Electronic device and control method thereof

Abstract

This electronic device comprises a memory for storing instructions and at least one processor including processing circuitry, wherein the instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: obtain key log information when a user input is received; obtain typing information indicating the result of a user's typing on the basis of the key log information; obtain feature data by extracting a preset feature on the basis of the typing information; when the feature data is obtained in the state where first representative data indicating a typing pattern of the user is stored in the memory, obtain second representative data by updating the first representative data on the basis of the feature data; and obtain a cognitive ability value corresponding to the user on the basis of the second representative data.

Description

Electronic device and control method thereof

[0001] The present disclosure relates to an electronic device and a method for controlling the same, and more specifically, to an electronic device and a method for controlling the same that evaluates cognitive ability based on a user's typing pattern.

[0002] Cognitive ability can vary depending on the user's mental or physical capabilities. A user's cognitive ability can be assessed by evaluating memory, problem-solving skills, and concentration. Generally, questionnaires, interviews, and computer-based tests may be used. However, judging cognitive ability through these separate tests presents a problem in that it cannot assess the user's cognitive ability in real time.

[0003] There is a problem in that it is difficult to detect early when cognitive ability declines suddenly.

[0004] To assess a user's cognitive abilities, AI-based user behavior tracking, brainwave (EEG) analysis, and eye-tracking technologies may exist.

[0005] However, user behavior tracking or eye-tracking technology requires the use of image sensors. When using image sensors, personal information may become an issue.

[0006] When using a user's biometric information for brainwave measurement, the electronic device must be equipped with a biosensor. In such cases, there is a problem of increased costs associated with the biosensor. Furthermore, the biometric information itself may also raise privacy concerns.

[0007] The present disclosure is designed to improve upon the aforementioned problem, and the purpose of the present disclosure is to provide an electronic device and a method for controlling the same that determine a user's cognitive ability using a user's typing input pattern.

[0008] According to one embodiment, the electronic device includes at least one processor comprising a memory for storing instructions and a processing circuitry, and when the instructions are executed individually or collectively by the at least one processor, the electronic device, upon receiving user input, acquires key log information, acquires typing information representing the user's typing result based on the key log information, acquires feature data by extracting a preset feature based on the typing information, and when the feature data is acquired while a first representative data representing the user's typing pattern is stored in the memory, acquires a second representative data by updating the first representative data based on the feature data, and acquires a cognitive ability value corresponding to the user based on the second representative data.

[0009] The key log information includes key information included in the user input and information on the time when the key information was received, and the typing information may include at least one of the number of characters, the number of words, the number of times a target key was entered, or the number of times it was typed.

[0010] The above feature data may include at least one of a delete key ratio, typing speed, typing pause time, or key movement time.

[0011] When the above instructions are executed individually or collectively by at least one processor, the electronic device may obtain an initial typing time, a final typing time, and a number of typings based on the key log information, and obtain the typing speed by dividing the value obtained by subtracting the initial typing time from the final typing time by the number of typings.

[0012] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may obtain the typing start time of the second word and the typing end time of the first word based on the key log information including the first word and the second word, and obtain the typing stop time by subtracting the typing end time of the first word from the typing start time of the second word.

[0013] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may obtain the typing time of the second character and the typing time of the first character based on the key log information including the first character and the second character, and obtain the key movement time by subtracting the typing time of the first character from the typing time of the second character.

[0014] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may determine that the user's cognitive ability is reduced as the deletion key ratio is higher, as the typing speed is slower, as the typing pause time is longer, or as the key movement time is longer.

[0015] The first representative data above may be data representing the user's timing pattern prior to the point in time when the user input is received.

[0016] The first representative data includes at least one of an average value or a variance value for representing feature data, and when the instructions are executed individually or collectively by the at least one processor, the electronic device may acquire the second representative data by updating at least one of the average value or the variance value included in the first representative data based on the feature data.

[0017] The electronic device includes a display, and when the instructions are executed individually or collectively by the at least one processor, the electronic device may generate a guide screen including the cognitive ability value and display the guide screen through the display.

[0018] According to one embodiment, a control method for an electronic device comprises the steps of: acquiring key log information when user input is received; acquiring typing information representing the typing result of the user based on the key log information; acquiring feature data by extracting a preset feature based on the typing information; acquiring a second representative data by updating the first representative data based on the feature data when the feature data is acquired while the first representative data representing the typing pattern of the user is stored in the electronic device; and acquiring a cognitive ability value corresponding to the user based on the second representative data.

[0019] The key log information includes key information included in the user input and information on the time when the key information was received, and the typing information may include at least one of the number of characters, the number of words, the number of times a target key was entered, or the number of times it was typed.

[0020] The above feature data may include at least one of a delete key ratio, typing speed, typing pause time, or key movement time.

[0021] The step of acquiring the above feature data can be to acquire the first typing time, the last typing time, and the number of typings based on the key log information, and to acquire the typing speed by dividing the value obtained by subtracting the first typing time from the last typing time by the number of typings.

[0022] The step of acquiring the above feature data may be to acquire the typing start time of the second word and the typing end time of the first word based on the key log information including the first word and the second word, and to acquire the typing stop time by subtracting the typing end time of the first word from the typing start time of the second word.

[0023] The step of acquiring the above feature data may be to acquire the typing time of the second character and the typing time of the first character based on the key log information including the first character and the second character, and to acquire the key movement time by subtracting the typing time of the first character from the typing time of the second character.

[0024] The above control method may include a step of determining that the user's cognitive ability decreases as the deletion key ratio increases, as the typing speed decreases, as the typing pause time increases, or as the key movement time increases.

[0025] The first representative data above may be data representing the user's timing pattern prior to the point in time when the user input is received.

[0026] The first representative data includes at least one of an average value or a variance value for representing feature data, and the step of obtaining the second representative data can be to obtain the second representative data by updating at least one of the average value or the variance value included in the first representative data based on the feature data.

[0027] The above control method may include the step of generating a guide screen including the cognitive ability value and the step of displaying the guide screen.

[0028] FIG. 1 is a diagram illustrating the operation of receiving typing input through an electronic device according to one embodiment.

[0029] FIG. 2 is a block diagram illustrating an electronic device according to one embodiment.

[0030] FIG. 3 is a block diagram illustrating the specific configuration of the electronic device of FIG. 2 according to one embodiment.

[0031] FIG. 4 is a diagram illustrating the operation of receiving typing input through a wearable device according to one embodiment.

[0032] FIG. 5 is a diagram illustrating the operation of acquiring a cognitive ability value based on typing input according to one embodiment.

[0033] FIG. 6 is a diagram illustrating the operation of obtaining a cognitive ability value using an AI model according to one embodiment.

[0034] FIG. 7 is a diagram illustrating the operation of acquiring a cognitive ability value without past data according to one embodiment.

[0035] FIG. 8 is a diagram illustrating the operation of obtaining a cognitive ability value using past data according to one embodiment.

[0036] FIG. 9 is a diagram illustrating the operation of obtaining a cognitive ability value using past data according to one embodiment.

[0037] FIG. 10 is a diagram illustrating the operation of obtaining representative data using additional data according to one embodiment.

[0038] FIG. 11 is a diagram illustrating the operation of obtaining representative data using user data according to one embodiment.

[0039] FIG. 12 is a diagram illustrating the operation of obtaining representative data using medical data according to one embodiment.

[0040] FIG. 13 is a drawing for explaining a method of using additional data according to one embodiment.

[0041] FIG. 14 is a drawing for explaining a filtering module according to one embodiment.

[0042] FIG. 15 is a diagram illustrating a filtering calculation process according to one embodiment.

[0043] FIG. 16 is a drawing for illustrating a filtering module according to one embodiment.

[0044] FIG. 17 is a drawing for explaining detailed capabilities according to one embodiment.

[0045] FIG. 18 is a diagram illustrating the correlation between detailed capability and feature data according to one embodiment.

[0046] FIG. 19 is a diagram illustrating the operation of analyzing typing input according to one embodiment.

[0047] FIG. 20 is a diagram illustrating the operation of analyzing typing input according to one embodiment.

[0048] FIG. 21 is a drawing for explaining a plurality of areas that distinguish an operation UI according to one embodiment.

[0049] FIG. 22 is a diagram illustrating the operation of separating typing input by area according to one embodiment.

[0050] FIG. 23 is a diagram illustrating the operation of calculating area movement time according to one embodiment.

[0051] FIG. 24 is a diagram illustrating the operation of separating typing input by area according to one embodiment.

[0052] FIG. 25 is a diagram illustrating the operation of separating typing input by area according to one embodiment.

