Method and apparatus for providing cognitive training

Abstract

A device for providing cognitive training according to one aspect comprises: a memory in which at least one program is stored; and at least one processor for executing said at least one program, wherein the at least one processor determines the patient's input as correct or incorrect based on a pre-set determination rule based on a first type input word and a second type input word included in the patient's input to a first cognitive training sentence using a correct answer determination model, and provides feedback to the patient based on a pre-set feedback rule based on the first type input word or the second type input word included in the patient's input determined as correct or incorrect using a feedback provision model, and provides a cognitive training evaluation to the patient who has received said feedback.

Description

Technology Field

[0001] The present disclosure relates to a method and apparatus for providing cognitive training. Background Technology

[0002] In general, human cognitive function consists of various sub-components such as attention, memory, judgment, and language ability, and these functions can decline due to causes such as aging, disease, and trauma. Accordingly, various training programs have been proposed to prevent or improve cognitive decline.

[0003] However, conventional cognitive training methods generally provide uniform content to users, which limits their ability to offer appropriate feedback or training paths tailored to individual cognitive abilities or responses. Furthermore, even when some methods collect user response data, they lack the functionality to analyze or incorporate it in real-time, which reduces training efficiency.

[0004] Therefore, there is a growing need for a more sophisticated and personalized method of providing cognitive training that can accurately evaluate the user's current cognitive state and dynamically adjust training content based on the evaluation results. [Prior Art][Patent](Patent 1) Republic of Korea Published Patent No. 10-2025-0055643 (Published April 25, 2025) The problem to be solved

[0005] The invention provides a method and apparatus for providing cognitive training. Additionally, the invention provides a computer-readable recording medium that records a program for executing the above method on a computer. The technical problems to be solved are not limited to those described above, and other technical problems may exist. means of solving the problem

[0006] According to one aspect of the present disclosure, a method for providing cognitive training may be provided, comprising: a step of determining the input of a patient as a correct answer or an incorrect answer according to a pre-set discrimination rule based on a first type input word and a second type input word included in the patient's input to a first cognitive training sentence using a correct answer determination model; a step of providing feedback to the patient according to a pre-set feedback rule based on the first type input word or the second type input word included in the patient's input determined as the correct answer or the incorrect answer using a feedback providing model; and a step of providing a cognitive training evaluation to the patient who has received the feedback.

[0007] An apparatus according to another aspect of the present disclosure may determine whether a patient's input is correct or incorrect based on a first type input word and a second type input word included in the patient's input to a first cognitive training sentence using a correct answer determination model, provide feedback to the patient based on a first type input word or a second type input word included in the patient's input determined to be correct or incorrect using a feedback provision model, and provide a cognitive training evaluation to the patient who has received the feedback.

[0008] A computer-readable recording medium according to another aspect of the present disclosure includes a recording medium that records a program for executing the above-described method on a computer. Effects of the invention

[0009] According to one embodiment of the present invention, by using an artificial intelligence model to communicate with the user in real-time and bidirectionally, it is possible to help improve the user's cognitive function.

[0010] In addition, cognitive reserve can be effectively activated by selectively providing a cognitive training method suitable for the user based on the results of the user's cognitive training performance.

[0011] In addition, by providing cognitive training to the user based on minimal rules, it may be easier to guide the patient's associative flow toward the planning direction of the cognitive training compared to cases where only artificial intelligence models are used.

[0012] The objectives of the present invention are not limited to those mentioned above, and other unmentioned objectives will be clearly understood by those skilled in the art from the description below. Brief explanation of the drawing

[0013] FIG. 1 is a diagram illustrating an example of a method for providing cognitive training according to one embodiment. FIG. 2 is a block diagram illustrating an example of a device for providing cognitive training according to one embodiment. FIG. 3 is a flowchart illustrating an example of a method for providing cognitive training according to one embodiment. FIG. 4 is a diagram illustrating an example of a method for determining a patient's input as a correct or incorrect answer according to one embodiment. FIGS. 5A and 5B are flowcharts illustrating an example of a method for providing feedback to a patient based on determining the patient's input as the correct answer according to one embodiment. FIG. 6 is a diagram illustrating an example of a method for providing feedback to a patient based on determining the patient's input as an incorrect answer according to one embodiment. FIG. 7 is a diagram illustrating an example of a method for providing a cognitive training evaluation to a patient according to one embodiment. Specific details for implementing the invention

[0014] The terms used in the embodiments have been selected to be as close as possible to currently widely used general terms; however, these may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been selected at the applicant's discretion, and in such cases, their meanings will be described in detail in the relevant description section. Therefore, terms used in the specification must be defined not merely by their names, but based on their meanings and the content throughout the specification.

[0015] When a part of the specification 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, terms such as "~ unit" or "~ module" as used in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or as a combination of hardware and software.

[0016] Additionally, terms including ordinal numbers, such as "first" or "second," used in the specification may be used to describe various components, but said components should not be limited by said terms. Such terms may be used for the purpose of distinguishing one component from another.

[0017] The present disclosure will be described in detail below with reference to the attached drawings. Specifically, a method for providing cognitive training according to one embodiment will be described in more detail with reference to FIGS. 1 to 7. However, embodiments may be implemented in various different forms and are not limited to the examples described herein.

[0019] FIG. 1 is a diagram illustrating an example of a method for providing cognitive training according to one embodiment.

[0020] Hereinafter, with reference to FIG. 1, an example of a method for providing cognitive training will be described.

[0021] Referring to FIG. 1, the patient (1) can perform cognitive training using a device (10) that provides cognitive training.

[0022] A device (10) providing cognitive training can provide cognitive training to a patient (1) and provide feedback on the input of the patient (1) performing the provided cognitive training. Here, the input of the patient (1) may include all forms of input, such as voice, text, touch, motion, and biosignals (e.g., brainwaves, gaze, heart rate, etc.).

[0023] In the present disclosure, cognitive training can be provided to a patient (1) using an artificial intelligence model of a device (10) that provides cognitive training.

[0024] For example, a device (10) providing cognitive training can provide cognitive training to a patient (1) and obtain input from the patient (1) performing the provided cognitive training. Additionally, the device (10) providing cognitive training can input the input of the patient (1) obtained as input data for an artificial intelligence model into the artificial intelligence model, analyze the input of the patient (1) to derive feedback as output data for the artificial intelligence model, and provide it to the patient (1).

