Psychological test interpretation method and device using same

Abstract

A psychological test interpretation method according to one embodiment of the present invention may comprise the steps of: acquiring psychological test data including a scale for a plurality of parameters; extracting, on the basis of the psychological test data, parameters of which the scale is greater than or equal to a first reference value and an interpretation index for the parameters of which the scale is greater than or equal to the reference value; determining an interpretation code on the basis of the parameters of which the scale is greater than or equal to the first reference value and the interpretation index; and providing interpretation content on the basis of the interpretation code.

Description

Psychological test interpretation method and device using the same

[0001] The present invention relates to a method for interpreting psychological tests, and more specifically, to a method for interpreting psychological tests using an interpretation code.

[0002]

[0003] Clinical psychological testing is an essential tool for evaluating and diagnosing an individual's mental and emotional state, playing a crucial role in identifying psychological problems and suggesting appropriate treatment plans. These tests are conducted based on standardized procedures that measure various psychological characteristics, and the results are interpreted by experts to diagnose an individual's mental condition and establish treatment plans. Clinical psychological testing includes tools that assess various psychological aspects, such as cognitive ability, personality traits, emotional stability, and mental health status, and the results of these tests serve as important evidence for objectively evaluating a patient's psychological issues.

[0004] However, the interpretation of such tests requires highly specialized knowledge and experience, and there is a problem in that significant subjectivity can be involved in the process. Even with identical test results, outcomes can vary depending on the interpreter's perspective or method of interpretation, which can affect the consistency and reliability of the results. Furthermore, the accuracy of interpretation can vary depending on the examiner's proficiency and experience, and the workload of professionals increases significantly, particularly in situations where a large number of test results must be processed quickly and accurately. Therefore, there is a growing need to reduce errors and inconsistencies that may occur during the interpretation of clinical psychological test results and to interpret results through more objective and standardized methods.

[0005] The present invention relates to the Ministry of Health and Welfare's Research-Oriented Hospital Development (1. Project No.: 2460000110, Sub-project No.: RS-2022-KH130303, Research Project Title: Super-Gap SUPER*Senior Total Health Care Platform, Lead Institution: Ajou University Medical Center, Research Period: 2024.01.01 ~ 2024.12.31, 2. Project No.: 2460000763, Sub-project No.: RS-2021-KH113821, Research Project Title: Construction of Human-Environmental Interaction Beyond Target Platform, Lead Institution: Ajou University Industry-Academic Cooperation Foundation, Research Period: 2024.01.01 ~ 2024.12.31), and the Ministry of Science and ICT's Group Research Support (Project No.: 1711191592, Sub-project No.: It was derived from research conducted as part of 2019R1A5A2026045, Research Project Title: Brain Disease Convergence Research Center, Lead Institution: Ajou University, Research Period: 2023.03.01 ~ 2024.02.29).

[0006] Meanwhile, in all aspects of the present invention, the Korean government, the entity providing the problem, has no property interest.

[0007]

[0008] One objective of the present invention is to a method for interpreting psychological tests using an interpretation code.

[0009]

[0010] A psychological test interpretation method according to one embodiment, in a psychological test interpretation method performed by at least one processor, may include: a step of acquiring psychological test data including a scale for a plurality of parameters; a step of extracting, based on the psychological test data, a parameter whose scale is greater than or equal to a first reference value and an interpretation indicator for the parameter whose scale is greater than or equal to the reference value; a step of determining an interpretation code based on the parameter whose scale is greater than or equal to the first reference value and the interpretation indicator; and a step of providing interpretation content based on the interpretation code.

[0011] Here, the psychological test data may include a first group parameter related to psychological test validity, a scale for the first group parameter, and a first graph for the first group parameter, and may include a second group parameter related to psychological state, a scale for the second group parameter, and a second graph for the second group parameter.

[0012] Here, the method may further include a step of determining the validity of a psychological test based on the profile of the first graph; and a step of determining the type of psychological state based on the profile of the second graph.

[0013] Here, the interpretation indicator may be the difference between the scale corresponding to each parameter and the first reference value.

[0014] Herein, the method further includes the step of extracting a parameter whose scale is less than a second threshold value and an interpretation indicator for the parameter whose scale is less than the second threshold value based on the psychological test data; and the step of determining an interpretation code based on the parameter whose scale is less than the second threshold value and the interpretation indicator for the parameter whose scale is less than the second threshold value, wherein the interpretation indicator for the parameter whose scale is less than the second threshold value may be the difference between the scale corresponding to each parameter and the second threshold value.