[0053] FIG. 26 is a diagram illustrating the operation of analyzing user input according to one embodiment.

[0054] FIG. 27 is a drawing for explaining typing information corresponding to Korean and English according to one embodiment.

[0055] FIG. 28 is a diagram illustrating the typing pattern of a patient with reduced cognitive ability according to one embodiment.

[0056] FIG. 29 is a diagram illustrating the effect of time series data according to one embodiment.

[0057] FIG. 30 is a diagram illustrating the operation of acquiring feature data according to one embodiment.

[0058] FIG. 31 is a diagram illustrating the operation of acquiring representative data according to one embodiment.

[0059] FIG. 32 is a drawing for illustrating a screen related to cognitive ability according to one embodiment.

[0060] FIG. 33 is a drawing for illustrating a screen related to cognitive ability according to one embodiment.

[0061] FIG. 34 is a drawing for illustrating a system including an electronic device (100) and a server (200) according to one embodiment.

[0062] FIG. 35 is a diagram illustrating the operation of obtaining a cognitive ability value from a server (200) according to one embodiment.

[0063] FIG. 36 is a drawing for explaining a method of controlling an electronic device (100) according to one embodiment.

[0064] The present disclosure will be described in detail below with reference to the attached drawings.

[0065] The terms used in the embodiments of this disclosure have been selected to be as widely used as possible, taking into account their functions within this disclosure; however, these terms 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 arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant explanatory section of this disclosure. Therefore, terms used in this disclosure should be defined not merely by their names, but based on their meanings and the overall content of this disclosure.

[0066] In this specification, expressions such as “have,” “may have,” “include,” or “may include” indicate the presence of such features (e.g., numerical values, functions, operations, or components such as parts) and do not exclude the presence of additional features.

[0067] The expression "at least one of A or / and B" should be understood as representing either "A" or "B" or "A and B".

[0068] Expressions such as "first," "second," "first," or "second" used in this specification may modify various components regardless of order and / or importance, and are used only to distinguish one component from another and do not limit said components.

[0069] Where it is stated that a component (e.g., Component 1) is "(operatively or communicatively) coupled with / to" or "connected to" another component (e.g., Component 2), it should be understood that the component may be directly connected to the other component or connected through the other component (e.g., Component 3).

[0070] The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "consisting of" are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0071] In the present disclosure, a "module" or "part" performs at least one function or operation and may be implemented in hardware or software, or a combination of hardware and software. Additionally, a plurality of "modules" or a plurality of "parts" may be integrated into at least one module and implemented by at least one processor, except for a "module" or "part" that needs to be implemented in specific hardware.

[0072] In this specification, the term "user" may refer to a person using an electronic device or a device using an electronic device (e.g., an artificial intelligence electronic device).

[0073] In the present disclosure, the statement that an artificial intelligence model is trained means that a basic artificial intelligence model (e.g., an artificial intelligence model including arbitrary random parameters) is trained using a number of training data by a learning algorithm, thereby creating a predefined rule of operation or an artificial intelligence model configured to perform a desired characteristic (or purpose). This training may be performed through a separate server and / or system, but is not limited thereto, and may also be performed on an electronic device (100). Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, transfer learning, or reinforcement learning, but are not limited to the examples described above.

[0074] Here, each artificial intelligence model may be implemented, for example, as a CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), BRDNN (Bidirectional Recurrent Deep Neural Network), or Deep Q-Network, but is not limited thereto.

[0075] A processor (110) for executing an artificial intelligence model according to one embodiment of the present disclosure may be implemented through a combination of software and a general-purpose processor such as a CPU, AP, DSP (Digital Signal Processor), a graphics-dedicated processor such as a GPU, VPU (Vision Processing Unit), or an artificial intelligence-dedicated processor such as an NPU. The processor (110) may be controlled to process input data according to a predefined operation rule or artificial intelligence model stored in memory (120). Alternatively, if the processor (110) is a dedicated processor (or an artificial intelligence-dedicated processor), it may be designed with a hardware structure specialized for processing a specific artificial intelligence model. For example, hardware specialized for processing a specific artificial intelligence model may be designed as a hardware chip such as an ASIC or FPGA. If the processor (110) is implemented as a dedicated processor, it may be implemented to include memory for implementing the embodiment of the present disclosure or to include a memory processing function for using external memory.

[0076] According to another example, the memory (120) may store information regarding an artificial intelligence model including multiple layers. Here, storing information regarding an artificial intelligence model may mean storing various information related to the operation of the artificial intelligence model, such as information about multiple layers included in the artificial intelligence model, and information about parameters used in each of the multiple layers (e.g., filter coefficients, bias, etc.).

[0077] An embodiment of the present disclosure will be described in more detail below with reference to the attached drawings.

[0078] FIG. 1 is a diagram illustrating the operation of receiving typing input through an electronic device (100) according to one embodiment.

[0079] Referring to FIG. 1, an electronic device (100) can receive user input. The electronic device (100) may include a display (140). The electronic device (100) may display an operation interface through the display (140). The operation interface may include a User Interface (UI) for receiving user input.

[0080] For example, the operation interface may include a key UI (101) for receiving text input. The key UI may be described as a key input UI, a touch input UI, etc.

[0081] The electronic device (100) can receive user input through a key UI (101). The electronic device (100) can obtain typing information based on the user input. The typing information may include information about at least one key input entered by the user.

[0082] Typing information may include at least one input by a user. Typing information may be described as touch information, input information, pressing information, tapping information, etc.

[0083] The electronic device (100) can acquire typing information. When typing information is acquired, the electronic device (100) can store the typing information in memory (120). The electronic device (100) can provide a cognitive ability value corresponding to the typing information. The cognitive ability value may be a score representing cognitive ability.

[0084] Cognitive ability can refer to the human mental capacity to perceive, understand, remember, learn, solve problems, or make decisions regarding information. Through cognitive ability, users can interpret environmental stimuli and regulate their behavior. Cognitive ability can change depending on experience, learning, and neurodevelopment. For example, cognitive ability may decline with age beyond a critical age. If cognitive ability declines, users may require medical treatment or assistance from those around them.

[0085] The electronic device (100) can analyze (or determine) the user's cognitive ability based on typing information.

[0086] FIG. 2 is a block diagram illustrating an electronic device (100) according to one embodiment.

[0087] Referring to FIG. 2, the electronic device (100) may include at least one processor (110) including a memory (120) for storing instructions and a processing circuitry.

[0088] At least one processor (110) can obtain key log information when user input is received.

[0089] At least one processor (110) can obtain user input through the operation interface of the electronic device (100). At least one processor (110) can obtain key log information corresponding to the user input.

[0090] Key log information can represent information related to the keys entered by the user.

[0091] Key log information may include key information included in user input and information on the time when the key information was received. A description of the key log information is provided in FIGS. 22 and FIGS. 30.

[0092] Key log information can be recorded as key log data, touch key data, key stamp data, key event data, etc.

[0093] For example, the electronic device (100) may include a touch display. At least one processor (110) may receive user input including touch input. At least one processor (110) may obtain key log information based on the touch input.

[0094] For example, an electronic device (100) may be connected to a keyboard. At least one processor (110) may receive user input including keyboard input. At least one processor (110) may obtain key log information based on keyboard input.

[0095] At least one processor (110) can obtain typing information representing the user's typing results based on key log information.

[0096] Typing information can represent information related to keys entered by the user. Typing information may include information calculating the number or frequency of specific targets.

[0097] Typing information may represent results analyzed through key log information. Typing information may include at least one of the number of characters, the number of words, the number of times a target key is entered, or the number of times it is typed. The target key may be a key pre-set for analysis. For example, the target key may include a delete key, an enter key, a spacebar key, etc.

[0098] Descriptions of typing information are provided in FIGS. 19, 27, and 30.

[0099] At least one processor (110) can obtain feature data by extracting preset features based on typing information.

[0100] Feature data may include at least one of a delete key ratio, typing speed, typing pause time, or key travel time.

[0101] At least one processor (110) can calculate the deletion key ratio. An explanation related to this is described in the embodiment (1930) of FIG. 19.

[0102] At least one processor (110) can obtain the initial typing time, the final typing time, and the number of typings based on key log information. At least one processor (110) can obtain the typing speed based on the initial typing time, the final typing time, and the number of typings. At least one processor (110) can obtain the typing speed by subtracting the initial typing time from the final typing time and dividing the result by the number of typings.

[0103] An explanation regarding typing speed is described in the embodiment (2020) of FIG. 20.