[0025] Here, an artificial intelligence model refers to a set of machine learning algorithms that use a layered algorithmic structure based on deep neural networks in machine learning technology and cognitive science.

[0026] For example, an artificial intelligence model may be composed of an input layer that receives input signals or data from the outside, an output layer that outputs output signals or data corresponding to the input data, and at least one hidden layer located between the input layer and the output layer, which receives a signal from the input layer, extracts a feature, and transmits it to the output layer. The output layer receives a signal or data from the hidden layer and outputs it to the outside.

[0027] More specifically, the artificial intelligence model in this disclosure may refer to a Large Language Model (LLM), and the Large Language Model is a language model having a vast amount of parameters, meaning an artificial intelligence model capable of understanding and generating human language patterns by learning text data.

[0028] That is, the artificial intelligence model in the present disclosure is an artificial intelligence model capable of understanding and generating human language patterns, and can determine whether sensory words included in the input of the patient (1) are used appropriately, whether the context of the text is natural, whether a predetermined word is used in an appropriate position, and whether various types of words (e.g., nouns, pronouns, numerals, particles, adjectives, determiners, adverbs, verbs, interjection words, etc.) correspond to each other.

[0029] Additionally, the artificial intelligence model described below in the present disclosure may include a correct answer determination model that determines whether the input of the patient (1) is correct or incorrect, a feedback provision model that provides feedback to the patient (1), a general sentence generation model that provides an answer according to the patient (1)'s request, and a basic language model that assists in the patient (1)'s performance of cognitive training. The model that provides feedback to the patient (1) may include a hint generation model that provides a hint to the patient (1) and an association sentence generation model that provides the next association cognitive training to the patient (1).

[0030] Meanwhile, the aforementioned correct answer determination model, feedback provision model (e.g., hint generation model and associative sentence generation model), general sentence generation model and basic language model may be included in a single artificial intelligence model, but are not limited thereto, and at least one artificial intelligence model may be an individual artificial intelligence model.

[0031] The method of providing cognitive training described in the present disclosure is not limited to simply using at least one artificial intelligence model, but also utilizes at least a minimum set of rules for providing cognitive training to the patient (1), thereby maximizing the cognitive training effect on the patient (1). In other words, the present invention can provide cognitive training to the patient by combining at least one artificial intelligence model and a set of rules, that is, at least one artificial intelligence model and a rule-based algorithm.

[0032] Here, the pre-set rules may include, but are not limited to, a pre-set determination rule for determining whether the input of the patient (1) is correct, and a pre-set feedback rule for providing feedback to the patient (1), and details will be described later.

[0033] Therefore, the patient (1) performing cognitive training can receive real-time feedback by communicating bidirectionally with the device (10) providing the cognitive training.

[0035] FIG. 2 is a block diagram illustrating an example of a device for providing cognitive training according to one embodiment.

[0036] Referring to FIG. 2, a device (hereinafter referred to as the 'device') (200) that provides cognitive training may include a communication unit (210), a processor (220), and a memory (230). Only the components related to the embodiment are shown in the device (200) of FIG. 2. Therefore, it is obvious to a person skilled in the art that other general components may be included in addition to the components shown in FIG. 2.

[0037] The communication unit (210) may include one or more components that enable wired / wireless communication with an external server or external device. For example, the communication unit (210) may include a short-range communication unit (not shown) and a mobile communication unit (not shown) for communication with an external server or external device.

[0038] The memory (230) is hardware that stores various data processed within the device (200) and can store a program for processing and controlling the processor (220).

[0039] For example, the memory (230) may store various data, such as the patient's medical data, data evaluating the patient's cognitive function, the patient's previous cognitive training performance data, the patient's cognitive training performance data, cognitive training, training words, the patient's response words, hint data, various types of words such as nouns, pronouns, numerals, particles, adjectives, determiners, adverbs, verbs, interjections, matching data between various types of words, and data generated according to the operation of the processor (220). Additionally, the memory (230) may store an operating system (OS) and at least one program (e.g., a program required for the processor (220) to operate).

[0040] The memory (230) may include RAM (random access memory), such as DRAM (dynamic random access memory) and SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), CD-ROM, Blu-ray or other optical disc storage, HDD (hard disk drive), SSD (solid state drive), or flash memory.

[0041] The processor (220) controls the overall operation of the device (200). For example, the processor (220) can control the input unit (not shown), display (not shown), communication unit (210), memory (230), etc., by executing programs stored in memory (230).

[0042] The processor (220) may be implemented using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), controllers, micro-controller units, microprocessors, and other electrical units for performing functions.

[0043] The processor (220) can control the operation of the device (200) by executing programs stored in memory (230). For example, the processor (220) can perform at least some of the methods for providing cognitive training described with reference to FIGS. 3 through 7.

[0045] FIG. 3 is a flowchart illustrating an example of a method for providing cognitive training according to one embodiment.

[0046] Hereinafter, with reference to FIG. 3, an example of a method for providing cognitive training will be described.

[0047] Referring to FIG. 3, a method for providing cognitive training may include steps 310 to 330. However, it is not limited thereto, and other general operations in addition to those shown in FIG. 3 may be further included in the method for providing cognitive training. Additionally, as described above with reference to FIG. 1 and FIG. 2, at least one of the operations of the flowchart shown in FIG. 3 may be processed by a processor.

[0048] First, in step 310, the processor can determine whether the patient's input is correct or incorrect based on a pre-established discrimination rule, using a correct answer determination model, based on the first type of input words and the second type of input words included in the patient's input to the first cognitive training sentence. Here, the first type of words may refer to noun words and the second type of words may refer to adjective words, but this is for the convenience of explanation and is not limited thereto. That is, the processor can determine whether the patient's input is correct or incorrect based not only on noun words and adjective words, but also on pronouns, numerals, particles, determiners, adverbs, verbs, and interjections included in the patient's input.

[0049] Additionally, the input word refers to a word included in the patient's input, the storage word described below refers to a word stored in memory or other storage device, the generated word refers to a word generated by the processor, and the representative word may refer to a word that is most highly associated with a predetermined word and can represent the predetermined word.

