Apparatus and method for providing episodic memory training for improving cognitive ability

Abstract

An apparatus and method for providing episodic memory training for improving cognitive ability are provided. The apparatus comprises: a memory storing at least one process for providing episodic memory training for improving cognitive ability; and a processor performing an operation on the basis of the process. The processor provides episodic data to a user terminal every preset first period, provides a first test for short-term memory evaluation for the episodic data to the user terminal when completion confirmation for the episodic data is received from the user terminal, provides a second test for long-term memory evaluation on the episodic data to the user terminal after a preset second period elapses from the time when the first test is completed, and adjusts the difficulty of the episodic memory training of a user on the basis of a first result for the first test and a second result for the second test.

Description

Device and method for providing episodic memory training for improving cognitive ability

[0001] The present disclosure relates to an apparatus and method for providing episodic memory training for improving cognitive ability.

[0002] Episodic memory refers to the memory of an individual's experiences and autobiographical events. It is characterized by the inclusion of the context in which the event occurred, such as the time, place, and situation.

[0003] Existing methods for evaluating and training episodic memory have mostly involved presenting a few words to elderly subjects and having them recall them after a certain period of time. However, because this training method makes it difficult to evaluate information regarding the content of events—a sub-component of episodic memory—there are limitations in comprehensively identifying and stimulating the patterns of episodic memory decline associated with the normal aging process.

[0004] In addition, computerized cognitive tasks are a method of evaluating and training all sub-elements of episodic memory by presenting various objects in different locations within a two-dimensional space on a screen and determining what the objects are, where they are located, and how many times they appear on the screen. Although these computerized cognitive tasks evaluate all sub-elements of episodic memory, there is a problem in that the location memory task for objects is out of touch with the cognitive domains that subjects utilize in real life, resulting in reduced practical utility.

[0005] The embodiments disclosed in this disclosure are intended to provide an apparatus and method for providing episodic memory training for improving cognitive ability.

[0006] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below.

[0007] An episodic memory training device for improving cognitive ability according to one aspect of the present disclosure for achieving the technical problem described above includes a memory storing at least one process for providing episodic memory training for improving cognitive ability and a processor that performs operations based on said process, wherein the processor provides episodic data to a user terminal at a predetermined first period, and when confirmation of completion of said episodic data is received from the user terminal, provides a first test for short-term memory evaluation of said episodic data to the user terminal, and after a predetermined second period has elapsed from the time when said first test is completed, provides a second test for long-term memory evaluation of said episodic data to the user terminal, and can adjust the difficulty of said episodic memory training of the user based on a first result of said first test and a second result of said second test.

[0008] Additionally, a method for providing episodic memory training for improving cognitive ability according to another aspect of the present disclosure for achieving the technical problem described above may include the steps of: providing episodic data to a user terminal at predetermined first periods; receiving confirmation of completion of the episodic data from the user terminal; providing a first test for short-term memory evaluation of the episodic data to the user terminal; providing a second test for long-term memory evaluation of the episodic data to the user terminal after a predetermined second period has elapsed from the time when the first test is completed; and adjusting the difficulty level of the user's episodic memory training based on a first result of the first test and a second result of the second test.

[0009] In addition, a computer program stored on a computer-readable recording medium for executing a method for implementing the present disclosure may be further provided.

[0010] In addition, a computer-readable recording medium for recording a computer program for executing a method for implementing the present disclosure may be further provided.

[0011] According to the aforementioned means for solving the problem of the present disclosure, the actual far-transfer function of patients with mild cognitive impairment can be improved through training that focuses on stories containing situations and contexts, rather than the conventional memorization of object names and locations. This can lead to functional improvement not only in the trained area but also in various aspects of daily life.

[0012] In addition, specific memory areas can be strengthened by providing questions in a consistent format, such as sentence structure and form, and then conducting training while changing parts of the questions.

[0013] In addition, training using stories with themes and narratives can stimulate the interest of patients with mild cognitive impairment and increase the completion rate.

[0014] Furthermore, it can be utilized in the field of cognitive rehabilitation and management for degenerative brain diseases such as stroke, Parkinson's disease, and dementia. It can also be extended beyond just dementia to various disease groups exhibiting symptoms of cognitive decline (such as geriatric depression, multiple sclerosis, and Parkinson's disease). This means it can be applied not only to geriatric diseases but also to disease groups across diverse age groups exhibiting cognitive decline symptoms, such as ADHD.