[0104] At least one processor (110) can obtain the typing start time of the second word and the typing end time of the first word based on key log information including the first word and the second word. At least one processor (110) can obtain a typing pause time based on the typing start time of the second word and the typing end time of the first word. The typing pause time may be described as a time interval between words or a pause time.

[0105] At least one processor (110) can obtain a typing stop time by subtracting the typing end time of the first word from the typing start time of the second word.

[0106] The typing pause time is described in the embodiment (2030) of FIG. 20.

[0107] At least one processor (110) can obtain the typing time of the second character and the typing time of the first character based on key log information including the first character and the second character.

[0108] At least one processor (110) can obtain a key movement time based on the typing time of the second character and the typing time of the first character.

[0109] At least one processor (110) can obtain the key travel time by subtracting the typing time of the first character from the typing time of the second character.

[0110] Key travel time can be described as the time interval between characters, key speed, etc. An explanation regarding key travel time is described in the embodiment (2040) of FIG. 20.

[0111] For example, the key travel time can be calculated by taking the area into account. An explanation related to this is described in FIGS. 21 to 25.

[0112] For example, feature data may include information related to swipe input. An explanation related to this is described in FIG. 26.

[0113] At least one processor (110) can determine that the user's cognitive ability is reduced as the deletion key ratio is higher, the typing speed is slower, the typing pause time is longer, or the key movement time is longer.

[0114] For example, as the deletion key ratio increases, at least one processor (110) may determine that the user's cognitive ability is declining.

[0115] For example, as the typing speed is slower, at least one processor (110) can determine that the user's cognitive ability is declining.

[0116] For example, the longer the typing pause time, the more likely at least one processor (110) is to determine that the user's cognitive ability is declining.

[0117] For example, the longer the key travel time, the more likely at least one processor (110) is to determine that the user's cognitive ability is declining.

[0118] An explanation regarding the assessment of cognitive ability is described in Fig. 28.

[0119] When feature data is acquired while the first representative data representing the user's typing pattern is stored in memory (120), at least one processor (110) can acquire the second representative data by updating the first representative data based on the feature data.

[0120] The first representative data may be data representing the user's timing pattern prior to the point in time when user input is received.

[0121] The first representative data may include at least one of an average value or a variance value to represent the feature data.

[0122] At least one processor (110) can obtain second representative data by updating at least one of the average value or variance value included in the first representative data based on the feature data.

[0123] Representative data may include values ​​to represent feature data. For example, representative data may include at least one of an average value, a minimum value, a maximum value, a variance value, a standard deviation value, and a change amount.

[0124] Representative data may be data that accumulates information about past users' typing patterns. Representative data may be recorded as accumulated typing pattern data.

[0125] Representative data may include embedding vectors. Representative data may include embedding vectors formed by compressing feature data.

[0126] An explanation regarding the representative data is described in Fig. 31.

[0127] At least one processor (110) can obtain a cognitive ability value corresponding to the user based on the second representative data.

[0128] A cognitive ability value may be a score representing cognitive ability. A cognitive ability value may be a numerical value representing an individual's cognitive ability. Cognitive ability may represent mental or physical abilities such as a person's memory, learning, problem-solving, attention, and logical thinking ability. Explanations related to cognitive ability are described in FIGS. 17 and 18.

[0129] Cognitive ability values ​​can be listed as predicted values ​​for cognitive impairment.

[0130] The electronic device (100) may include a display (140). At least one processor (110) may generate a guide screen including cognitive ability values. At least one processor (110) may display the guide screen through the display. Descriptions related to the guide screen are described in FIGS. 32 and 33.

[0131] The electronic device (100) can determine cognitive ability by analyzing the user's key input. The electronic device (100) can provide feedback on cognitive ability to the user in a normal device usage environment without a separate cognitive ability test. For example, in a situation where the user uses a text messaging function, the electronic device (100) can determine the user's cognitive ability.

[0132] FIG. 3 is a block diagram illustrating the specific configuration of the electronic device (100) of FIG. 2 according to one embodiment.

[0133] FIG. 3 is a block diagram of an exemplary electronic device (100) capable of performing the operations described in this document.

[0134] Referring to FIG. 3, the electronic device (100) may be one of various forms of electronic devices, such as a notebook (190), smartphones (191) having various form factors (e.g., a bar-type smartphone (191-1), a foldable-type smartphone (191-2), or a sliderable (or rollable)-type smartphone (191-3)), a tablet (192), a cellular phone (not shown), and other similar computing devices (not shown). The components, their relationships, and their functions illustrated in FIG. 3 are illustrative only and are not intended to limit the implementations described or claimed herein. The electronic device (100) may be referred to as a mobile device, a user device, a multifunction device, a portable device, or a server.

[0135] The electronic device (100) may include components comprising at least one processor (110) (hereinafter referred to as processor (110)), at least one memory (120) (hereinafter referred to as memory (120)), at least one display (140) (hereinafter referred to as display (140)), at least one image sensor (150) (hereinafter referred to as image sensor (150)), at least one communication circuit (160) (hereinafter referred to as communication circuit (160)), and / or at least one sensor (170) (hereinafter referred to as sensor (170)). The components are merely exemplary. For example, the electronic device (100) may include other components (e.g., power management integrated circuitry (PMIC), audio processing circuit, antenna, rechargeable battery, or input / output interface). For example, some components may be omitted from the electronic device (100). For example, some components may be integrated into a single component.

[0136] The processor (110) may be implemented as one or more integrated circuit (or circuitry) chips and may perform various data processing operations. The processor (110) may include at least one electrical circuit and may process instructions (or programs, data) stored in memory (120) individually or collectively in a distributed manner. The processor (110) may include a processor assembly comprising one or more processing circuits. The processor (110) may include any processing circuit that is operative to control the performance and operations of one or more components of the electronic device (100) (e.g., memory (120), display (140), image sensor (150), communication circuit (160), and / or sensor (170)). For example, the processor (110) (e.g., application processor (AP)) may be implemented as a system on chip (SoC) (e.g., a single chip or chipset). For example, the processor (110) may be implemented with a plurality of cores (or at least one core circuit), a plurality of chips, or a plurality of chipsets. For example, the processor (110) may include one or more processing circuits. For example, the processor (110) may include one or more processing circuits configured to perform the various functions of the present disclosure individually and / or collectively. As an example without limitation, at least a portion of the processor (110) may be included in a first chip of the electronic device (100), and at least another portion of the processor (110) may be included in a second chip of the electronic device (100) different from the first chip of the electronic device (100).

[0137] For example, the processor (110) may include a central processing unit (111), a graphics processing unit (112), a neural processing unit (113), an image signal processor (114), a display controller (115), a memory controller (116), a storage controller (117), a communication processor (118), and / or a sensor interface (119). These components of the processor (110) are merely exemplary. For example, the processor (110) may include other components. For example, some components of the processor (110) may be omitted from the processor (110). For example, some components of the processor (110) may be included as separate components of the electronic device (100) outside of the processor (110). For example, some components of the processor (110) (e.g., memory controller (116)) may be included in other components (e.g., at least part of memory (120), an interface (e.g. available for connection to at least one component of the electronic device (100)), a display (140) and / or an image sensor (150)).

[0138] The processor (110) may cause other components of the electronic device (100) to perform various operations by executing instructions stored in memory (120). The CPU (111) (or central processing circuit) may be configured to control the components of the processor (110) based on the execution of instructions stored in memory (120) (e.g., volatile memory (121) and / or non-volatile memory (122)). The GPU (112) (or graphics processing circuit) may be configured to execute parallel operations (e.g., rendering). The NPU (113) (or neural processing circuit, or AI (artificial intelligence) chip) may be configured to execute operations for an artificial intelligence model (e.g., convolution computation). An ISP (114) (or image signal processing circuit) may be configured to process a raw image acquired through an image sensor (150) into a format suitable for a component within the electronic device (100) or a component of the processor (110). A display controller (115) (or display control circuit, or DPU (display processing unit)) may be configured to process an image acquired from a CPU (111), GPU (112), ISP (114), or memory (120) (e.g., volatile memory (121)) into a format suitable for a display (140). A memory controller (116) (or memory control circuit) may be configured to control reading data from the volatile memory (121) and writing data to the volatile memory (121). A storage controller (117) (or storage control circuit) may be configured to control reading data from the non-volatile memory (122) and writing data to the non-volatile memory (122).The CP (118) (communication processing circuit) may be configured to process data obtained from a component of the processor (110) into a format suitable for transmitting to another electronic device via the communication circuit (160), or to process data obtained from another electronic device via the communication circuit (160) into a format suitable for processing by the component of the processor (110). For example, the communication circuit (160) may include one or more communication circuits. The sensor interface (119) (or sensing data processing circuit, sensor hub) may be configured to process data regarding the state of the electronic device (100) and / or the state around the electronic device (100), obtained through the sensor (170), into a format suitable for the component of the processor (110).