[0015] Here, the step of determining the interpretation code may include: a step of classifying interpretation indicators for extracted parameters according to a certain standard; a step of generating at least one intermediate code based on the classified interpretation indicators; and a step of determining the reference code with the highest similarity as the interpretation code based on the similarity of the intermediate code to a previously stored reference code.

[0016] Here, the step of generating the intermediate code may be a step of generating the intermediate code by setting the interpretation indicator having the largest value among the distinguished interpretation indicators as the first order.

[0017] Here, the step of determining the reference code with the highest similarity as the interpretation code may include the step of comparing the interpretation indicator of the first sequence of the intermediate code and the previously stored reference code, and the step of extracting a reference code that includes a number of interpretation indicators equal to or greater than the number of interpretation indicators included in the intermediate code among the previously stored reference codes.

[0018] Here, the step of providing the interpretation content may be a step of providing an interpretation guide corresponding to the interpretation code.

[0019] Here, the step of providing the interpretation content may include the step of inputting the interpretation code and subject information into a pre-trained artificial intelligence model, and the step of generating the interpretation content based on the result value of the artificial intelligence model.

[0020] Here, the first reference value is set based on subject information, and the subject information may include at least one of the subject's age, gender, place of residence, occupation, and previous medical history.

[0021] Here, a computer program stored on a computer-readable recording medium may be provided to execute the above-mentioned psychological test interpretation method.

[0022]

[0023] A computing device according to one embodiment includes a communication module; a memory; and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program may include instructions for acquiring psychological test data including a scale for a plurality of parameters, extracting a parameter whose scale is greater than or equal to a first reference value and an interpretation indicator for the parameter whose scale is greater than or equal to the reference value based on the psychological test data, determining an interpretation code based on the parameter whose scale is greater than or equal to the first reference value and the interpretation indicator, and providing interpretation content based on the interpretation code.

[0024]

[0025] According to one embodiment of the present invention, a method for interpreting a psychological test using an interpretation code may be provided.

[0026]

[0027] FIG. 1 is a flowchart of a method for interpreting a psychological test according to one embodiment.

[0028] FIG. 2 is a flowchart of a method for determining an interpretation code according to one embodiment.

[0029] FIG. 3 is a specific flowchart of a method for determining an interpretation code according to one embodiment.

[0030] Figure 4 is a diagram illustrating psychological test data.

[0031] Figure 5 is a diagram illustrating a method for determining interpretation codes.

[0032] Figures 6 to 8 are diagrams illustrating example results of a psychological test interpretation method.

[0033]

[0034] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the contents described in the attached drawings. However, the present invention is not limited or restricted by exemplary embodiments. Unless otherwise defined, all terms used in this specification (including technical and scientific terms) shall be used in a meaning that is commonly understood by those skilled in the art to which this disclosure belongs, but this may vary depending on the intent of those skilled in the art, case law, the emergence of new technology, etc.

[0035] Furthermore, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise. In certain cases, terms have been selected at the applicant's discretion, and in such cases, their meanings will be described in detail in the relevant explanatory sections. Accordingly, terms used in this disclosure should be defined not merely by their names, but based on their meanings and the content throughout this disclosure.

[0036] Throughout this specification, when a part is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, the singular form used in this specification includes the plural form unless specifically stated otherwise. Additionally, the expression "at least one of a, b, and / or c" as used throughout this specification may encompass 'a alone', 'b alone', 'c alone', 'a and b', 'a and c', 'b and c', or 'a, b, and c all'.

[0037] Meanwhile, terms such as "first and / or second" used in this specification may be used to describe various components, but they are used solely for the purpose of distinguishing one component from another and are not intended to limit the scope to the components referred to by such terms. For example, without departing from the scope of the present invention, the first component may be named the second component, and the second component may also be named the first component.

[0038] Additionally, terms such as “part,” “module,” etc., as described in this specification refer to a unit that processes at least one function or operation, which may be implemented in hardware or software, or a combination of hardware and software. Furthermore, embodiments of this disclosure may be represented in this specification by functional block configurations and various processing steps. These functional blocks may be implemented by various numbers of hardware and / or software configurations that execute specific functions. For example, embodiments of this disclosure may employ integrated circuit configurations such as memory, processing, logic, look-up tables, etc., which can execute various functions under the control of one or more microprocessors or other control devices.