[0015] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0016] FIG. 1 is a diagram schematically illustrating an episodic memory training provision system for improving cognitive ability according to one embodiment of the present disclosure.

[0017] FIG. 2 is a flowchart of a method for providing episodic memory training for improving cognitive ability according to one embodiment of the present disclosure.

[0018] FIG. 3 is a diagram illustrating short-term memory evaluation and long-term memory evaluation for episodic memory training according to one embodiment of the present disclosure.

[0019] FIG. 4 is a diagram illustrating a case in which the time interval between short-term memory evaluation and long-term memory evaluation according to one embodiment of the present disclosure is utilized as a disruptive stimulus.

[0020] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms “part, module, component, block” as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of “parts, modules, components, blocks” may be implemented as a single component, or a single “part, module, component, block” may include a plurality of components. Throughout the specification, when a part is described as being “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection via a wireless communication network.

[0021] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0022] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0023] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0024] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0025] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0026] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.

[0027] Prior to the explanation, the meanings of the terms used in this specification are briefly explained. However, since the explanation of terms is intended to aid in understanding this specification, it should be noted that unless explicitly stated as a limiting factor, they are not used to limit the technical scope of this disclosure.

[0028] In this specification, the term "device" includes all various devices capable of performing computational processing and providing results to a user. For example, a device may include a computer, a server device, and a portable terminal, or may take the form of any one of these.

[0029] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0030] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0031] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0032] Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0033] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined operation rules or artificial intelligence models configured to perform a desired characteristic (or objective) are created by a basic artificial intelligence model being trained using a number of training data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0034] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights 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. The artificial neural network may include a Deep Neural Network (DNN), such as a Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), or Deep Q-Networks, but is not limited to the examples mentioned above.

[0035] The processor can create a neural network, train (or learn) a neural network, perform operations based on received input data, generate an information signal based on the results of the operation, or retrain the neural network.

[0036] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning Machine), ESN (Echo It will be understood by a person skilled in the art that any neural network may be included, but is not limited to, State Network, Deep Residual Network, Differential Neural Computer, Neural Turning Machine, Capsule Network, Kohonen Network, and Attention Network.

[0037] According to an exemplary embodiment of the present disclosure, the processor comprises 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 (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, Time-Series Forecasting, Optimization for ResNet Data Intelligence, Various artificial intelligence structures and algorithms, such as recommendation and data creation, may be used, but are not limited thereto.

[0038] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0039] FIG. 1 is a diagram schematically illustrating an episodic memory training provision system for improving cognitive ability according to one embodiment of the present disclosure.

[0040] Referring to FIG. 1, an episodic memory training providing system (1) (hereinafter, system) for improving cognitive ability according to one embodiment may include an episodic memory training providing device (10) and a user terminal (20). However, in some embodiments, the system (1) may include fewer or more components than the components shown in FIG. 1.

[0041] A system (1) according to one embodiment of the present disclosure can help improve a patient's cognitive ability by providing episodic memory training to a user at regular intervals (e.g., one day), evaluating the results of the patient's training performance for each period, and adjusting the difficulty of the training according to the evaluation results.

[0042] The episodic memory training device (10) may have episodic data of various categories and provide it to the user so that the user can perform training.

[0043] In this case, the anecdotal data may be audio data containing recorded stories with themes and narratives categorized by category, or text data in which the stories are typed.

[0044] In addition, anecdotal data can include both data containing long stories that exceed a certain time and data containing short stories that do not exceed a certain time.

[0045] The episodic memory training device (10) provides episodic memory training as a digital therapeutic, and can enhance the training through various types of disruptive stimuli (memory interference).

[0046] The user terminal (20) may refer to a terminal device of a user (patient) who uses the episodic memory training service through a service platform. The patient may use the service in the form of an online web or app by installing a service application (program) provided by the episodic memory training device (10) on the user terminal (20). However, it is not limited thereto, and the user terminal (20) may be a terminal device used by a medical professional or a terminal device provided in a hospital.