[0139] Memory (120) may include one or more storage media (or one or more storage devices). For example, memory (120) may include a memory assembly comprising one or more storage media. For example, the one or more storage media may include a hard drive, a permanent memory such as flash memory, read-only memory (ROM) (e.g., non-volatile memory (122)), a semi-permanent memory such as random access memory (RAM) (e.g., volatile memory (121)), any other suitable type of storage (or storage assembly), or any combination thereof. Memory (120) may include a cache memory, which is one or more different types of memory used to temporarily store data for a function or feature of the electronic device (100). As an example not limited to, the cache memory may be included within the processor (110). The memory (120) may be fixedly embedded within the electronic device (100) or incorporated into one or more suitable types of components (e.g., a SIM (subscriber identity module) card and / or an SD (secure digital) card) that can be repeatedly inserted into and removed from the electronic device (100).

[0140] For example, memory (120) may store one or more software applications, such as operating system (or system) software applications, firmware software applications, driver software applications, plugin (e.g., add-in, add-on, and / or applet) software applications, and / or any other suitable software applications. For example, the one or more software applications may include instructions executable by the processor (110). For example, memory (120) may store instructions that can be called by an application programming interface (API). For example, memory (120) may store instructions within a library.

[0141] FIG. 4 is a diagram illustrating the operation of receiving typing input through a wearable device according to one embodiment.

[0142] Referring to FIG. 4, the electronic device (100) may be implemented as a wearable device. The wearable device may include a display. The wearable device may provide an operation interface through the display. The wearable device may obtain user input through the operation interface.

[0143] For example, the wearable device may be a smart watch. The electronic device (100) may be implemented as various devices that receive user input through a display in addition to a smart watch.

[0144] FIG. 5 is a diagram illustrating the operation of acquiring a cognitive ability value based on typing input according to one embodiment.

[0145] Referring to FIG. 5, the electronic device (100) can acquire user typing information (or touch information) (S510). Based on the typing information, the electronic device (100) can acquire feature data representing the user's typing pattern (S520). The feature data may include information representing the user's typing. The feature data may include at least one piece of information representing the user's typing pattern.

[0146] Feature data may include at least one of typing speed, touch pressure, typing pause time, delete key ratio, error rate (ratio of the number of grammatically incorrect characters to the number of characters entered), pause time (time interval between words), recommended word selection frequency, finger gesture speed, hand tremor frequency during swipe, and linearity during swipe. A specific description related thereto is provided in FIG. 30.

[0147] The electronic device (100) can store a typing pattern history in memory (120). The electronic device (100) can store a pattern history representing the user's past typing patterns in memory (120). The electronic device (100) can obtain a cognitive ability value corresponding to feature data based on the past typing pattern history (S530).

[0148] The electronic device (100) can provide guide information based on a cognitive ability value (S540). The electronic device (100) can provide the user with high or low cognitive ability by providing the cognitive ability value to the user. The electronic device (100) can output a screen corresponding to the cognitive ability. An explanation related to this is described in FIGS. 31 and FIGS. 32.

[0149] FIG. 6 is a diagram illustrating the operation of obtaining a cognitive ability value using an AI model according to one embodiment.

[0150] Referring to the embodiment (600) of FIG. 6, the electronic device (100) may include at least one of a data extraction module (10), a preprocessing module (20), a data storage module (30), and an AI model (40).

[0151] The electronic device (100) can acquire typing information. The electronic device (100) can transmit the typing information to the data extraction module (10).

[0152] The data extraction module (10) may be a module that extracts features corresponding to input data. The data extraction module (10) may include a plurality of parameters for determining how to extract features corresponding to input data. The data extraction module (10) may obtain feature data corresponding to typing information, which is input data, based on the plurality of parameters.

[0153] The data extraction module (10) can acquire typing information. The data extraction module (10) can acquire feature data based on the typing information. The data extraction module (10) can acquire feature data representing the user's typing pattern included in the typing information. The feature data may include at least one feature, and at least one feature may be identified based on a pre-set criterion. The data extraction module (10) can transmit the feature data to the preprocessing module (20). Specific operations for acquiring feature data are described in FIGS. 19, 20, and 23.

[0154] The preprocessing module (20) can receive feature data from the data extraction module (10). The preprocessing module (20) can generate representative data based on the feature data. The representative data may include a value to represent the feature data. For example, the representative data may include at least one of an average value, a minimum value, a maximum value, a variance value, a standard deviation value, and a change amount. The preprocessing module (20) can transmit the representative data to the data storage module (30).

[0155] The data storage module (30) may be a module that stores received representative data. The data storage module (30) may receive representative data from the preprocessing module (20). The data storage module (30) may store the received representative data. When a request from the electronic device (100) is received, the data storage module (30) may store the representative data.

[0156] The data storage module (30) can transmit the stored representative data to the preprocessing module (20) according to a specific event. The specific event may include an event in which new typing information is received.

[0157] The data storage module (30) can store representative data and then transmit the representative data to the AI ​​model (40).

[0158] The AI ​​model (40) can obtain representative data from the data storage module (30) as input data. The AI ​​model (40) may be a cognitive ability analysis model. The AI ​​model (40) may generate a cognitive ability value as output data based on representative data representing the user's typing pattern. The AI ​​model (40) may be a model trained by training data that includes the typing patterns and cognitive ability values ​​of each of multiple people. The AI ​​model (40) may be a model trained to receive representative data representing a keyboard pattern and to generate a cognitive ability value corresponding to the representative data.

[0159] For example, the AI ​​model (40) may include a Cumulative Recurrent Autoencoder (CRA). The CRA may be a form of deep learning model. The AI ​​model (40) learns the accumulated information and sequence patterns of the input data and can reconstruct or predict existing data.

[0160] FIG. 7 is a diagram illustrating the operation of acquiring a cognitive ability value without past data according to one embodiment.

[0161] Referring to FIG. 7, first typing information can be obtained (S705). The electronic device (100) can obtain first feature data based on the first typing information (S710). The electronic device (100) can obtain first feature data corresponding to the first typing information using a data extraction module (10).

[0162] The electronic device (100) can obtain first representative data based on first feature data (S715). The electronic device (100) can obtain first representative data (S720). The electronic device (100) can obtain first representative data corresponding to the first feature data using a preprocessing module (20).

[0163] The electronic device (100) can store the first representative data in memory (120) (S720). The electronic device (100) can store the first representative data through a data storage module (30).

[0164] The electronic device (100) can obtain a first cognitive ability value based on the first representative data (S725). The electronic device (100) can obtain a first cognitive ability value through an AI model (40). The electronic device (100) inputs the first representative data into the AI ​​model (40) and can obtain a first cognitive ability value corresponding to the first representative data from the AI ​​model (40).

[0165] The electronic device (100) can provide a first cognitive ability value (S730). The electronic device (100) can provide information indicating cognitive ability to the user based on the first cognitive ability value.

[0166] For example, the electronic device (100) can output guide information generated based on a first cognitive ability value through a display (140).

[0167] For example, the electronic device (100) can output guide information generated based on a first cognitive ability value through the speaker of the electronic device (100).

[0168] For example, the electronic device (100) can transmit guide information generated based on a first cognitive ability value to a server (200).

[0169] For example, the electronic device (100) can transmit guide information generated based on a first cognitive ability value to an external device that can be connected to the electronic device (100).

[0170] FIG. 8 is a diagram illustrating the operation of obtaining a cognitive ability value using past data according to one embodiment.

[0171] Referring to FIG. 8, the electronic device (100) can obtain second typing information (S805). The electronic device (100) can obtain second typing information after obtaining first typing information. The electronic device (100) can obtain second typing information while storing first representative data based on the first typing information.

[0172] The electronic device (100) can acquire second feature data based on second typing information (S810). The electronic device (100) can acquire second feature data corresponding to the second typing information using a data extraction module (10).

[0173] The electronic device (100) can obtain second representative data by updating first representative data based on second feature data (S815). The electronic device (100) can obtain second representative data based on second feature data and first representative data. The electronic device (100) can obtain second representative data through a preprocessing module (20).

[0174] The electronic device (100) can store the second representative data in memory (120) (S820). The electronic device (100) can store the second representative data through a data storage module (30).