[0039] In an embodiment according to the present disclosure, functions related to artificial intelligence may be implemented through a processor and memory. In this case, the processor may be any one of a general-purpose processor such as a CPU (Center Processing Unit), AP (Application Processor), DSP (Digital Signal Processor), a graphics-dedicated processor such as a GPU (Graphic Processing Unit) or VPU (Vision Processing Unit), and an artificial intelligence-dedicated processor such as an NPU (Neural Network Processing Unit). The processor may process input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the processor is an artificial intelligence-dedicated processor, the artificial intelligence-dedicated processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model. In some embodiments according to the present disclosure, functions related to artificial intelligence may be implemented through a plurality of processors.

[0040] In an embodiment according to the present disclosure, a predefined operation rule or artificial intelligence model may be configured to perform machine learning. Here, being configured to perform machine learning means that the predefined operation rule or artificial intelligence model is configured to perform a desired characteristic (or objective) by learning using a plurality of training data based on a learning algorithm. Such learning may be performed on the device itself in which the artificial intelligence according to the present disclosure is implemented, or it may be performed through a separate server and / or system.

[0041] Artificial intelligence models can be implemented as neural networks (or artificial neural networks) and can operate based on statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network can refer to a model in which artificial neurons (nodes), which form a network through the connection of synapses, change the strength of synaptic connections through learning to possess problem-solving capabilities. A neural network can be composed of multiple neural network layers; for example, a neural network may include an input layer, a hidden layer, and an output layer. Each of the multiple neural network layers may include at least one node and at least one weight, and neural network operations can be performed through operations between the results of operations of the previous (precious) layer and the weights. At least one weight possessed by the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, at least one weight may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Neural networks can infer a result to be predicted from an arbitrary input.

[0042] The learning methods of artificial intelligence models can be classified according to the learning approach into supervised learning, where input and output data are provided as training data and the correct answer (output data) corresponding to the problem (input data) is predetermined; unsupervised learning, where only input data is provided without output data and the correct answer (output data) corresponding to the problem (input data) is not predetermined; and reinforcement learning, where a reward is granted whenever an action is taken from the current state and learning proceeds in a direction that maximizes this reward. Alternatively, they can be classified according to the architecture, which is the structure of the learning model.

[0043] In the embodiments of the present disclosure, the artificial intelligence model is a Convolutional Neural Network (CNN) such as GoogleNet, AlexNet, VGG Network, Region with Convolutional Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based Deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restructured Boltzmann Machine (RBM), Fully Convolutional Network, Long Short-Term Memory Network (LSTM), Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA for Natural Language Processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, Time-Series Forecasting, Optimization, Recommendation for ResNet Data Intelligence, At least one of various artificial intelligence structures and algorithms, such as data creation, may be used. The examples described above are merely examples of artificial intelligence structures and algorithms used according to the embodiments of the present disclosure and do not limit the artificial intelligence structures and algorithms used according to the embodiments of the present disclosure.

[0044] Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In describing the embodiments, technical details that are well known in the art to which the present invention pertains and are not directly related to the present invention will be omitted. This is to ensure that the essence of the present invention is conveyed more clearly without obscuring it by omitting unnecessary explanations. For the same reason, some components in the accompanying drawings may be exaggerated, omitted, or schematically depicted. Furthermore, the size of each component does not entirely reflect its actual size. Throughout this specification, the same reference numerals may refer to the same or corresponding components.

[0045]

[0046] The method for interpreting a psychological test according to the present invention may be performed within a computing device. Specifically, the method for interpreting a psychological test according to the present invention may be performed by at least one processor included within the computing device. The computing device may include a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory. In this case, the at least one processor may include, but is not limited to, a communication processor, an analysis processor, an arithmetic processor, a storage processor, etc.

[0047] In addition, when describing the method of interpreting psychological tests in this specification, the MMPI (Minnesota Multiphasic Personality Inventory) is described as a representative example, but it is not limited thereto, and various psychological tests to which the present invention can be applied may include the SCT test, TCI test, Rorschach test, etc.

[0048]

[0049] FIG. 1 is a flowchart of a method for interpreting a psychological test according to one embodiment.

[0050] Referring to FIG. 1, a psychological test interpretation method according to one embodiment may include the step of acquiring psychological test data (S100), the step of extracting specific parameters and interpretation indicators (S200), the step of determining an interpretation code (S300), and the step of providing interpretation content (S400). FIG. 1 illustrates steps S100 to S400 being performed sequentially, but is not limited thereto, and some steps may be merged and performed simultaneously, some steps may be omitted, or new steps may be added.

[0051] The step of acquiring psychological test data (S100) allows the processor to acquire psychological test data including clinical scales for multiple parameters. Specifically, the psychological test data will be described with reference to FIG. 4.

[0052] Figure 4 is a diagram illustrating psychological test data.