[0047] The user terminal (20) can perform episodic memory training at regular intervals (e.g., daily) through a service application. Specifically, it can listen to or read episodic data provided through the service application daily. And, once listening to or reading the episodic data is completed, it can perform a short-term memory evaluation of the episodic data daily, and then, after a certain period of time has passed, perform a long-term memory evaluation of the episodic data.

[0048] Here, short-term memory assessment may be a test to evaluate how much of a story is remembered immediately after listening to or reading it, or after a few minutes have passed.

[0049] Long-term memory assessment may be a test designed to evaluate how much of a story is remembered a few days after listening to or reading it.

[0050] The user terminal (20) may include information processing means such as a computer, a processor such as a control unit, a shooting means such as a camera, an input / output means including a touch screen, and any device including a communication function. That is, any device such as a smartphone, tablet, PDA, laptop, desktop, etc., can be applied.

[0051] Referring to FIG. 1, the episodic memory training providing device (10) may include a communication unit (11), a memory (12), and a processor (13). However, in some embodiments, the episodic memory training providing device (10) may include fewer or more components than the components shown in FIG. 1.

[0052] The communication unit (11) may include one or more modules that enable wireless or wired communication between the episodic memory training providing device (10) and the user terminal (20), between the episodic memory training providing device (10) and an external device (not shown), and between the episodic memory training providing device (10) and a communication network. For example, it may include at least one of a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0053] Various types of communication networks may be used, for example, wireless communication methods such as WLAN (Wireless LAN), Wi-Fi, Wibro, Wimax, and HSDPA (High Speed ​​Downlink Packet Access), or wired communication methods such as Ethernet, xDSL (ADSL, VDSL), HFC (Hybrid Fiber Coax), FTTC (Fiber to The Curb), and FTTH (Fiber to The Home).

[0054] Meanwhile, the communication network is not limited to the communication method presented above, and may include all forms of communication methods that are widely known or will be developed in the future, in addition to the communication method described above.

[0055] The wired communication module may include various wired communication modules such as a Local Area Network (LAN) module, a Wide Area Network (WAN) module, or a Value Added Network (VAN) module, as well as various cable communication modules such as USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).

[0056] In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, the wireless communication module may include wireless communication modules that support various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, and 6G.

[0057] A short-range communication module is for short-range communication and can support short-range communication by using at least one of Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, NFC (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technologies.

[0058] Memory (12) may store at least one process for providing episodic memory training for improving cognitive ability.

[0059] The memory (12) can store data supporting various functions of the episodic memory training providing device (10) and programs for the operation of the processor (13), and can store input / output data (e.g., music files, still images, videos, etc.), and can store a number of application programs (or applications) running on the episodic memory training providing device (10), data for the operation of the episodic memory training providing device (10), and instructions. At least some of these application programs can be downloaded from an external server via wireless communication.

[0060] The memory (12) may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory, magnetic disk, and optical disk. Additionally, the memory (12) may be a database that is separated from the episodic memory training providing device (10) but connected via wired or wireless means. Alternatively, the database may be included in the episodic memory training providing device (10) as an individual component with the memory (12).

[0061] The processor (13) may perform the aforementioned operation using a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of components within the episodic memory training providing device (10), and the data stored in the memory. At this time, the memory (12) and the processor (13) may each be implemented as separate chips. Alternatively, the memory (12) and the processor (13) may be implemented as a single chip.

[0062] In addition, the processor (13) can control one or a combination of the components described above in order to implement various embodiments according to the present disclosure described in FIGS. 2 to 4 below on the episodic memory training providing device (10).

[0063] Hereinafter, with reference to FIGS. 2 to 4, a method for providing episodic memory training to improve cognitive ability to a patient with impaired cognitive ability will be described in detail.

[0064] FIG. 2 is a flowchart of a method for providing episodic memory training for improving cognitive ability according to one embodiment of the present disclosure.

[0065] FIG. 3 is a diagram illustrating short-term memory evaluation and long-term memory evaluation for episodic memory training according to one embodiment of the present disclosure.

[0066] FIG. 4 is a diagram illustrating a case in which the time interval between short-term memory evaluation and long-term memory evaluation according to one embodiment of the present disclosure is utilized as a disruptive stimulus.

[0067] Referring to FIG. 2, the processor (13) of the episodic memory training providing device (10) can provide episodic data to the user's terminal (20) at each preset first period (S210).