[0175] The electronic device (100) can obtain a second cognitive ability value based on the second representative data (S825). The electronic device (100) can obtain a second cognitive ability value by inputting the second representative data into the AI ​​model (40).

[0176] The electronic device (100) can provide a second cognitive ability value (S830). The operation of providing the cognitive ability value may correspond to the operation S730 of FIG. 7.

[0177] FIG. 9 is a diagram illustrating the operation of obtaining a cognitive ability value using past data according to one embodiment.

[0178] The operations S905, S910, S915, S920, S925, and S930 of FIG. 9 may correspond to the operations S1005, S1010, S1015, S1020, S1025, and S1030 of FIG. 10. Redundant description is omitted.

[0179] Referring to FIG. 9, the electronic device (100) can obtain typing information (S905). The electronic device (100) can obtain feature data based on the typing information (S910).

[0180] After feature data is acquired, the electronic device (100) can determine whether the previously stored first representative data is stored (S911). The previously stored first representative data may represent data representing the typing patterns of past users. The operation for acquiring the first representative data is described in FIG. 7.

[0181] If the previously stored first representative data is not stored (S911-N), the electronic device (100) can obtain the first representative data based on the feature data (S912). The operation S912 can correspond to the operation S715 of FIG. 7.

[0182] The electronic device (100) can store the first representative data (S913). The S913 operation may correspond to the S720 operation of FIG. 7.

[0183] After obtaining the first representative data, the electronic device (100) can obtain new typing information and obtain new feature data.

[0184] If the first representative data is stored (S911-Y), the electronic device (100) can obtain the second representative data by updating the first representative data based on the feature data (S915). The electronic device (100) can store the second representative data in memory (120). The electronic device (100) can obtain a cognitive ability value based on the second representative data (S925). The electronic device (100) can provide the cognitive ability value (S930).

[0185] FIG. 10 is a diagram illustrating the operation of obtaining representative data using additional data according to one embodiment.

[0186] Referring to the embodiment (1000) of FIG. 10, the electronic device (100) may include at least one of a data extraction module (10), a preprocessing module (20), a data storage module (30), and an AI model (40). An explanation related to this is provided in FIG. 6. Redundant explanations are omitted.

[0187] The electronic device (100) can acquire at least one of user data, reference feature data, or medical data.

[0188] User data may include information related to the user. User data may include information representing the user. For example, user data may include at least one of age, gender, nationality, place of residence, and language.

[0189] The electronic device (100) can acquire reference feature data based on user data. The reference feature data may represent pre-set data representing a specific group. The reference feature data may be pre-set data representing a specific age, a specific gender, or a specific nationality.

[0190] For example, assume that the electronic device (100) has acquired user data representing a man in his 60s. The electronic device (100) may acquire reference feature data corresponding to the user data. The reference feature data may include average feature data of a man in his 60s.

[0191] Medical data may include the user's medical records. Medical data may be obtained through an Electronic Medical Record (EMR) system. Depending on the implementation example, medical data may be included in user data. For security purposes, medical data may be processed separately and not included in user data.

[0192] For example, an electronic device (100) may transmit at least one of user data, reference feature data, or medical data to a preprocessing module (20). The preprocessing module (20) may receive at least one of user data, reference feature data, or medical data. The preprocessing module (20) may obtain representative data based on at least one of user data, reference feature data, and medical data, and the feature data.

[0193] For example, an electronic device (100) can obtain representative data based on feature data and user data.

[0194] For example, an electronic device (100) can obtain representative data based on feature data, user data, and reference feature data.

[0195] For example, an electronic device (100) can obtain representative data based on feature data, user data, reference feature data and medical data.

[0196] For example, an electronic device (100) can obtain representative data based on feature data and reference feature data.

[0197] For example, an electronic device (100) can obtain representative data based on feature data, reference feature data and medical data.

[0198] For example, an electronic device (100) can obtain representative data based on feature data and medical data.

[0199] In the above-described multiple embodiments, when existing representative data is stored, the electronic device (100) can additionally obtain a second representative data by considering the existing representative data (first representative data).

[0200] FIG. 11 is a diagram illustrating the operation of obtaining representative data using user data according to one embodiment.

[0201] The operations S1105, S1105, S1110, S1111, S1120, S1125, and S1130 of FIG. 11 may correspond to the operations S905, S905, S910, S911, S920, S925, and S930 of FIG. 9. Redundant description is omitted.

[0202] When the previously stored first representative data is identified as stored (S1111-Y), the electronic device (100) can obtain user data (S1112). The electronic device (100) can obtain at least one of the user's age, gender, or nationality from the user data.

[0203] The electronic device (100) can acquire reference feature data corresponding to user data (S1113). The electronic device (100) can acquire reference feature data corresponding to at least one of the user's age, gender, or nationality.

[0204] The electronic device (100) can obtain second representative data by updating first representative data based on at least one of feature data, user data, or reference feature data (S1115).

[0205] FIG. 12 is a diagram illustrating the operation of obtaining representative data using medical data according to one embodiment.

[0206] The operations S1205, S1210, S1211, S1212, S1213, S1220, S1225, and S1230 of FIG. 12 may correspond to the operations S1105, S1110, S1111, S1112, S1113, S1120, S1125, and S1130 of FIG. 11. Redundant description is omitted.

[0207] After user data and reference feature data corresponding to the user data are obtained, the electronic device (100) can obtain medical data corresponding to the user data (S1214). When the electronic device (100) receives the user data, it can request medical data corresponding to the user data from an external server. For example, the external server may be an EMR server. The EMR server may be a server that provides medical data of a specific user through security. The electronic device (100) can receive medical data from the external server.

[0208] The electronic device (100) can obtain second representative data by updating first representative data based on at least one of feature data, user data, reference feature data, or medical data. The electronic device (100) can obtain second representative data through a preprocessing module (20).

[0209] FIG. 13 is a drawing for explaining a method of using additional data according to one embodiment.

[0210] Referring to the embodiment (1310) of FIG. 13, the preprocessing module (20) may receive at least one of feature data, first representative data, user data, reference feature data, or medical data. The preprocessing module (20) may obtain second representative data based on at least one of feature data, first representative data, user data, reference feature data, or medical data.

[0211] Referring to the embodiment (1320) of FIG. 13, the electronic device (100) may include at least one of a preprocessing module (20) and a user profile generation module (50).

[0212] The user profile generation module (50) may be a module that generates a group of data related to a user. For example, the user profile generation module (50) may receive at least one of user data, reference feature data, or medical data. The user profile generation module (50) may generate a user profile based on at least one of user data, reference feature data, or medical data. A user profile may represent a unit of data that groups and stores data related to a user. The user profile generation module (50) may transmit the generated user profile to the preprocessing module (20).

[0213] The preprocessing module (20) can receive at least one of feature data, first representative data, or user profile. The preprocessing module (20) can obtain second representative data based on at least one of feature data, first representative data, or user profile.

[0214] The operations performed after acquiring the second representative data are described in FIG. 6 or FIG. 10.

[0215] FIG. 14 is a drawing for explaining a filtering module according to one embodiment.

[0216] Referring to the embodiment (1400) of FIG. 14, the electronic device (100) may include at least one of a preprocessing module (20) and a filtering module (60).

[0217] The preprocessing module (20) can obtain second representative data based on at least one of feature data, reference feature data, or first representative data.

[0218] The preprocessing module (20) can obtain third representative data based on at least one of feature data, reference feature data, or reference representative data.

[0219] Reference representative data may represent pre-configured representative data. Representative data may include representative values ​​representing feature data acquired based on typing information. Reference representative data may be data predetermined by user settings rather than acquired by typing information obtained in real time.

[0220] The preprocessing module (20) may use the first representative data to obtain the second representative data. The first representative data may be data representing the user's past typing patterns. Therefore, the second representative data may be data obtained by reflecting the user's past typing patterns.

[0221] The preprocessing module (20) may use reference representative data instead of the first representative data when obtaining the third representative data. The reference representative data may be data that does not reflect the typing patterns of past users. Therefore, the third representative data may be data obtained by reflecting only the typing patterns of the current user.

[0222] For example, the reference representative data may be 0 (or null). The preprocessing module (20) may obtain third representative data based on feature data and reference feature data.

[0223] The preprocessing module (20) can transmit the second representative data and the third representative data to the filtering module (60).

[0224] The filtering module (60) can analyze the received data to determine whether cognitive ability can be inferred through the newly acquired feature data. If it is determined that the feature data is unsuitable for inferring cognitive ability, the electronic device (100) may not generate a cognitive ability value by filtering the corresponding feature data.