[0050] Accordingly, the first type input word may refer to a specific type of word entered by the patient. Additionally, the first type stored word may refer to a specific type of word already stored in memory or other storage devices. Additionally, the first type generated word may refer to a specific type of word generated by the processor. Furthermore, the first type representative word may refer to a specific type of word capable of representing a predetermined word.

[0051] Here, specific types of words may include nouns, adjectives, pronouns, numerals, particles, determiners, adverbs, verbs, and interjections, as described above; and descriptions regarding Type 2 input words, Type 2 stored words, Type 2 generated words, and Type 2 representative words are omitted as they overlap with the descriptions of Type 1 words.

[0052] For example, the processor may extract a first type input word and a second type input word from at least one word included in the patient's input, generate at least one second type representative word corresponding to the first type input word based on the first type input word using a correct answer determination model, and determine the patient's input as correct or incorrect according to a pre-set determination rule based on at least one second type representative word and the second type input word.

[0053] In addition, the processor can determine whether the patient's input is correct or incorrect based on a pre-set discrimination rule using a correct answer determination model, based on whether the second type input word corresponds to at least one second type representative word.

[0054] In step 320, the processor can use a feedback providing model to provide feedback to the patient according to a pre-set feedback rule based on a first type input word or a second type input word included in the patient's input determined to be correct or incorrect.

[0055] For example, the processor may provide the patient with a second cognitive training sentence as the next cognitive training based on determining the patient's input as the correct answer.

[0056] As one example, the processor may derive a Type 2 stored word corresponding to a Type 1 input word based on determining the patient's input as the correct answer, and provide a Type 2 cognitive training sentence to the patient based on the Type 1 input word and the Type 2 stored word. As another example, the processor may obtain a Type 2 generated word based on the Type 1 input word from an associative sentence generation model based on determining the patient's input as the correct answer, and provide a Type 2 cognitive training sentence to the patient based on the Type 1 input word and the Type 2 generated word.

[0057] Additionally, the processor may obtain at least one first-type generated word corresponding to a second-type input word from a hint generation model based on determining the patient's input as an incorrect answer, and provide a hint sentence to the patient based on association information of at least one first-type generated word and the second-type input word.

[0058] In step 330, the processor can provide a cognitive training evaluation to the patient who received the feedback.

[0059] For example, the processor may use a basic language model to provide a first evaluation of recalling training words provided in cognitive training in the order in which cognitive training was performed, and a second evaluation of recalling training words provided in cognitive training based on cues regarding training words.

[0060] Meanwhile, the processor can determine the difficulty level of cognitive training based on the results of the cognitive training evaluation.

[0061] In other words, the processor can adjust the difficulty of cognitive training by increasing or decreasing the number of times the training is performed based on the results of the cognitive training evaluation. Additionally, the processor can adjust the difficulty of cognitive training by determining the difficulty of the training words. Furthermore, the processor can adjust the difficulty of cognitive training based on the frequency of providing hints, i.e., hint sentences, to the patient, the specificity of the hints, and the like.

[0062] The processor can provide the patient with intensive training, association training, and association training as cognitive training. More specifically, the processor may include intensive training that involves repeatedly learning training words, association training that involves learning words associated with the training words, and association training that involves learning the meanings between the training words.

[0063] As an example, intensive training can be a type of training that strengthens a patient's frontal lobe by repeatedly learning training words. For instance, intensive training may involve repeatedly providing the patient with training words to help them memorize them.

[0064] As another example, association training can be a type of training that strengthens a patient's temporal lobe by learning words associated with a specific word. For instance, association training may involve having the patient input another word or phrase that is sensorially associated with a specific word, phrase, or image.

[0065] As another example, association training can be a type of training that strengthens the patient's parietal lobe by learning the meanings between words related to commercial items. For instance, association training could involve providing the patient with training words and having them tell a story that is semantically connected using those words. Additionally, association training could involve providing the patient with images generated using the training words, enabling them to recall the training words from the provided images.

[0066] In this disclosure, one of the various methods for providing association training to a patient among the various cognitive trainings described above is described in detail.

[0068] Hereinafter, with reference to FIGS. 4 to 7, the method by which the processor provides cognitive training is described in more detail. More specifically, the method by which the processor provides association training is described in more detail.

[0069] First, the processor can determine whether the patient's input is correct or incorrect based on a pre-set discrimination rule, using a correct answer discrimination model, based on the first type input words and second type input words included in the patient's input to the first cognitive training sentence.

[0070] FIG. 4 is a diagram illustrating an example of a method for determining a patient's input as a correct or incorrect answer according to one embodiment.

[0071] Hereinafter, with reference to FIG. 4, an example of a method in which a processor determines a patient's input as correct or incorrect will be described.

[0072] Referring to FIG. 4, the processor can provide a first cognitive training sentence (400) to the patient.

[0073] The processor can provide the patient with a first cognitive training sentence (400) containing a predetermined word.

[0074] For example, the processor can generate a first cognitive training sentence (400) using at least one first type word and at least one second type word. Additionally, the processor can provide the generated first cognitive training sentence (400) to a patient.

[0075] More specifically, referring to the first cognitive training sentence (400) illustrated in FIG. 4, the processor can generate a first cognitive training sentence (400) including first type words such as "monkey" and "buttocks" and second type words such as "red" and "if red," which are conjugated and connected words of the second type word "red," and can provide the generated first cognitive training sentence (400) to a patient.

[0076] Additionally, the processor can generate a first cognitive training sentence (400) based on at least one first-type word and at least one second-type word using a basic language model. That is, the processor can generate a first cognitive training sentence (400) as output data by using at least one first-type word and at least one second-type word as input data to the basic language model.

[0077] The processor can obtain the patient's input (410) for the first cognitive training sentence (400) from the patient.

[0078] For example, the processor may obtain a patient's input (410) of "if it is red, it is an apple" as a response to a first cognitive training sentence (400) from the patient. Additionally, the patient's input (410) may include at least one first type word and at least one second type word.

[0079] More specifically, referring to the patient's input (410) illustrated in FIG. 4, the patient's input (410) may include a first type of input word (420) such as "apple" and a second type of input word (430) such as "red".

[0080] Based on the acquired patient input (410), the processor can determine whether the patient's input (410) is a correct response corresponding to the first cognitive training sentence (400), that is, whether it is a correct answer or an incorrect answer.