[0068] As mentioned above, anecdotal data may be audio data in which a story with a theme and plot is recorded, or text data in which the story is typed.

[0069] Such anecdotal data can be classified into Type 1 data and Type 2 data depending on the length of the story.

[0070] Type 1 data may refer to anecdotal data in which the length of the story does not exceed a preset time. Type 2 data may refer to anecdotal data in which the length of the story exceeds a preset time.

[0071] For example, if the length of the story exceeds 3 minutes, it may be classified as Type 2 anecdotal data, and if the length of the story is up to 3 minutes, it may be classified as Type 1 anecdotal data.

[0072] Additionally, the first period may refer to the cycle in which the user performs episodic memory training. For example, if the first period is set to 1 day, the user may receive one episodic data item each day and perform training through listening or reading.

[0073] According to an embodiment, the processor (13) can determine the type of anecdotal data provided to the user based on the user's training difficulty.

[0074] The processor (13) can determine the type of anecdotal data to be provided in the next cycle according to the initial difficulty level set by the AI-based model or the difficulty level adjusted according to the evaluation results measured through the user's training performance.

[0075] In this case, the initial difficulty level may be applied to user training by default until the difficulty is adjusted. Since the difficulty is not adjusted until the results of the long-term memory assessment for the first cycle are generated, anecdotal data can be provided and training conducted using the initial difficulty level until that point.

[0076] AI-based models can determine a training difficulty level suitable for the user by analyzing personal information such as the user's age and gender, and health information such as the user's disease status and cognitive ability.

[0077] In this case, the AI-based model can be trained using other users' personal information, health information, and training difficulty as input data, and the training results as correct answer data.

[0078] According to an embodiment, the processor (13) can determine the type of anecdotal data provided to the user based on the selection of the user terminal (20). The user may perform training with a long story or training with a short story according to their preference.

[0079] Next, the processor (13) of the episodic memory training providing device (10) can receive confirmation of the completion of episodic data from the user terminal (20) (S220).

[0080] Next, the processor (13) of the episodic memory training providing device (10) can provide a first test for short-term memory evaluation of episodic data to the user terminal (20) (S230).

[0081] As described above, short-term memory assessment may be a first test to evaluate how much of a story is remembered immediately after listening to or reading the story, or after a few minutes have elapsed.

[0082] When the user completes checking (listening or reading) the provided anecdotal data at each first period, the user terminal (20) can send a confirmation completion message to the anecdotal memory training providing device (10).

[0083] The episodic memory training providing device (10) can provide a first test for evaluating the user's short-term memory regarding the corresponding episodic data to the user terminal (20).

[0084] Referring to FIG. 3, episodic memory training can be performed at each first period (e.g., every day). When the user checks episodic data A during the first period (A), which is day 1, a short-term memory evaluation of episodic data A can be performed at the time when the check is completed (A-1). When the user checks episodic data B during the second period (B), which is day 2, a short-term memory evaluation of episodic data B can be performed at the time when the check is completed (B-1). In this way, short-term memory evaluations of episodic data C, D, C, E, and F can be performed.

[0085] Referring again to FIG. 2, the processor (13) of the episodic memory training providing device (10) may provide a second test for long-term memory evaluation of the episodic data to the user terminal (20) after a preset second period has elapsed from the time when the first test is completed (S240).

[0086] Here, the second period may be set to be longer than the first period. For example, the second period may be set to three days. As described above, since the long-term memory evaluation is a second test to evaluate how much of the story is remembered after a few days have elapsed since listening to or reading the story, the second period is set to be longer than the first period.

[0087] Referring to FIG. 3, a long-term memory evaluation of anecdotal data A may be performed at a time point (A-2) after a second period (e.g., 3 days) has passed since a short-term memory evaluation of anecdotal data A was performed (A-1). A long-term memory evaluation of anecdotal data B may be performed at a time point (B-2) after a second period (e.g., 3 days) has passed since a short-term memory evaluation of anecdotal data B was performed (B-1). In this manner, long-term memory evaluations of anecdotal data C, D, C, E, and F may be performed.

[0088] At this time, since the user is simultaneously performing a short-term memory evaluation for each anecdotal data according to the first period, a short-term memory evaluation for anecdotal data D can be performed at the time (A-2) when a long-term memory evaluation for anecdotal data A is performed, and a short-term memory evaluation for anecdotal data E can be performed at the time (B-2) when a long-term memory evaluation for anecdotal data B is performed.

