, a second survey set corresponding to the second sub-attribute, and a third survey set corresponding to the third sub-attribute. Family Characteristics Analysis Method 3. In Paragraph 2, The step of determining the plurality of sub-characteristics is characterized by including the step of generating a first reference value for determining the first sub-characteristic in a family characteristic database and comparing the response data for the first survey set with the first reference value. Family Characteristics Analysis Method 4. In Paragraph 1, The aforementioned multiple family members include a father, mother, and children, and The above family characteristic combination data includes family type, conflict factors, protective factors, and vulnerability factors, and The above family type is generated based on a combination of the individual temperament types of each of the above multiple family members, and The above conflict factors include temperament differences between parents and temperament differences between parents and children generated based on the above survey responses, and The above protective factors include the child's resilience, parent's vitality level, and temperament compatibility generated based on the above survey responses, and The above vulnerability factors are characterized by including parental vulnerability and child sensitivity generated based on the above survey responses, Family Characteristics Analysis Method 5. In Paragraph 4, The step of generating the above-mentioned family characteristic combination data is characterized by including the step of predicting the likelihood of problem occurrence based on the above-mentioned family type, conflict factors, protective factors, and vulnerability factors. Family Characteristics Analysis Method 6. In Paragraph 5, The step of predicting the possibility of the above-mentioned problem occurrence is characterized by including a step of multiplying the conflict factor, protection factor, and vulnerability factor, respectively, by a pre-set weight. Family Characteristics Analysis Method 7. In a family characteristics analysis device, Input module that receives survey responses from each of the multiple family members; A sub-characteristic judgment module that determines multiple sub-characteristics based on the above survey responses; An individual temperament type determination module that determines the individual temperament type of each of the plurality of family members based on the above sub-characteristics; A family trait combination module that generates family trait combination data based on the above individual temperament types; and Characterized by including a family trait database that stores the above-mentioned family trait combination data. Family characteristics analysis device.

8. In Paragraph 7, The above sub-characteristics include a first sub-characteristic regarding vitality, a second sub-characteristic regarding control, and a third sub-characteristic regarding emotional sensitivity, and The above survey response is characterized by including a first survey set corresponding to the first sub-attribute, a second survey set corresponding to the second sub-attribute, and a third survey set corresponding to the third sub-attribute. Family characteristics analysis device.

9. In Paragraph 8, The above sub-attribute determination module is characterized by generating a first reference value for determining the first sub-attribute based on information stored in the family attribute database. Family characteristics analysis device.

10. In Paragraph 9, The aforementioned multiple family members include a father, mother, and children, and The above family characteristic combination data includes family type, conflict factors, protective factors, and vulnerability factors, and The above family type is generated based on a combination of the individual temperament types of each of the above multiple family members, and The above conflict factors include temperament differences between parents and temperament differences between parents and children generated based on the above survey responses, and The above protective factors include the child's resilience, parent's vitality level, and temperament compatibility generated based on the above survey responses, and The above vulnerability factors are characterized by including parental vulnerability and child sensitivity generated based on the above survey responses, Family characteristics analysis device.

11. In Paragraph 10, It further includes a problem occurrence prediction module that predicts the likelihood of problem occurrence based on the above-mentioned family type, conflict factors, protective factors, and vulnerability factors, and The above problem occurrence prediction module is characterized by calculating the probability of problem occurrence by multiplying the above conflict factor, protection factor, and vulnerability factor by a preset weight, respectively. Family characteristics analysis device.

12. As a computing device that performs family characteristic analysis, A processor comprising at least one core; Memory containing program code executable in the above processor; and network unit for acquiring data; Includes, The above processor is, Characterized by receiving survey responses from each of the multiple family members, determining multiple sub-traits based on the survey responses, determining the individual temperament type of each of the multiple family members based on the sub-traits, and generating family trait combination data based on the individual temperament types. Computing device.

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