[0081] For example, the processor can extract a first type input word (420) and a second type input word (430) among at least one word included in the patient's input (410).

[0082] For example, if the processor contains multiple first-type words in the patient's input (410), it can extract the first-type word most closely associated with the second-type input word (430) as the first-type input word (420). Additionally, if the first-type word is a compound word consisting of two first-type words separated by a space, such as "monkey butt," the processor can extract the compound word "monkey butt" as a single first-type input word (420).

[0083] For example, if a second type input word (430) included in the patient's input (410) is not a base form, the processor can extract the second type input word and normalize it to a base form. More specifically, if a second type input word (430) included in the patient's input (410) is not a base form, such as "if it is red," the processor can extract "if it is red" and normalize it to a base form, such as "red" or "red."

[0084] For example, the processor can use the patient's input (410) as input data for the correct answer determination model to obtain, as output data, the first type input word (420) that is most closely associated with the second type input word (430) among a plurality of first type words. That is, the correct answer determination model may refer to a model that has been trained to calculate the semantic distance between a plurality of words and to derive first type words and second type words that match each other according to the calculated semantic distance.

[0085] The processor can generate at least one second type representative word corresponding to the first type input word (420) based on the first type input word (420).

[0086] For example, the processor can use a first type input word (420) as input data for a correct answer determination model to obtain at least one second type representative word corresponding to the first type input word (420) as output data. That is, the correct answer determination model may refer to a model that has been trained to generate at least one second type representative word that modifies the first type input word (420) in everyday life.

[0087] More specifically, the processor can generate words such as "round," "pretty," "crispy," "delicious," "sour," "fragrant," "smooth," "cool," "red," "fresh," etc. as second-type representative words modifying "apple," which is a first-type input word (420) included in the patient's input (410). Additionally, if the first-type input word included in the patient's input is "tiger," the processor can generate words such as "fierce," "frightening," "agile," "strong," "spirited," "courageous," "imposing," "majestic," "rough," "fast," etc. as second-type representative words modifying "tiger."

[0088] Meanwhile, the processor may generate at least one second-type representative word corresponding to the first-type input word (420) based on the first-type input word (420) according to a prompt, that is, a pre-set generation rule.

[0089] For example, the processor can generate at least one second-type representative word corresponding to the first-type input word (420) according to a pre-set generation rule to generate at least one second-type input word that people generally think of for the first-type input word (420).

[0090] More specifically, the processor can generate words such as "round," "pretty," "crispy," "delicious," "sour," "fragrant," "smooth," "cool," "red," "fresh," etc. as representative words of the second type that modify "apple," which is a first type input word (420) included in the patient's input (410), according to a pre-set generation rule. Additionally, if the first type input word included in the patient's input is "tiger," the processor can generate words such as "fierce," "frightening," "agile," "strong," "spirited," "courageous," "imposing," "majestic," "rough," "fast," etc. as representative words of the second type that modify "tiger."

[0091] The processor can determine whether the patient's input (410) is correct or incorrect based on a pre-set discrimination rule based on at least one second-type representative word and a second-type input word (430).

[0092] For example, the processor can determine whether the patient's input (410) is correct or incorrect based on a pre-set determination rule based on whether the second type input word (430) corresponds to at least one second type representative word using a correct answer determination model.

[0093] For example, a pre-set determination rule may mean a rule that determines whether the patient's input (410) is correct or incorrect depending on whether the second type input word (430) corresponds to at least one second type representative word. In other words, the pre-set determination rule may mean a rule that determines the patient's input (410) as correct if the second type input word (430) corresponds to at least one second type representative word that people typically use when describing the first type input word (420), and determines the patient's input (410) as incorrect if there is no correspondence.

[0094] More specifically, the processor can determine whether the second type input word (430), "if red," corresponds to at least one second type representative word when the second type input word (430) included in the patient's input (410) is "if red," and at least one generated second type representative word is "round," "pretty," "crispy," "delicious," "sour," "fragrant," "smooth," "cool," "red," or "fresh." That is, the processor can determine whether the second type input word (430) is identical or similar to at least one second type representative word. Therefore, the processor can determine the patient's input (410) as the correct answer, as the second type input word (430), "if red," corresponds to the same or similar meaning as a second type representative word such as "red."

[0095] Additionally, the processor can determine the patient's input (410) as the correct answer even if the second type input word is "thin" and the second type representative word is "flat," since "thin" and "flat" can correspond to second type words that are used interchangeably.

[0096] Conversely, when a patient input such as “if red, it’s a tiger” is obtained as an answer to a first cognitive training sentence (400) such as “A monkey’s butt is red. If it’s red?”, the processor can generate at least one second type representative word that modifies the first type input word “tiger,” and can determine whether the patient’s input is correct or incorrect using the generated at least one second type representative word and the second type input word “if red.”

[0097] More specifically, the processor can determine whether the second-type input word "red mask" corresponds to at least one second-type representative word when the second-type input word included in the patient's input is "red mask" and at least one generated second-type representative word is "fierce," "scary," "agile," "strong," "spirited," "courageous," "imposing," "majestic," "rough," or "fast." Therefore, since the second-type input word "red mask" is not identical or similar to all second-type representative words, the processor can determine the patient's input as an incorrect answer.

[0098] By utilizing a correct answer discrimination model together with pre-set discrimination rules, the processor can effectively guide the patient's associative flow in a direction consistent with the training intent, compared to the case where only an artificial intelligence model is used. In other words, the processor can determine the patient's input as correct or incorrect based on pre-set discrimination rules; however, by using the correct answer discrimination model to identify the patient's intent regarding the input, the processor can determine the patient's input as correct even if it is not the correct answer according to the pre-set discrimination rules, provided that the input aligns with the training intent. Here, the pre-set discrimination rules can be configured differently depending on the concept of cognitive training; for example, different discrimination rules can be set according to a concept that evokes a specific mascot character or a concept that evokes a specific product.