[0053] Referring to FIG. 4, psychological test data may include multiple parameters and numerical values ​​(e.g., T-scores) for each of the multiple parameters. In this case, the multiple parameters may be various items depending on the psychological test. Also, in this case, the numerical values ​​for each of the multiple parameters may be values ​​calculated based on data previously tested by the subject.

[0054] For example, multiple parameters may include, but are not limited to, VRIN (Variable Response Inconsistency), TRIN (True Response Inconsistency), Fb (Back F Scale), L (Lie), F (Frequency), K (Correction), S (Superlative Self-Presentation Scales), Hs (Hysteria), D (Depression), Hy (Conversion Disorder), Pd (Antisociality), Mf (Masculinity / Femininity), Pa (Paranoia), Sc (Psychotic Tendency), Ma (Hyperactivity), Si (Social Introversion). Additionally, for example, the numerical values ​​for each of the multiple parameters may be values ​​calculated based on the values ​​checked by the subject for psychological test items such as 'I like computer magazines,' 'I have a good appetite,' 'I wake up easily at small noises,' and 'My father is a good person,' but are not limited to these.

[0055] Multiple parameters included in the psychological test data may include a first group of parameters related to the validity of the psychological test and a second group of parameters related to the psychological state. The first group of parameters may include parameters for determining whether the psychological test is valid for the subject. For example, the first group of parameters may include, but is not limited to, VRIN (Variable Response Inconsistency), TRIN (True Response Inconsistency), Fb (Back F Scale), L (Lie), F (Frequency), K (Correction), S (Superlative Self-Presentation Scales), etc.

[0056] The second group parameter may include parameters for determining the psychological state of the subject. For example, the second group parameter may include, but is not limited to, Hs (hysteria), D (depression), Hy (conversion disorder), Pd (antisociality), Mf (masculinity / femininity), Pa (paranoia), Sc (psychotic tendency), Ma (hyperactivity), Si (social introversion), etc.

[0057] Psychological test data may include a first group parameter, a scale for the first group parameter, and a first graph (100) for the first group parameter. Additionally, psychological test data may include a second group parameter, a scale for the second group parameter, and a second graph (200) for the second group parameter. Specifically, referring to FIG. 4, it can be seen that the first graph (100) and the second graph (200) appear separately. At this time, the first graph (100) is a graph indicating the validity of the psychological test, and the second graph (200) is a graph indicating the psychological state of the subject.

[0058] The processor can determine the validity of the psychological test based on the profile of the first graph (100). Additionally, the processor can determine the type of psychological state of the subject based on the profile of the second graph (200).

[0059] Specifically, the processor can check and / or determine the profile of the first graph (100) and the profile of the second graph (200). For example, the processor can check and / or determine whether the shape of the first graph (100) or the second graph (200) is a specific shape (e.g., W shape, upward-sloping shape, downward-sloping shape, cone shape, etc.). In this case, the specific shape is a previously stored shape, and the validity of the psychological test or the psychological state can be determined according to the specific shape.

[0060] For example, if the shape of the first graph (100) or the second graph (200) is high on the left and low on the right, the processor may determine that the subject's psychological state has neurotic or psychotic problems.

[0061] Referring again to FIG. 1, the step of extracting specific parameters and interpretation indicators (S200) may be a step in which a processor extracts parameters whose scales are greater than or equal to a first threshold value and interpretation indicators for parameters whose scales are greater than or equal to a threshold value based on psychological test data. At this time, the interpretation indicator may be the difference between the scale corresponding to each parameter and the first threshold value. However, it is not limited thereto, and the interpretation indicator may be calculated differently depending on the type of psychological test or the purpose of the test, such as the difference with a specific parameter (e.g., the parameter immediately preceding in order), or the average value of scales greater than or equal to a threshold value.

[0062] At this time, the first threshold value may be set based on the subject's information. That is, the first threshold value may be set differently based on at least one of the subject's age, gender, place of residence, occupation, and previous medical history. For example, the first threshold value when the subject is 30 years of age or older may have a lower value than the first threshold value when the subject is under 30 years of age, but is not limited thereto.

[0063] In addition, the processor can extract parameters whose scale is greater than or equal to a first threshold value, as well as parameters whose scale is less than a second threshold value. Accordingly, the processor can determine the psychological state of a subject by considering both parameters with high scales and parameters with low scales, not only by determining the psychological state of the subject based on parameters with scales greater than or equal to the first threshold value, but also by determining the psychological state of the subject based on parameters with scales less than the second threshold value.