[0089] In addition, the first test for short-term memory evaluation and the second test for long-term memory evaluation can be conducted in various types.

[0090] According to an embodiment, the first and second tests may be performed in a form that provides a plurality of questions related to the story of the anecdotal data and receives responses to measure the accuracy rate.

[0091] According to an embodiment, the first and second tests may be performed by requesting a user to recall and speak about the relevant anecdotal data, and measuring the similarity between the recorded recall content and the relevant anecdotal data. In this case, the voice data containing the recall content is converted into text data, and the similarity can be calculated based on an artificial intelligence-based algorithm to determine how similar the converted text data is to the source text data of the anecdotal data.

[0092] Next, the processor (13) of the episodic memory training providing device (10) can adjust the difficulty of the user's episodic memory training based on the first result of the first test and the second result of the second test (S250).

[0093] According to an embodiment, the processor (13) can adjust the difficulty level based on the correct answer rate of the first test (first result) and the correct answer rate of the second test (second result). At this time, the processor (13) can adjust the difficulty level upward by determining that the cognitive function is better when the correct answer rates of the first test and the second test are higher. Additionally, the processor (13) can adjust the difficulty level upward by further considering the ratio of the correct answer rate of the second test to the correct answer rate of the first test, determining that the cognitive function is better when the ratio value is higher.

[0094] According to an embodiment, the processor (13) can adjust the difficulty level based on the similarity of the first test (first result) and the similarity of the second test (second result). At this time, the processor (13) can adjust the difficulty level upward by determining that the cognitive function is better when the similarity of the first test and the similarity of the second test are greater. Additionally, the processor (13) can adjust the difficulty level upward by further considering the ratio of the similarity of the second test to the similarity of the first test and determining that the cognitive function is better when the ratio value is larger.

[0095] According to an embodiment, the processor (13) can generate a first result and a second result by assigning a higher evaluation weight when the anecdotal data is of type 2 than when it is of type 1.

[0096] As mentioned above, since Type 1 is a short story and Type 2 is a long story, greater weight can be assigned to evaluate cases where training was performed with Type 2 anecdotal data.

[0097] In the present disclosure, the difficulty of episodic memory training can be adjusted by providing various types of distracting stimuli (memory interference). Here, the distracting stimuli may be provided in at least one of the following methods: limiting categories, changing the content of question sentences, or adjusting the time interval between a first test and a second test.

[0098] According to an embodiment, when a disruptive stimulus is provided in a manner that limits the category, the difficulty level can be adjusted through the limitation of the category of the anecdotal data to be provided to the user terminal (20).

[0099] When the difficulty is increased based on the first result and the second result, the processor (13) may limit the category of anecdotal data to be provided after the difficulty is increased to the same category as the category of anecdotal data provided before the difficulty was increased, or to a category of sub-attributes.

[0100] For example, if anecdotal data in a flower-related category has been provided previously, the category of the next anecdotal data to be provided can be decided to be the same flower-related category to increase the difficulty level. By making the categories of the previously provided anecdotal data and the upcoming anecdotal data identical, the user is confused by the appearance and meaning of the flowers, causing interference with memory, thereby enabling more enhanced training.

[0101] According to an embodiment, when a disruptive stimulus is provided in a manner that changes the content of a question sentence, the difficulty level can be adjusted by changing the content between the first question provided during the first test and the second question provided during the second test to the user terminal (20).

[0102] When the difficulty level is increased based on the first result and the second result, the processor (13) may change the content composition differently while maintaining the same sentence structure of the second question relative to the first question in the first and second tests to be performed thereafter. Specifically, while maintaining the sentence structure, only the content of the questions regarding time, person, place, etc., may be changed differently.

[0103] For example, during the first test of the same anecdotal data, the question “What is the protagonist’s name?” can be provided, and during the second test, the question “What is the protagonist’s friend’s name?” can be provided. In this way, memory training can be enhanced by providing the sentences in a consistent format, such as structure and structure, while changing the content of the questions.

[0104] According to an embodiment, if the disruptive stimulus is provided in a manner that adjusts the time interval between the first test and the second test, the difficulty level may be adjusted by adjusting the interval of the second period between the first test and the second test for the anecdotal data to be subsequently provided to the user terminal (20).