[0099] More specifically, if the patient answers "If it's red, it's a car" as a response to the first cognitive training sentence (400), according to the pre-set discrimination rule, people generally do not think of words like "red" as association words for "car," so the patient's input can be determined as an incorrect answer. However, if the concept of the cognitive training is one that evokes a red sports car, the processor can use a correct answer determination model to identify the intent of the patient's input "If it's red, it's a car" and determine whether the patient's input aligns with the concept of the cognitive training, that is, whether it aligns with the training intent. In other words, the processor can use the patient's input as input data for the correct answer determination model and derive a correct or incorrect answer judgment result that considers the patient's input and the concept of the cognitive training as output data. Therefore, in this case, the processor can identify the training intent of the concept that evokes a red sports car and ultimately determine the patient's input "If it's red, it's a car" as the correct answer.

[0100] Conversely, if the patient answers "If it's red, it's a car" as a response to the first cognitive training sentence (400), if only the artificial intelligence model is used, the processor can determine the patient's input as the correct answer because "red car" also exists. However, if the artificial intelligence model and a pre-set discrimination rule are used together, although "red car" may actually exist, the processor can determine the patient's input as the incorrect answer because people generally do not think of words like "red" as association words for "car."

[0101] In this way, the correct answer discrimination model and pre-established discrimination rules are used complementarily depending on the patient's input, the concept of cognitive training, etc., to determine the patient's input as correct or incorrect, and to more effectively guide the patient's associative flow in a direction consistent with the training intention.

[0102] Meanwhile, the processor may use a correct answer determination model to perform at least one of the operations included in the method for determining whether the patient's input (410) described above is correct or incorrect. For example, the correct answer determination model may refer to a model that has been trained to use the patient's input (410) as input data to derive a result determining whether the patient's input (410) is correct or incorrect as output data. That is, the processor may use the correct answer determination model to perform at least one of the operations included in the method for determining whether the patient's input (410) described above is correct or incorrect, even for operations that are not explicitly described as using the correct answer determination model.

[0103] Meanwhile, the processor can provide feedback to the patient according to a pre-set feedback rule based on the first type input word (420) or second type input word (430) included in the patient's input (410) determined to be correct or incorrect, using a feedback provision model.

[0104] For example, a pre-set feedback rule may mean a rule that, when the patient's input (410) is determined to be correct, provides the patient with a second cognitive training sentence using a second type stored word or a second type generated word corresponding to a first type input word, and when the patient's input (410) is determined to be incorrect, provides the patient with a hint sentence using a first type generated word corresponding to a second type input word.

[0105] For example, if the patient's input (410) is determined to be correct, the processor may generate a second cognitive training sentence for performing the next cognitive training based on the first type input word (420) included in the patient's input (410) and provide the second cognitive training sentence to the patient as feedback. For another example, if the patient's input (410) is determined to be incorrect, the processor may generate a hint sentence for the patient to input the correct answer to the first cognitive training sentence (400) based on the second type input word (430) included in the patient's input (410) and provide the hint sentence to the patient as feedback.

[0107] FIGS. 5A and 5B are flowcharts illustrating an example of a method for providing feedback to a patient based on determining the patient's input as the correct answer according to one embodiment.

[0108] Hereinafter, with reference to FIGS. 5a and 5b, an example of a method for providing feedback to a patient based on the processor determining the patient's input as the correct answer will be described.

[0109] First, referring to FIG. 5a, the processor can derive a second type storage word (530) corresponding to a first type input word (520) included in the patient's input (510).

[0110] The processor can derive a second type stored word (530) corresponding to a first type input word (520) based on determining the patient's input (510) as the correct answer.

[0111] For example, the processor may determine whether the first type input word (520) is stored in the database based on the determination that the patient's input (510) is the correct answer. Here, the database is a space capable of storing certain data and may include a certain storage device, a server, or the memory described in FIG. 2.

[0112] Additionally, the processor can derive a second type stored word (530) corresponding to the first type input word (520) from the database based on the determination that the first type input word (520) is stored in the database.

[0113] For example, the processor may derive a second type stored word (530) corresponding to a first type input word (520) from a database based on a pre-set feedback rule. More specifically, the processor may derive a second type stored word (530) from a database that does not overlap with the second type input word, and may derive the second type stored word (530) based on a pre-set feedback rule to derive a second type word (530) that corresponds to a characteristic of the first type input word (520) that the patient can easily perceive, such as the shape, color, movement, taste, sound, or texture, among the second type words that modify the first type input word (520). Additionally, the pre-set feedback rule may be configured so that even if a second type word modifies the first type input word (520), a second type word that modifies the first type input word (520) symbolically or metaphorically is not derived as a second type stored word (530).

[0114] Meanwhile, referring to FIG. 5b, the processor can derive a second type generated word (531) corresponding to the first type input word (521) using an associative sentence generation model (540) based on the determination that the first type input word (521) is not stored in the database. That is, when the first type input word (521) is not stored in the database, the processor can derive a second type generated word (531) corresponding to the first type input word (521) according to a pre-set feedback rule to derive the second type generated word (531) using the associative sentence generation model (540).

[0115] For example, the processor may obtain a second type generated word (531) based on a first type input word (521) from an associative sentence generation model (540) based on the patient's input (510) being determined as the correct answer. That is, the associative sentence generation model (540) may refer to a model that has been trained to generate a second type generated word (531) corresponding to the first type input word (521) as output data, using the first type input word (521) as input data.

[0116] Accordingly, if the first type input word (521) is not stored in the database, the processor can obtain a second type generated word (531) corresponding to the first type input word (521) using an associative sentence generation model (540). Additionally, the processor can store the obtained second type generated word (531) in the database and, in subsequent cognitive training, use the second type generated word (531) stored in the database as a second type stored word (530) to provide feedback to the patient.

[0117] Meanwhile, the second type stored word (530) and the second type generated word (531) may include at least one second type word corresponding to the first type input word (521). More specifically, the second type stored word (530) and the second type generated word (531) may mean at least one second type word that modifies the first type input word (521), and with reference to FIG. 5a and FIG. 5b, the second type stored word (530) and the second type generated word (531) may include second type words such as "red," "sour," "sweet," and "round" that modify "apple," which is the first type input word (521).

[0118] Accordingly, the processor can generate a second cognitive training sentence for performing the next cognitive training using a second type stored word (530) stored in a database or a second type generated word (531) derived using an associative sentence generation model (540).

[0119] The processor can determine, according to a pre-set feedback rule, which of the second type stored word (530) or the second type generated word (531) does not overlap with the second type input word included in the patient's input (510).