[0225] The filtering module (60) can obtain second representative data and third representative data. The filtering module (60) can perform a filtering function by comparing the second representative data and the third representative data.

[0226] The filtering module (60) may include at least one of a difference value calculation module, a distribution storage module, and a distribution verification module.

[0227] The difference calculation module can calculate the difference between the second representative data and the third representative data. The difference value can be described as a difference vector. The difference calculation module can transmit the difference value to the distribution storage module. A larger difference value may indicate that the currently acquired typing information differs from the existing pattern.

[0228] For example, the difference calculation module can obtain the difference value using Manhattan Distance. Manhattan Distance represents the distance calculated by summing the absolute values ​​of the differences between vectors.

[0229] For example, the difference calculation module can obtain the difference value using cosine similarity. Cosine similarity can be a value that measures similarity based on the directionality between two vectors.

[0230] The distribution storage module can store a threshold range representing a normal distribution for user typing patterns. The distribution storage module can store a threshold range indicating that the difference vector is within a normal range.

[0231] The distribution verification module can obtain the difference value between the second representative data and the third representative data from the difference value calculation module. The distribution verification module can obtain a threshold range from the distribution storage module. The distribution verification module can identify whether the difference value falls within the threshold range. The distribution verification module can output a filtering result. The filtering result may include information indicating whether to obtain a cognitive ability value using the received typing information.

[0232] If the difference value falls within a threshold range, the electronic device (100) can determine that the received feature data is included in a normal distribution. If the difference value falls within a threshold range, the electronic device (100) can obtain a cognitive ability value using second representative data corresponding to the feature data. The electronic device (100) can obtain a cognitive ability value by inputting the second representative data into an AI model (40).

[0233] FIG. 15 is a diagram illustrating a filtering calculation process according to one embodiment.

[0234] The operations S1505, S1510, S1511, S1512, S1513, S1520, S1525, and S1530 of FIG. 15 may correspond to the operations S1105, S1110, S1111, S1112, S1113, S1120, S1125, and S1130 of FIG. 11. The operations S1515-1, S1515-2, S1515-3, S1515-4, and S1515-5 may correspond to the operations of the filtering module (60) of FIG. 14. Redundant descriptions are omitted.

[0235] When user data and reference feature data are obtained, the electronic device (100) can obtain second representative data based on at least one of the feature data, reference feature data, or first representative data (S1515-1).

[0236] The electronic device (100) can obtain third representative data based on at least one of feature data, reference feature data, or reference representative data (S1515-2). An explanation regarding the reference representative data is described in FIG. 14.

[0237] The electronic device (100) can obtain the difference value between the second representative data and the third representative data (S1515-3). The electronic device (100) can identify whether the difference value is included in a preset threshold range. The threshold range may be a range indicating whether the difference value corresponds to a normal distribution.

[0238] If the difference value is not included in the threshold range (S1515-4-N), the electronic device (100) may delete the second representative data (S1515-5). The electronic device (100) may determine that the cognitive ability value is not obtained using the second representative data. The electronic device (100) may determine that the typing information from which feature data was extracted deviates from the normal distribution of the existing typing pattern.

[0239] If the difference value is included in the threshold range (S1515-4-Y), the electronic device (100) can store the second representative data (S1520). The electronic device (100) can obtain a cognitive ability value based on the second representative data (S1525). The electronic device (100) can provide the cognitive ability value (S1530).

[0240] FIGS. 14 and 15 describe the operation of generating a plurality of representative data using a preprocessing module (20) and comparing the plurality of representative data with a filtering module (60). According to another embodiment, the filtering module (60) can perform a filtering function by comparing pattern data rather than representative data.

[0241] FIG. 16 is a drawing for illustrating a filtering module according to one embodiment.

[0242] Referring to the embodiment (1600) of FIG. 16, the electronic device (100) may include at least one of a pattern generation module (25) or a filtering module (60).

[0243] The pattern generation module (25) may be a module that analyzes the user's typing pattern. The pattern generation module (25) may generate pattern data based on input data. The pattern data may represent the user's typing pattern corresponding to the input data.

[0244] The preprocessing module (20) can obtain second pattern data based on at least one of feature data, reference feature data, or first pattern data.

[0245] The preprocessing module (20) can obtain third pattern data based on at least one of feature data, reference feature data, or reference pattern data.

[0246] Reference pattern data may represent pre-set pattern data. Pattern data may include pattern values ​​representing feature data acquired based on typing information. Reference pattern data may be data predetermined by user settings rather than acquired by typing information obtained in real time.

[0247] The preprocessing module (20) may use the first pattern data to obtain the second pattern data. The first pattern data may be data representing the user's past typing pattern. Accordingly, the second pattern data may be data obtained by reflecting the user's past typing pattern.

[0248] The preprocessing module (20) may use reference pattern data instead of the first pattern data when acquiring the third pattern data. The reference pattern data may be data that does not reflect the typing patterns of past users. Therefore, the third pattern data may be data acquired by reflecting only the typing patterns of the current user.

[0249] For example, the reference pattern data may be 0 (or null). The preprocessing module (20) may obtain third pattern data based on feature data and reference feature data.

[0250] The preprocessing module (20) can transmit the second pattern data and the third pattern data to the filtering module (60).

[0251] The filtering module (60) can analyze the received data to determine whether cognitive ability can be inferred through the newly acquired feature data. If it is determined that the feature data is unsuitable for inferring cognitive ability, the electronic device (100) may not generate a cognitive ability value by filtering the corresponding feature data.

[0252] The filtering module (60) can acquire second pattern data and third pattern data. The filtering module (60) can perform a filtering function by comparing the second pattern data and the third pattern data.

[0253] The filtering module (60) may include at least one of a difference value calculation module, a distribution storage module, and a distribution verification module.

[0254] The difference value calculation module can calculate the difference value between the second pattern data and the third pattern data. The difference value can be described as a difference vector. The difference value calculation module can transmit the difference value to the distribution storage module. A larger difference value may indicate that the currently acquired typing information differs from the existing pattern.

[0255] The distribution storage module can store a threshold range representing a normal distribution for user typing patterns. The distribution storage module can store a threshold range indicating that the difference vector is within a normal range.

[0256] The distribution verification module can obtain the difference value between the second pattern data and the third pattern data from the difference value calculation module. The distribution verification module can obtain a threshold range from the distribution storage module. The distribution verification module can identify whether the difference value falls within the threshold range. The distribution verification module can output a filtering result. The filtering result may include information indicating whether to obtain a cognitive ability value using the received typing information.

[0257] If the difference value falls within a threshold range, the electronic device (100) can determine that the received feature data is included in a normal distribution. If the difference value falls within a threshold range, the electronic device (100) can obtain a cognitive ability value using second pattern data corresponding to the feature data. The electronic device (100) can obtain a cognitive ability value by inputting the second pattern data into an AI model (40).

[0258] Referring to the embodiment (1610) of FIG. 16, the preprocessing module (20) can obtain second representative data based on at least one of feature data, reference feature data, first representative data, or second pattern data. The electronic device (100) can obtain a cognitive ability value by inputting the second representative data into an AI model (40).

[0259] FIG. 17 is a drawing for explaining detailed capabilities according to one embodiment.

[0260] Referring to the table (1700) in FIG. 17, the electronic device (100) can identify specific abilities to analyze cognitive abilities. Specific abilities may include at least one of memory ability, visuospatial judgment ability, attention ability, executive ability, or language ability.

[0261] Memory ability can be the mental capacity to store past information and retrieve it when needed.

[0262] Spatiotemporal judgment ability can be the ability to recognize the location and movement of objects and to grasp relationships within space.

[0263] Attention can be the ability to focus on specific stimuli or tasks and filter out unnecessary information.

[0264] Executive ability can be the cognitive ability to perform specific actions to achieve a goal.

[0265] Language ability can be the ability to understand, express, and communicate using language.

[0266] The electronic device (100) can identify detailed capabilities based on feature data. The electronic device (100) can determine detailed capabilities corresponding to the feature data and identify cognitive capabilities based on the determined detailed capabilities. Cognitive capabilities may be described as comprehensive cognitive capabilities.

[0267] FIG. 18 is a diagram illustrating the correlation between detailed capability and feature data according to one embodiment.

[0268] The table (1800) of FIG. 18 may indicate a correlation between feature data and detailed capabilities. The feature data may include at least one of a delete key ratio, typing speed, time interval between words (pause time), or time interval between characters (key movement time, key speed).

[0269] The deletion key ratio may be highly correlated in the order of memory ability, spatiotemporal judgment ability, and attention ability.

[0270] Typing speed may be highly correlated with spatiotemporal judgment ability, attention ability, and language ability, in that order.