[0064] Specifically, the processor can extract parameters whose scale is less than a second threshold value and interpretation indicators for parameters whose scale is less than the second threshold value based on psychological test data. In this case, the interpretation indicators for parameters less than the second threshold value may be the difference between the scale corresponding to each parameter and the second threshold value, but are not limited thereto.

[0065] The step of determining an interpretation code (S300) may be a step in which a processor determines an interpretation code based on parameters and interpretation indicators that are greater than or equal to a first reference value. At this time, the processor may consider not only parameters and interpretation indicators that are greater than or equal to the first reference value, but also parameters and interpretation indicators that are less than a second reference value. Step S300 will be described in detail below with reference to FIG. 2.

[0066] The step of providing interpretation content (S400) may be a step in which the processor selects and / or outputs interpretation content based on the interpretation code determined in step S300.

[0067] According to one embodiment, in step S400, the processor may provide an interpretation guide corresponding to the interpretation code.

[0068] According to another embodiment, in step S400, the processor may generate and / or provide user-customized interpretation content using a pre-trained artificial intelligence model. Specifically, the processor may obtain subject information in advance. In this case, the subject information may include at least one of the subject's age, gender, place of residence, occupation, and previous medical history.

[0069] The processor can input interpretation codes and subject information into a pre-trained artificial intelligence model. The processor can generate and / or provide interpretation content based on the output values ​​of the artificial intelligence model. In this case, the artificial intelligence model is a model trained on training data including interpretation codes and subject information, and upon receiving interpretation codes and subject information, it can provide psychological test interpretation content customized for the subject. This is explained below with reference to the exemplary results shown in FIGS. 6 to 8.

[0070]

[0071] FIG. 2 is a flowchart of a method for determining an interpretation code according to one embodiment.

[0072] Referring to FIG. 2, a method for determining an interpretation code according to one embodiment may include a step of distinguishing interpretation indicators (S310), a step of generating an intermediate code (S320), and a step of determining a reference code with high similarity as an interpretation code (S330). FIG. 2 illustrates steps S310 to S330 being performed sequentially, but is not limited thereto, and some steps may be merged and performed simultaneously, some steps may be omitted, or new steps may be added.

[0073] The step of classifying interpretation indicators (S310) may be a step in which the processor classifies interpretation indicators for the extracted parameters according to a certain standard. This step may be intended to reflect the fact that there may be significant differences between interpretation indicators. In this case, the certain standard may be whether the difference between interpretation indicators is greater than or equal to a third standard value. This will be explained in detail with reference to FIG. 5.

[0074] Figure 5 is a diagram illustrating a method for determining interpretation codes.

[0075] Referring to FIG. 5, the processor can extract parameters having a scale greater than or equal to a first reference value. In the example of FIG. 5, the reference value is 70, and accordingly, the processor can confirm that among the multiple parameters, Pd, Sc, and Si have a scale greater than or equal to the first reference value. At this time, the processor can calculate an interpretation index based on the scale values ​​of the extracted parameters. In the example of FIG. 5, the processor can determine 4, which is the difference between the first reference value (70) and the scale (74), as the interpretation index for the Pd parameter. Similarly, the processor can determine 8 and 0 as interpretation indices for Sc and Si, respectively.

[0076] The processor can distinguish interpretation indicators according to certain criteria. For example, the processor can distinguish indicators where the difference is 5 or more. As in the example above, if the interpretation indicators of the extracted parameters are 8, 4, and 0, the processor can distinguish the interpretation indicators as [8], [4, 0] based on whether the difference is 5 or more.

[0077] Referring again to FIG. 2, the step of generating an intermediate code (S320) may be a step in which a processor generates at least one intermediate code based on the interpretation indicators distinguished in step S310. In the above example, the processor may generate at least one intermediate code based on [8], [4, 0]. The intermediate code may be generated according to the number of combinations of the distinguished interpretation indicators. For example, the processor may generate an intermediate code of [8], [4, 0] or [8], [0, 4] depending on the number of cases.

[0078] At this time, the step of generating an intermediate code may be a step of generating an intermediate code by setting the interpretation index having the largest value among the separated interpretation indices as the first order. Accordingly, the processor may generate an intermediate code of [8], [4, 0] with 4 as the first order, since 4 is greater than 0 in the case of [8], [4, 0] or [8], [0, 4].

[0079] The step of determining a reference code with high similarity as an interpretation code (S330) may include a step in which the processor determines the similarity between the intermediate code generated in step S320 and a previously stored reference code. At this time, the processor may determine the reference code with the highest similarity among the previously stored reference codes as the interpretation code. Step S330 will be described in detail below with reference to FIG. 3.