[0120] More specifically, the processor may determine, based on a pre-set feedback rule, whether there exists a Type 2 word among the Type 2 stored words (530) or Type 2 generated words (531) that corresponds to the Type 2 input word "red" included in the patient's input (510). Additionally, if there exists a Type 2 word "red" among the Type 2 stored words (530) or Type 2 generated words (531) that corresponds to "red," the processor may randomly select any one of the remaining Type 2 words excluding that Type 2 word, i.e., "red." However, not limited thereto, the processor may select a Type 2 word suitable for the patient among the Type 2 stored words (530) or Type 2 generated words (531) based on the patient's age, gender, cognitive function, previous cognitive training results, difficulty of current cognitive training, etc.

[0121] Accordingly, the processor can provide a second cognitive training sentence to the patient based on the selected second type word and the first type input word (520, 521).

[0122] As one example, the processor may provide a second cognitive training sentence to the patient based on a first type input word (520) and a second type stored word (530). As another example, the processor may provide a second cognitive training sentence to the patient based on a first type input word (5201) and an adjective generating word (531).

[0123] More specifically, if the processor selects "round" from among the second type stored words (530) or second type generated words (531) that does not overlap with the second type input word "red," it can generate a second cognitive training sentence using the first type input word (520) "apple" and the selected second type word "round," and provide the generated second cognitive training sentence to the patient.

[0124] For example, the processor can generate a second cognitive training sentence such as “Apples are round. Now, please tell me what comes to mind when you say round” using the first type input word (520) “apple” and the selected second type word “round”, and can provide the generated second cognitive training sentence as feedback to the patient.

[0125] Meanwhile, the processor may perform at least one of the operations included in the method of providing feedback based on determining the above-described patient input (510) as the correct answer by using an associative sentence generation model. For example, the associative sentence generation model may refer to a model that has been trained to generate a second cognitive training sentence as output data by using a first type input word (520, 521) included in the patient input (510) as input data. That is, the processor may perform at least one of the operations included in the method of providing feedback based on determining the above-described patient input (510) as the correct answer by using an associative sentence generation model, even for operations that are not explicitly described as using an associative sentence generation model.

[0126] FIG. 6 is a diagram illustrating an example of a method for providing feedback to a patient based on determining the patient's input as an incorrect answer according to one embodiment.

[0127] Hereinafter, with reference to FIG. 6, an example of a method for providing feedback to a patient based on the fact that the processor has determined the patient's input to be incorrect is described.

[0128] Referring to FIG. 6, the processor can derive at least one first type generated word (640) corresponding to a second type input word (630) included in the patient's input (610).

[0129] Based on determining that the patient's input (610) is incorrect, the processor can obtain at least one first-type generated word (640) corresponding to a second-type input word (630) according to a pre-set feedback rule from the hint generation model.

[0130] For example, if the patient's input (610) is incorrect, the processor may generate a first-type generated word (640) that can be modified by a second-type input word (630) included in the patient's input (610), derive association information of a word among the first-type generated words (640) that does not overlap with the first-type input word, generate a hint sentence, and provide the hint sentence to the patient according to a pre-set feedback rule to provide the generated hint sentence to the patient. Additionally, the pre-set feedback rule may be configured not to derive association information that is too abstract for the patient to associate, or too specific for the patient to associate too easily. Here, association information may refer to various types of information related to the word, such as the word's category, definition, example sentence, initial consonant, etc., and this is merely an example and is not limited thereto.

[0131] For example, the processor can use a second type input word (630) as input data for a hint generation model to derive at least one first type generation word (640) as output data. That is, the hint generation model may refer to a model that has been trained to generate a first type word corresponding to the second type input word (630), that is, a word that the second type input word (630) can modify.

[0132] Accordingly, the processor can obtain at least one first-type generated word (640), such as “apple,” “rose,” “lips,” “fire truck,” etc., which “red mask” can modify, based on a second-type input word (630), such as “red mask,” included in the patient’s input (610).

[0133] Additionally, the processor can determine any one of the generated first type words (640) that does not overlap with the first type input word “tiger” included in the patient’s input (610).

[0134] The processor can provide a hint sentence to the patient based on association information of at least one first type generated word (640) and a second type input word (630).

[0135] For example, the processor may determine at least one first-type word among the first-type generated words (640) that does not overlap with the first-type input word, and derive association information of the determined first-type word. Additionally, the processor may generate a hint sentence using the association information of the derived first-type word and the second-type input word (630), and provide the generated hint sentence as feedback to the patient.

[0136] More specifically, if the processor selects "apple" which does not overlap with the first type input word "tiger" among at least one first type generated word (640), it can derive "fruit" as the associated information (more specifically, the category among the associated information) of "apple". Additionally, the processor can generate a hint sentence such as "What are some red fruits?" using "fruit," which is the associated information of "apple," and the second type input word "red." Thus, the processor can provide feedback to the patient by providing the generated hint sentence. Meanwhile, for the sake of convenience of explanation, only the method of generating a hint sentence using the category among the associated information of the first type generated word (640) has been described; however, as described above, the method of generating a hint sentence using the definition, example sentence, initial consonant, etc. of the first type generated word (640) included in the associated information can be applied in the same way.

[0137] The processor may obtain input for a first cognitive training sentence again from a patient who has been provided with a hint sentence. Additionally, if the input obtained again from the patient is correct, the processor may generate a second cognitive training sentence and provide it to the patient in the manner described above with reference to FIGS. 5a and 5b, thereby enabling the patient to perform cognitive training. However, the processor may stop the cognitive training if the patient fails to input the correct answer for the first or second cognitive training sentence despite being repeatedly provided with hint sentences.

[0138] In addition, the processor can generate a hint sentence based on the patient's hint request, not only when the patient's input (610) is incorrect, but also provide the generated hint sentence to the patient. That is, the processor can continuously generate a hint sentence based on the patient's input and provide the hint sentence to the patient.

[0139] More specifically, the processor determines whether the patient's input includes expressions requesting a hint, such as "what," "I don't know," "hint," or "well," and if such expressions are included, it can provide a hint sentence to the patient in the manner described above.