[0271] The time interval between words (pause time) may be highly correlated with memory ability, language ability, and attention ability, in that order.

[0272] The time intervals between characters (key movement time, key speed) may be highly correlated in the order of executive ability, spatiotemporal judgment ability, and attention ability.

[0273] FIG. 19 is a diagram illustrating the operation of analyzing typing input according to one embodiment.

[0274] Referring to the embodiment (1910) of FIG. 19, the electronic device (100) can obtain user input. The electronic device (100) can obtain key log information based on the user input. The key log information may include key information and time information.

[0275] Key information may represent a key entered by a user. The key may include not only a physical key but also a key in the form of a UI displayed on a display. For example, if a keyboard input UI is displayed on a display screen and the user touches the keyboard input UI, the electronic device (100) may obtain key information corresponding to the user's touch location.

[0276] Time information can indicate the time when a key is entered. Time information can be mapped to key information.

[0277] For example, assume that user input typing "galaxy ai is here" is received. The electronic device (100) may receive user input including a first word (galaxy), a second word (ai), a third word (is), and a fourth word (here). The electronic device (100) may receive user input in which a space key is pressed between words.

[0278] At the first time point (t1), g is input, at the second time point (t2), a is input, at the third time point (t3), l is input, at the fourth time point (t4), a is input, at the fifth time point (t5), x is input, at the sixth time point (t6), y is input, at the seventh time point (t7), a space key is input, at the eighth time point (t8), a is input, at the ninth time point (t9), i is input, at the tenth time point (t10), a space key is input, at the eleventh time point (t11), i is input, at the tt second time point (t12), s is input, at the thirteenth time point (t13), a space key is input, at the fourteenth time point (t14), h is input, at the fifteenth time point (t15), e is input, at the sixteenth time point (t16), r is input, and at the seventeenth time point (t17), e can be input.

[0279] The electronic device (100) can determine that user input has been received from a first time point (t1) to a 17th time point (t17) based on key log information. If user input is not received during a threshold time, the electronic device (100) can determine that user input has been received from the time point (t1) when the first user input was received to the time point (t17) when the last input was received.

[0280] Referring to the embodiment (1920) of FIG. 19, the electronic device (100) can obtain typing information based on key log information. The typing information may include results analyzed through the key log information. For example, the typing information may include at least one of the number of characters, the number of words, the number of enter keys, the number of space keys, the number of delete keys, or the number of typings.

[0281] The character count can represent the number of characters included in the user input.

[0282] The word count can represent the number of words included in the user input.

[0283] The number of Enter keys can represent the number of Enter keys included in the user input.

[0284] The number of space keys can represent the number of space keys included in the user input.

[0285] The number of delete keys can represent the number of delete keys included in the user input.

[0286] The number of typings may represent the number of typings included in the user input. If the electronic device (100) receives user input via touch input, the number of typings may be recorded as the number of touches.

[0287] The electronic device (100) can acquire feature data based on typing information. The feature data may include information indicating the user's typing pattern.

[0288] Referring to the embodiment (1930) of FIG. 19, the feature data may include at least one of a deletion key ratio or a typo rate. The electronic device (100) may obtain a deletion key ratio based on the number of deletion keys and the number of characters. The electronic device (100) may obtain a deletion key ratio based on the number of input characters and the number of characters that do not conform to grammar among those characters.

[0289] The electronic device (100) can obtain a value obtained by dividing the number of deletion keys by the number of characters (L) as the deletion key ratio.

[0290] The electronic device (100) can obtain the number of characters that do not conform to the rules of grammar divided by the number of characters entered (L) as the error rate.

[0291] The electronic device (100) can obtain a typo rate based on the number of missing characters (S), the number of replacement characters (D), the number of additional input characters (I), and the total number of characters (N).

[0292] The number of missing characters (S) can represent the number of missing characters relative to the correct grammar in the input text.

[0293] The replacement character count (D) can represent the number of (incorrect) replacement characters relative to the correct grammar in the input text.

[0294] The number of additional input characters (I) can represent the number of additional characters entered relative to the correct grammar in the input text.

[0295] The total number of characters (N) can represent the total number of characters when entered grammatically correct.

[0296] For example, assume that the input text is “Galaxy AI is har” and the grammatically correct text is “Galaxy AI is here”. The electronic device (100) can identify the number of missing characters (S) as 1 (e). The electronic device (100) can identify the number of replacement characters (D) as 1 (a). The electronic device (100) can identify the number of additional input characters (I) as 1 (a). The electronic device (100) can identify the total number of characters (N) as 14.

[0297] FIG. 20 is a diagram illustrating the operation of analyzing typing input according to one embodiment.

[0298] The embodiment (2010) of FIG. 20 may correspond to the embodiment (1910) of FIG. 19. Redundant description is omitted.

[0299] Referring to the embodiment (2020) of FIG. 20, the feature data may include a typing speed. The electronic device (100) may obtain a typing speed based on the last typing time, the first typing time, and the number of typings.

[0300] The electronic device (100) can obtain a first value by subtracting the first typing time (t1) from the final typing time (t17). The electronic device (100) can obtain a value obtained by dividing the first value by the number of typings (T) as the typing speed.

[0301] As another example, in the embodiment (2020) of FIG. 20, the electronic device (100) can obtain a typing speed by using the number of characters (L) instead of the number of typings (T).

[0302] Referring to the embodiment (2030) of FIG. 20, the feature data may include time intervals between words. The time interval between words may be described as a pause time. The time interval between words may represent a time interval during which a character related to a word is not entered.

[0303] For example, in the case of one word, the time interval between words may not be obtained.

[0304] For example, if there are two words, the time interval between the words can be obtained once.

[0305] For example, if there are three or more words, the time interval may be obtained two or more times. If the time interval is obtained two or more times, the electronic device (100) may obtain the time interval between words based on a representative value (e.g., an average value).

[0306] The electronic device (100) can obtain time intervals between words based on the start time of each word, the end time of each word, and the number of words.

[0307] The electronic device (100) can obtain a second value by subtracting the end point of word (n) from the start point of word (n+1). The electronic device (100) can obtain a third value by subtracting 1 from the number of words. The electronic device (100) can obtain the value obtained by dividing the second value by the third value as the time interval between words. If there are multiple time intervals between words, the electronic device (100) can obtain an average value as the time interval between words.

[0308] Word (n+1) is written as a subsequent word or the next word, and word (n) can be written as a preceding word, the current word, or the reference word.

[0309] It is assumed that user input includes the first word (galaxy), the second word (ai), the third word (is), and the fourth word (here).

[0310] The electronic device (100) can obtain the time interval between the first words (t8-t6) by subtracting the end time (t6) of the first word (galaxy) from the start time (t8) of the second word (ai).

[0311] The electronic device (100) can obtain the time interval between the second words (t11-t9) by subtracting the end time (t9) of the second word (ai) from the start time (t11) of the third word (is).

[0312] The electronic device (100) can obtain the time interval between the third words (t14-t12) by subtracting the end time (t12) of the third word (is) from the start time (t14) of the fourth word (here).

[0313] The electronic device (100) can obtain the time interval between words by dividing the sum of the first time interval between words (t8-t6), the second time interval between words (t11-t9), and the third time interval between words (t14-t12) by the number of time intervals (3). The number of time intervals may be the value obtained by subtracting 1 from the number of words.

[0314] Referring to the embodiment (2040) of FIG. 20, feature data can obtain time intervals between characters. The electronic device (100) can obtain time intervals between characters based on the input time of character (n+1), the input time of character (n), and the number of characters (L).

[0315] Character (n+1) is written as a successor character or the next character, and character (n) can be written as a preceding character, the current character, or the reference character.

[0316] For example, in the case of a single character, the time interval between characters may not be obtained.

[0317] For example, if there are two or more characters, the time interval between characters can be obtained one or more times. If the time interval between characters is two or more times, the electronic device (100) can obtain the time interval between characters based on an average value.

[0318] The electronic device (100) can obtain a fourth value by summing the time intervals between characters. The electronic device (100) can obtain a fifth value by subtracting 1 from the number of characters (L). The electronic device (100) can obtain the value obtained by dividing the fourth value by the fifth value as the time interval between characters.

[0319] FIG. 21 is a drawing for explaining a plurality of areas that distinguish an operation UI according to one embodiment.

[0320] Referring to the embodiment (2100) of FIG. 21, the electronic device (100) may provide a key UI. The key UI may include a plurality of keys. The electronic device (100) may receive user input through the key UI. The key UI may be displayed by a key layout structure corresponding to a pre-set language.