[0080]

[0081] FIG. 3 is a specific flowchart of a method for determining an interpretation code according to one embodiment.

[0082] Referring to FIG. 3, a method for determining an interpretation code according to one embodiment may include a step (S331) of comparing interpretation indicators in the first order and a step (S332) of extracting a reference code based on the number of indicators. FIG. 3 illustrates steps S331 and S332 being performed sequentially, but is not limited thereto; the order of the two steps may be changed, the two steps may be performed simultaneously, or a new step may be added.

[0083] The step of comparing the interpretation index of the first order (S331) may be a step in which the processor determines whether the interpretation index of the first order of the intermediate code and the previously stored reference code is the same. In the above example where the intermediate codes are [8] and [4, 0], the processor can extract a reference code that has the same interpretation index of the first order as 8, which is the interpretation index of the first order of [8]. Additionally, the processor can extract a reference code that has the same interpretation index of the first order as 4, which is the interpretation index of the first order of [4, 0].

[0084] Additionally, the processor can extract a reference code based on the number of indicators (S332). In the above example where the intermediate codes are [8] and [4, 0], the processor can extract a reference code with 1 included indicator to extract a reference code that is similar to [8]. Additionally, the processor can extract a reference code with 2 included indicators to extract a reference code that is similar to [4, 0].

[0085] In summary, to determine the interpretation code corresponding to [8], the processor can extract a reference code in which the first interpretation index is 8 and the number of included interpretation indexes is 1. Additionally, to determine the interpretation code corresponding to [4, 0], the processor can extract a reference code in which the first interpretation index is 4 and the number of included interpretation indexes is 2.

[0086] Accordingly, the processor may determine the reference code Spike 8, which has the highest similarity to [8] among the reference codes, as the interpretation code for [8]. Additionally, the processor may determine the 40 / 04 Codes, which have the highest similarity to [4, 0] among the reference codes, as the interpretation code for [4, 0]. The determined interpretation codes are examples only and may vary depending on the type of psychological test, etc.

[0087]

[0088] Figures 6 to 8 are diagrams illustrating example results of a psychological test interpretation method.

[0089] Referring to FIG. 6, the processor can extract interpretation indicators for parameters whose scale is greater than or equal to a threshold value based on psychological test data. In the example of FIG. 6, the processor can calculate parameters whose scale is greater than or equal to a threshold value of 70 and interpretation indicators for each parameter. As a specific example, the processor can extract an interpretation indicator of 1 for parameter D and interpretation indicators of 0 for parameters Hy and Pd.

[0090] The processor can distinguish interpretation indicators for the extracted parameters according to certain criteria. In the example of Fig. 6, since the extracted interpretation indicators do not differ in value by more than 5, the processor may not distinguish between 1, 0, and 0.

[0091] After the separation process, the processor can generate at least one intermediate code based on the interpretation indicators. Since 1, 0, and 0 are not separated from each other, the processor can generate one intermediate code. In this case, the processor can generate an intermediate code of [1, 0, 0] or [1, 0] with the value of 1, which has the largest numerical value, as the first order.

[0092] The processor can compare the generated intermediate code with a previously stored reference code and determine the reference code with the highest similarity as the interpretation code. Accordingly, the processor can determine the reference code of 10 / 01 as the interpretation code in the example of FIG. 6 based on the intermediate code of [1, 0, 0] or [1, 0].

[0093] The processor can provide interpretation content based on interpretation codes and subject information. For example, the processor can acquire interpretation content by inputting interpretation codes and subject information into a pre-trained artificial intelligence model.

[0094] For example, the provided interpretation content states: "According to the provided results and interpretation, the test taker scores highly on clinical scales associated with psychological distress. The most significantly high scales include depression (D), hysteria (Hy), antisocial behavior (Pd), and hypochondria (Hs). These results suggest that the individual may be experiencing symptoms of depression, concerns about physical problems, anxiety regarding relationship or societal expectations, and generalized anxiety. The validity interpretation of the test indicates a defensive profile, meaning that the individual recognizes their difficulties but attempts to deny or suppress them, and thus lives in a state of chronically difficult adaptation. The 23 cotype is known as 'smiling depression' and corresponds to individuals who strictly control their emotions while exhibiting depression, fatigue, and vague somatic discomfort. Combining the validity results with high clinical scales allows for the estimation of potential diagnoses. At the most notable highs, it is reasonable to consider a diagnosis of Major Depressive Disorder (MDD) or Bipolar II Disorder. Additionally, the disorder scales may be a factor indicating a somatic symptom disorder. However, these It may be, but is not limited to, "It is important to contextualize the individual's specific profile, background, and presented problems through clinical interviews and other assessments for integrated evaluation of interpretation."