[0140] Meanwhile, the processor may use a hint generation model to perform at least one of the operations included in the method of providing feedback based on determining the aforementioned patient input (610) as an incorrect answer. For example, the hint generation model may refer to a model that has been trained to generate a hint sentence as output data using a second type input word (630) included in the patient input (610) as input data. That is, the processor may use the hint generation model to perform at least one of the operations included in the method of providing feedback based on determining the aforementioned patient input (610) as an incorrect answer, even for operations that are not explicitly described as using the hint generation model.

[0141] Meanwhile, the processor can provide a cognitive training evaluation to the patient who received the feedback.

[0142] For example, the processor can obtain input for a first cognitive training sentence from a patient who has been provided with a hint sentence, and if the patient's input obtained again is correct, generate a second cognitive training sentence and provide it to the patient, thereby enabling the patient to perform cognitive training.

[0143] Therefore, the processor can provide a cognitive training evaluation to a patient who has performed cognitive training by receiving various feedback in the method described above.

[0145] FIG. 7 is a diagram illustrating an example of a method for providing a cognitive training evaluation to a patient according to one embodiment.

[0146] Hereinafter, with reference to FIG. 7, an example of a method in which a processor provides a cognitive training evaluation to a patient is described.

[0147] Referring to FIG. 7, the processor can provide a cognitive training evaluation to the patient based on the training words (710) provided in the cognitive training.

[0148] The processor may provide a cognitive training evaluation to the patient based on all training words (710) provided to the patient performing the cognitive training. Here, the cognitive training evaluation may mean an evaluation to determine how well the patient performing the cognitive training can recall the training words (710).

[0149] For example, the processor may use a basic language model to provide a first evaluation of recalling the training words (710) provided in the cognitive training in the order in which the cognitive training was performed, and a second evaluation of recalling the training words (710) provided in the cognitive training based on clues about the training words.

[0150] First, the processor can provide the patient with a first evaluation of recalling the training words (710) provided in the cognitive training in the order in which the cognitive training was performed.

[0151] For example, the processor may provide a first evaluation problem to the patient to recall the training words (710) in the order in which the training words (710) were provided in cognitive training. Additionally, the processor may obtain the patient's input regarding the first evaluation problem and determine whether the patient's input is correct or incorrect.

[0152] More specifically, the processor can generate a first evaluation problem as output data by using the training words (710) used in cognitive training as input data for the basic language model. For example, the processor can provide the patient with a first evaluation problem such as, "Please tell me the training words (710) that you exchanged with me in order during cognitive training." Additionally, the processor can obtain the patient's input regarding the first evaluation problem and determine whether the patient's input is correct or incorrect. For example, if the processor obtains the patient's input such as, "A monkey's butt is red, if it is red it is an apple, and an apple is round," the processor can determine the patient's input as correct. For another example, if the processor obtains the patient's input such as, "A monkey's butt is red, if it is red it is an apple, and so on," the processor can determine the patient's input as incorrect. Additionally, the processor can determine the patient's input as incorrect not only when the patient's input does not contain all training words (710), but also when there is no input from the patient within a set time, or when the order is incorrect.

[0153] The processor may provide the patient with a second evaluation of recalling the training word (710) provided in cognitive training based on the clues for the training word (710).

[0154] For example, the processor may generate a second evaluation problem containing clues for the training word (710) and provide the generated second evaluation problem to the patient.

[0155] More specifically, the processor can generate a second evaluation problem as output data by using the training words (710) used in cognitive training as input data for the basic language model. For example, the processor can provide the patient with a second evaluation problem such as, "Among the training words (710) that you exchanged with me during cognitive training, what is red and round?" Additionally, the processor can obtain the patient's input regarding the second evaluation problem and determine whether the patient's input is correct or incorrect. As an example, if the processor obtains the patient's input "apple," it can determine the patient's input as correct. As another example, if the processor obtains the patient's input "grapefruit," it can determine the patient's input as incorrect.

[0156] Therefore, the processor can evaluate whether the patient has faithfully performed the cognitive training by providing the patient with the first assessment problem and / or the second assessment problem. Additionally, the processor may provide the second assessment problem after providing the patient with the first assessment problem, but is not limited thereto; conversely, the processor may provide the first assessment problem after providing the patient with the second assessment problem.

[0157] Meanwhile, the processor can determine the difficulty level of cognitive training based on the results of the cognitive training evaluation.

[0158] In other words, the processor can adjust the difficulty of cognitive training by increasing or decreasing the number of times cognitive training is performed based on the results of the cognitive training evaluation.

[0159] For example, the difficulty of cognitive training may increase as the number of times the training is performed increases and decrease as it decreases, and the processor can adjust the difficulty of the cognitive training by adjusting the number of times the training is performed based on the patient's cognitive training evaluation results. In other words, the difficulty of the cognitive training can be adjusted according to the number of times the processor provides cognitive training sentences to the patient.

[0160] In addition, the processor can adjust the difficulty of cognitive training by determining the difficulty of the training words. For example, the processor determines the difficulty of the training words to be provided in cognitive training based on the abstractness of the training words themselves and the frequency of use of the training words included in the cognitive training evaluation results, and can adjust the difficulty of the cognitive training using the determined training words. That is, the higher the abstractness of the training words themselves and the lower their frequency of use, the higher the difficulty of the corresponding training words can be determined—i.e., they can be considered difficult training words; and the more difficult (or easier) the training words are, the more difficult (or easier) the cognitive training can become.

[0161] In addition, the processor can adjust the difficulty of cognitive training based on factors such as the frequency of providing hints—specifically, hint sentences—to the patient and the specificity of the hints. For example, the processor can adjust the difficulty of cognitive training by changing the frequency of providing hints to the patient. That is, the difficulty of cognitive training may become harder (or easier) as the frequency of providing hints to the patient increases (or decreases). Furthermore, the processor can adjust the difficulty of cognitive training by controlling the degree of specificity of the hints provided to the patient. That is, the difficulty of cognitive training may become harder (or easier) as the specificity of the hints decreases (or increases). Here, specificity can be determined by how specific the description of the training word is; for instance, if the training word is "tiger" and the hint is "beast," the specificity is determined to be low, while if the hint is "a beast with stripes and yellow and black fur," the specificity is determined to be high.