[0105] When the difficulty level is increased based on the first result and the second result, the processor (13) can increase the interval of the second period.

[0106] Referring to FIG. 4, after the first and second tests on anecdotal data A have been performed, if the processor (13) decides to increase the difficulty based on the first and second results, the second period may be adjusted from (B-1) to (B-2') to (B-1) to (B-2''). For example, the second period may be adjusted from 3 days to 4 days. Accordingly, the second test on anecdotal data B may be performed at time point B-2'' instead of time point B-2'.

[0107] Although FIG. 2 describes the steps being executed sequentially, this is merely an illustrative explanation of the technical concept of the present embodiment. A person skilled in the art to which the present embodiment belongs can modify and adapt the steps described in FIG. 2 in various ways, such as changing the order or executing them in parallel, without departing from the essential characteristics of the present embodiment. Therefore, FIG. 2 is not limited to a chronological order.

[0108] Meanwhile, in the above description, the steps described in FIG. 2 may be further divided into additional steps or combined into fewer steps according to an embodiment of the present disclosure. Also, some steps may be omitted as necessary, and the order of steps may be changed.

[0109] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0110] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0111] As described above, the disclosed embodiments have been explained with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted restrictively.

Claims

1. A memory storing at least one process for providing episodic memory training for improving cognitive ability; and It includes a processor that performs operations based on the above process, and The above processor is, Anecdotal data is provided to a user's terminal at predetermined first periods, and when confirmation of the anecdotal data is received from the user terminal, a first test for short-term memory evaluation of the anecdotal data is provided to the user terminal, and after a predetermined second period has elapsed from the time the first test is completed, a second test for long-term memory evaluation of the anecdotal data is provided to the user terminal, and the difficulty level of the user's anecdotal memory training is adjusted based on the first result of the first test and the second result of the second test. Device for providing episodic memory training to improve cognitive ability.

2. In Paragraph 1, The above difficulty level is adjusted by limiting the category of anecdotal data to be provided to the user terminal, Device for providing episodic memory training to improve cognitive ability.

3. In Paragraph 2, If the above difficulty level is adjusted upward, the processor, Limiting the category of anecdotal data to be provided after the above upward adjustment to the same category as the category of anecdotal data provided before the above upward adjustment, or to the category of a sub-attribute. Device for providing episodic memory training to improve cognitive ability.

4. In Paragraph 1, The above difficulty level is adjusted through changes in content between the first question provided during the first test and the second question provided during the second test, Device for providing episodic memory training to improve cognitive ability.

5. In Paragraph 4, If the above difficulty level is adjusted upward, the processor, Maintaining the same sentence structure as the second question compared to the first question above, but changing the content composition differently, Device for providing episodic memory training to improve cognitive ability.

6. In Paragraph 1, The above difficulty level is adjusted by adjusting the interval of the second period between the first inspection and the second inspection of the anecdotal data to be provided to the user terminal, and If the above difficulty level is adjusted upward, the processor, Adjusting the interval of the above second period to be longer, Device for providing episodic memory training to improve cognitive ability.

7. In Paragraph 1, The above anecdotal data is classified into first type data and second type data, and Generating the first result and the second result by assigning a higher evaluation weight to the case where the anecdotal data is of the second type than to the case where it is of the first type. Device for providing episodic memory training to improve cognitive ability.

8. In Paragraph 7, The above processor is, Determining the type of anecdotal data according to the above difficulty level, or determining the type of anecdotal data according to the selection of the user terminal, Device for providing episodic memory training to improve cognitive ability.

9. In a method performed by a device, A step of providing anecdotal data to a user's terminal at each pre-set first period; A step of receiving confirmation of completion of the anecdotal data from the user terminal; A step of providing a first test for short-term memory evaluation of the anecdotal data to the user terminal; A step of providing a second test for long-term memory evaluation of the anecdotal data to the user terminal after a preset second period has elapsed from the time when the first test is completed; and A step comprising adjusting the difficulty of the user's episodic memory training based on the first result of the first test and the second result of the second test. Method for providing episodic memory training to improve cognitive ability.

10. A computer-readable recording medium that is combined with a computer, which is hardware, and stores a computer program that executes the method of claim 9.

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