[0095]

[0096] Referring to FIG. 7, the processor can extract interpretation indicators for parameters whose scale is greater than or equal to a threshold value based on psychological test data. In the example of FIG. 7, the processor can calculate parameters whose scale is greater than or equal to a threshold value of 70 and interpretation indicators for each parameter. As a specific example, in the example of FIG. 7, the processor may not have any parameters or interpretation indicators to extract. However, the processor can extract parameters and interpretation indicators for the first graph (parameters related to psychological test validity) as well as for the second graph (parameters related to psychological state) of FIG. 4. The detailed process for this is omitted as it may be redundant.

[0097] The processor can provide interpretation content based on interpretation codes and subject information. For example, the processor can obtain interpretation content by inputting interpretation codes and subject information into a pre-trained artificial intelligence model. In the example of FIG. 7, data corresponding to the absence of an interpretation code can be input into the artificial intelligence model.

[0098] For example, the provided interpretation content may be, but is not limited to, "According to the provided results, the validity interpretation suggests an generally acceptable scale regarding response consistency, but requires some caution regarding the possibility of denial or downplaying of problems. In terms of excessive clinical scales, there are no T-scores of 65 or higher. The only notable aspect is the presence of a K+ profile, which reflects defensiveness regarding reporting and may indicate an unconscious denial of problems or an overly controlled individual. However, without additional information on the patient's background, reported problems, and other assessment results, there are limitations in providing a fragmentary diagnostic impression based solely on MMPI-2 results. A holistic assessment considering contextual factors must be performed to comprehensively understand the patient's mental health status."

[0099]

[0100] Referring to FIG. 8, the processor can extract interpretation indicators for parameters whose scale is greater than or equal to a threshold value based on psychological test data. In the example of FIG. 8, the processor can calculate parameters whose scale is greater than or equal to a threshold value of 75 and interpretation indicators for each parameter. Specifically, the processor can extract an interpretation indicator for parameter Hs as 10, an interpretation indicator for parameter Hy as 5, an interpretation indicator for Pd as 8, an interpretation indicator for parameter Pa as 16, and an interpretation indicator for parameter Sc as 10. The threshold value in FIG. 8 is different from the threshold value in FIG. 6, and this can be set based on subject information, etc.

[0101] The processor can classify interpretation indicators for the extracted parameters according to certain criteria. In the example of FIG. 8, the extracted interpretation indicators are 16, 10, 10, 8, and 5, and there are interpretation indicators that differ from each other by 5 or more. Therefore, the processor can classify the extracted interpretation indicators as

[0016] , [10, 8], [5] or

[0016] ,

[0010] , [8, 5].

[0102] After the separation process, the processor may generate at least one intermediate code based on the interpretation indicators. At this time, the processor may generate intermediate codes

[0016] , [10, 8], [5] or

[0016] ,

[0010] , [8, 5] with the largest numerical value as the first.

[0103] The processor can compare the generated intermediate code with a previously stored reference code and determine the reference code with the highest similarity as the interpretation code. Accordingly, the processor can determine the interpretation code for

[0016] as Spike 16, the interpretation code for [10, 8] as 10 / 8, and the interpretation code for [5] as 50 / 05 based on the intermediate codes of

[0016] , [10, 8], [5] or

[0016] ,

[0010] , [8, 5].

[0104] The processor can provide interpretation content based on interpretation codes and subject information. For example, the processor can acquire interpretation content by inputting interpretation codes and subject information into a pre-trained artificial intelligence model.

[0105] For example, the provided interpretation content states: "Based on the provided results, various interpretations may be made regarding the examinee's validity, clinical scales, and overall profile. VRIN, TRIN, and F scores indicate consistent responses and represent valid test results. However, the F score may also reflect the recognition of unique experiences or attitudes in adolescents, creative individuals, or those with moderate impairments. Elevated clinical scales include Pa (91), Hs (85), Sc (85), Pd (83), Hy (80), D (77), Mf (71), Pt (69), and Ma (69). The examinee's profile identifies Spike 16, which possesses major characteristics indicating high sensitivity, suspicion, and paranoia. This can lead to an argumentative personality, moral justification, and a rigid worldview, resulting in difficulties in interpersonal relationships. Code 10 / 8 is also identified in the results. These individuals often manifest physical discomfort as a defense mechanism against psychosis or delusions. They may lack social and sexual adaptation, lack trust, and feel alienated from others. Rarely occurring The 50 / 05 code type indicates sensitivity and thoughtfulness in both men and women. Men with this profile tend to be analytical, whereas women may be more practical, direct, and assertive. They share a tendency toward worry, indecisiveness, self-criticism, guilt, and easily hurt emotions. Additionally, the Paranoid Valley profile highlights the potential for the applicant to have serious trust issues, suspicion, and a distorted perception of reality. The combination of these traits and the profile paints a picture of an individual with high sensitivity, suspicion, and potential paranoia. They may struggle with interpersonal relationships and may have a somewhat distorted view of reality.