[0162] More specifically, if the patient's inputs for both the first and second assessments provided to the patient during a pre-set period are determined to be correct, the processor may increase the number of times cognitive training sentences are provided. Additionally, if the patient's inputs for both the first and second assessments provided to the patient during a pre-set period are determined to be incorrect, the processor may decrease the number of times cognitive training sentences are provided. Meanwhile, if at least one of the patient's inputs for both the first and second assessments provided to the patient during a pre-set period is determined to be incorrect, the processor may maintain the number of times cognitive training sentences are provided at the current level.

[0163] Meanwhile, the processor may perform at least one of the operations included in the method of providing a cognitive training evaluation to the patient described above by using a basic language model. That is, the basic language model may refer to a model that has been trained to generate a cognitive training evaluation problem as output data using a training word (710) provided in cognitive training as input data. Additionally, the basic language model may refer to a model that has been trained to derive a result determining whether the patient's input is correct or incorrect as output data using the patient's input regarding the cognitive training evaluation problem provided as input data.

[0164] Meanwhile, the processor may use a general sentence generation model to perform at least one of the operations included in the method of providing cognitive training to the patient described above. That is, the general sentence generation model may refer to a model that has been trained to provide a response to the patient's request as output data, using various inputs from the patient, such as sentence re-request inputs and rule queries, as input data. In other words, the general sentence generation model may refer to a model that has been trained to provide a response to the patient's request overall while the patient performs cognitive training.

[0165] In the method described above in the present disclosure, a device providing cognitive training to a patient can communicate in real time and provide cognitive training to the patient.

[0166] In addition, patients can perform cognitive training anytime and anywhere through a device that provides cognitive training regardless of location.

[0167] In addition, the patient can receive real-time feedback from the cognitive training device while performing cognitive training.

[0168] In addition, the patient's cognitive function can be effectively improved by providing cognitive training tailored to the patient's cognitive function.

[0169] In addition, by using an artificial intelligence model to analyze emotional words and sentence contexts included in the patient's input, accurate feedback on the patient's input can be provided.

[0170] In addition, by providing cognitive training to patients based on minimal rules, it may be easier to guide the patient's associative flow toward the planning direction of the cognitive training compared to cases where only artificial intelligence models are used.

[0171] Meanwhile, the above-described method can be written as a program executable on a computer and can be implemented on a general-purpose digital computer that operates the program using a computer-readable recording medium. In addition, the structure of the data used in the above-described method can be recorded on a computer-readable recording medium through various means. The computer-readable recording medium includes storage media such as magnetic storage media (e.g., ROM, RAM, USB, floppy disk, hard disk, etc.) and optical reading media (e.g., CD-ROM, DVD, etc.).

[0172] A person skilled in the art related to the present embodiment will understand that it may be implemented in modified forms without departing from the essential characteristics of the description above. Therefore, the disclosed methods should be considered in an illustrative rather than a restrictive sense, and the scope of rights is defined in the claims rather than the description above, and should be interpreted to include all differences within the scope of equivalence.

Claims

Claim 1 A method for providing cognitive training, comprising: a step of determining the patient's input as correct or incorrect based on a pre-set discrimination rule based on a first type input word and a second type input word included in the patient's input to a first cognitive training sentence using a correct answer determination model; a step of providing feedback to the patient based on a pre-set feedback rule based on the first type input word or the second type input word included in the patient's input determined as correct or incorrect based on a feedback provision model; and a step of providing a cognitive training evaluation to the patient who received the feedback; wherein the step of determining the patient's input as correct or incorrect includes: a step of extracting the first type input word and the second type input word from at least one word included in the patient's input; a step of generating at least one second type representative word corresponding to the first type input word based on the first type input word; and a step of determining the patient's input as correct or incorrect based on the at least one second type representative word and the second type input word according to the pre-set discrimination rule. Claim 2 A method according to claim 1, wherein the step of generating at least one second-type representative word comprises the step of generating at least one second-type representative word corresponding to the first-type input word based on the first-type input word, based on the correct answer determination model or a preset generation rule. Claim 3 In claim 2, the step of determining the patient's input as correct or incorrect comprises: determining the patient's input as correct or incorrect according to the pre-set determination rule based on whether the second type input word corresponds to at least one second type representative word using the correct answer determination model; the method. Claim 4 A method according to claim 1, wherein the step of providing feedback to the patient comprises: a step of deriving a second type stored word corresponding to the first type input word based on determining the patient's input as the correct answer; and a step of providing a second cognitive training sentence to the patient based on the first type input word and the second type stored word. Claim 5 A method according to claim 1, wherein the step of providing feedback to the patient comprises: a step of obtaining a second type generated word based on the first type input word from an associative sentence generation model based on determining the patient's input as the correct answer; and a step of providing a second cognitive training sentence to the patient based on the first type input word and the second type generated word. Claim 6 A method according to claim 1, wherein the step of providing feedback to the patient comprises: a step of obtaining at least one first-type generated word corresponding to the second-type input word from a hint generation model based on determining the patient's input as an incorrect answer; and a step of providing a hint sentence to the patient based on association information of the at least one first-type generated word and the second-type input word. Claim 7 A method according to claim 1, wherein the step of providing a cognitive training evaluation to the patient comprises: a first evaluation of recalling training words provided in the cognitive training in the order in which the cognitive training was performed using a basic language model, and a second evaluation of recalling training words provided in the cognitive training based on clues regarding the training words. Claim 8 The method of claim 1 further comprises the step of determining the difficulty of the cognitive training based on the result of the cognitive training evaluation. Claim 9 A computer-readable, non-transient recording medium having a program for executing the method of claim 1 on a computer. Claim 10 A device for providing cognitive training, comprising: a memory in which at least one program is stored; and at least one processor for executing said at least one program, wherein the at least one processor determines the patient's input as correct or incorrect based on a pre-set discrimination rule based on a first type input word and a second type input word included in the patient's input to a first cognitive training sentence using a correct answer determination model, provides feedback to the patient based on a pre-set feedback rule based on the first type input word or the second type input word included in the patient's input determined as correct or incorrect using a feedback provision model, provides a cognitive training evaluation to the patient who received the feedback, extracts the first type input word and the second type input word from among at least one word included in the patient's input, generates at least one second type representative word corresponding to the first type input word based on the first type input word, and determines the patient's input as correct or incorrect based on the at least one second type representative word and the second type input word according to the pre-set discrimination rule.

Images