[0106]

[0107] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0108] Although the embodiments have been described above with reference to limited examples and drawings, those skilled in the art can make various modifications and variations from the description above. For example, suitable results can be achieved even if the described techniques are performed in a different order than described, and / or the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0109] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

1. A method for interpreting a psychological test performed by at least one processor, A step of acquiring psychological test data including a scale for multiple parameters; A step of extracting a parameter whose scale is greater than or equal to a first threshold value and an interpretation indicator for the parameter whose scale is greater than or equal to a threshold value based on the above psychological test data; A step of determining an interpretation code based on a parameter and an interpretation indicator in which the above scale is greater than or equal to the above first reference value; and A step comprising providing interpretation content based on the above interpretation code Psychological test interpretation methods.

2. In Paragraph 1, The above psychological test data is, It includes a first group parameter related to psychological test validity, a scale for the first group parameter, and a first graph for the first group parameter, and A second group parameter related to a psychological state, a scale for said second group parameter, and a second graph for said second group parameter. Psychological test interpretation methods.

3. In Paragraph 2, A step of determining the validity of a psychological test based on the profile of the first graph above; and A step of determining a psychological state type based on the profile of the second graph above is further included. Psychological test interpretation methods.

4. In Paragraph 1, The above interpretation indicator is the difference between the scale corresponding to each parameter and the above first reference value. Psychological test interpretation methods.

5. In Paragraph 1, A step of extracting a parameter whose scale is less than a second threshold value and an interpretation indicator for the parameter whose scale is less than the second threshold value based on the above psychological test data; and The method further includes the step of determining an interpretation code based on a parameter whose scale is less than the second reference value and an interpretation indicator for the parameter less than the second reference value, and The interpretation indicator for parameters below the second reference value is the difference between the scale corresponding to each parameter and the second reference value. Psychological test interpretation methods.

6. In Paragraph 1, The step of determining the above interpretation code is, A step of classifying interpretation indicators for extracted parameters according to certain criteria; A step of generating at least one intermediate code based on distinguished interpretation indicators; and A step comprising determining the reference code with the highest similarity as the interpretation code based on the similarity of the intermediate code to the previously stored reference code. Psychological test interpretation methods.

7. In Paragraph 6, The step of generating the intermediate code above is a step of generating the intermediate code by setting the interpretation indicator having the largest value among the distinguished interpretation indicators as the first order. Psychological test interpretation methods.

8. In Paragraph 7, The step of determining the reference code with the highest similarity as the interpretation code is, A step of comparing the interpretation indicator of the first sequence of the above intermediate code and the above previously stored reference code, and A step of extracting a reference code containing a number of interpretation indicators equal to or greater than the number of interpretation indicators included in the intermediate code among the previously stored reference codes. Psychological test interpretation methods.

9. In Paragraph 1, The step of providing the above interpretation content is the step of providing an interpretation guide corresponding to the above interpretation code. Psychological test interpretation methods.

10. In Paragraph 1, The step of providing the above interpretation content is, The step of inputting the above interpretation code and subject information into a pre-trained artificial intelligence model, and A step comprising generating the interpretation content based on the result value of the artificial intelligence model. Psychological test interpretation methods.

11. In Paragraph 1, The above first reference value is set based on subject information, and The above subject information includes at least one of the subject's age, gender, place of residence, occupation, and previous medical history. Psychological test interpretation methods.

12. A computer program stored on a computer-readable recording medium to execute the psychological test interpretation method described in any one of paragraphs 1 through 11.

13. Communication module; Memory; and It includes at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, and The above at least one program is, Acquire psychological test data including scales for multiple parameters, and Based on the above psychological test data, parameters for which the scale is greater than or equal to a first threshold value and interpretation indicators for parameters for which the scale is greater than or equal to a threshold value are extracted, and Determining an interpretation code based on a parameter whose scale is greater than or equal to the first reference value and the interpretation indicator, and Includes commands for providing interpretation content based on the above interpretation code Computing device.

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