[0321] The electronic device (100) can divide the key UI into multiple regions. The electronic device (100) can classify user input into divided regions. The electronic device (100) can divide the key UI into at least one of a first region (21), a second region (22), or a third region (23).

[0322] The key UI can be divided into a character area and a non-character area. The character area may include a number area and a character area. The number area may include numbers from 0 to 9. The character area may represent an area that indicates characters corresponding to a specific language (e.g., the alphabet az). The non-character area may include keys that do not correspond to characters (delete key, enter key, space key, special key) and special symbols.

[0323] The first area (21) may correspond to the left part of the key UI within the character area. The second area (22) may correspond to the right part of the key UI within the character area. The third area (23) may represent a non-character area. Each area may be changed according to the user's settings. Therefore, depending on the implementation example, the first area (21) and the second area (22) may not necessarily contain only characters.

[0324] The electronic device (100) can identify user input entered into the first area (21), user input entered into the second area (22), and user input entered into the third area (23).

[0325] FIG. 22 is a diagram illustrating the operation of separating typing input by area according to one embodiment.

[0326] The embodiment (2200) of FIG. 22 may correspond to the embodiment (2100) of FIG. 21. The first region (21), the second region (22), and the third region (23) may correspond to the description of FIG. 21. Redundant descriptions are omitted.

[0327] For example, assume that user input typing "galaxy ai is here" is received. The electronic device (100) may receive user input including a first word (galaxy), a second word (ai), a third word (is), and a fourth word (here). The electronic device (100) may receive user input by pressing a space key between words. In the embodiment (2200) of FIG. 22, the order of user input is indicated by numbers.

[0328] The electronic device (100) can obtain key log information (2210). The key log information (2210) may include at least one of a sequence, a character, a time point, a region, and whether the region has moved.

[0329] The electronic device (100) can determine whether consecutive characters were entered in the same area or in a different area.

[0330] FIG. 23 is a diagram illustrating the operation of calculating the area movement time interval according to one embodiment.

[0331] Referring to the embodiment (2300) of FIG. 23, the feature data may include a region movement time interval. The electronic device (100) may obtain a region movement time interval based on the input time of a character (n+1), the input time of a character (n), and the number of region movements.

[0332] Character (n+1) is written as a successor character or the next character, and character (n) can be written as a preceding character, the current character, or the reference character.

[0333] The electronic device (100) can obtain a region movement time interval based on the key log information (2210) of FIG. 22.

[0334] FIG. 24 is a diagram illustrating the operation of separating typing input by area according to one embodiment.

[0335] Referring to the embodiment (2400) of FIG. 24, the key UI may include a first area (21) representing a number area among character areas, a second area (22) representing a character area among character areas, and a third area (23) representing a non-character area.

[0336] The electronic device (100) can obtain key log information based on user input entered into the first area (21), the second area (22), and the third area (23).

[0337] FIG. 25 is a diagram illustrating the operation of separating typing input by area according to one embodiment.

[0338] Referring to the embodiment (2500) of FIG. 25, the key UI may include a first area (21) representing a character area and a second area (22) representing a non-character area.

[0339] The electronic device (100) can obtain key log information based on user input entered into the first area (21), the second area (22), and the third area (23).

[0340] FIG. 26 is a diagram illustrating the operation of analyzing user input according to one embodiment.

[0341] Referring to the embodiment (2600) of FIG. 26, the electronic device (100) can receive user input including a swipe touch. The electronic device (100) can obtain typing information regarding the user input. The electronic device (100) can obtain feature data based on the typing information.

[0342] Feature data may include at least one of sliding gesture linearity, finger tremor frequency, finger tremor displacement, or gesture line velocity.

[0343] Sliding gesture linearity can indicate the degree of linearity of a movement when a user slides a finger across the screen. The closer the sliding trajectory is to a straight line, the higher the linearity value obtained. Linearity can be calculated as the deviation of the trajectory.

[0344] For example, a sliding gesture linearity can represent the cumulative difference between a straight line connecting a touch start point and a touch end point and sampled fingertip movements.

[0345] Finger tremor frequency can represent the frequency at which a finger vibrates. Frequency can be measured as the number of vibrations per second (Hz). The frequency can be obtained based on minute finger vibrations that occur when a user touches. Tremors exceeding a threshold frequency (or vibration rate) may be determined to be tremors caused by a physical abnormality rather than intentional movement.

[0346] Finger tremor displacement can represent the magnitude of the tremor that occurs when the finger touches. The electronic device (100) can obtain finger tremor displacement based on the change in position (distance traveled) of the finger during vibration. The larger the displacement, the more unstable the input may be judged.

[0347] The gesture line speed can be the value obtained by dividing the line length of an individual gesture by the time taken to input the line of the individual gesture. The individual gesture line length can represent the length of the line drawn when the user inputs the gesture.

[0348] The electronic device (100) can evaluate the user's cognitive ability based on swipe input.

[0349] For example, the lower the linearity of the sliding gesture, the lower the electronic device (100) can be evaluated as having a lower user's cognitive ability.

[0350] For example, the higher the frequency of finger tremors, the lower the electronic device (100) can evaluate the user's cognitive ability.

[0351] For example, the greater the finger tremor displacement, the lower the user's cognitive ability can be evaluated by the electronic device (100).

[0352] For example, the slower the speed of the gesture line, the lower the user's cognitive ability can be evaluated by the electronic device (100).

[0353] FIG. 27 is a drawing for explaining typing information corresponding to Korean and English according to one embodiment.

[0354] Referring to the embodiment (2700) of FIG. 27, the electronic device (100) can provide a key UI. The embodiment (2700) can display a key UI corresponding to Korean (KR). Depending on the setting language of the electronic device (100), the key UI can be changed to a UI corresponding to that language.

[0355] The table (2710) of FIG. 27 may represent typing information corresponding to Hangul (KR). Hangul (KR) may include an initial consonant, a medial vowel, and a medial vowel. User input may include an initial consonant and a medial vowel. A final consonant may be optionally entered.

[0356] The electronic device (100) can obtain the key movement time (tm1) between the initial consonant and the medial consonant.

[0357] The electronic device (100) can obtain the key movement time (tm2) between the neutral and final consonants.

[0358] The electronic device (100) can obtain the key movement time (tm3) between the initial consonant and the final consonant.

[0359] The electronic device (100) can obtain the ratio (rm1) of the key movement time between the medial and final consonants to the key movement time between the initial and final consonants (tm3*) when there is no final consonant.

[0360] The electronic device (100) can acquire typing information including at least one of a key movement time between an initial consonant and a medial consonant (tm1), a key movement time between a medial consonant and a final consonant (tm2), a key movement time between an initial consonant and a final consonant (tm3), and a ratio (rm1). The electronic device (100) can acquire feature data based on the typing information.

[0361] The table (2720) of FIG. 27 can represent typing information corresponding to all characters including the alphabet (EN) and Hangul (KR). All characters including the alphabet (EN) and Hangul (KR) can include consonants and vowels.

[0362] The electronic device (100) can obtain consonants and key transition times (tm4) between consonants.

[0363] The electronic device (100) can obtain the key movement time (tm5) between vowels and vowels.

[0364] The electronic device (100) can obtain the key transition time (tm6) between consonants and vowels.

[0365] The electronic device (100) can obtain typing information including at least one of a key travel time between consonants and consonants (tm4), a key travel time between vowels (tm5), and a key travel time between consonants and vowels (tm6).

[0366] The table (2730) of FIG. 27 can represent typing information corresponding to all characters, including the alphabet (EN) and Hangul (KR). All characters, including the alphabet (EN) and Hangul (KR), can be classified into right-hand keys and left-hand keys.

[0367] The electronic device (100) can obtain the travel time (tm7) between the right-hand key and the left-hand key.

[0368] The electronic device (100) can obtain the right-hand key and the time between the right-hand keys (tm8).

[0369] The electronic device (100) can obtain the left-hand key and the time between the left-hand keys (tm9).

[0370] The electronic device (100) can obtain typing information including at least one of the travel time between a right-hand key and a left-hand key (tm7), the travel time between a right-hand key and a right-hand key (tm8), and the travel time between a left-hand key and a left-hand key (tm9).

[0371] FIG. 28 is a diagram illustrating the typing pattern of a patient with reduced cognitive ability according to one embodiment.

[0372] The embodiment (2810) of FIG. 28 shows typing speed over time. The typing speed of a patient with reduced cognitive ability may be slower than the typing speed of a person with normal cognitive ability.

[0373] The embodiment (2820) of FIG. 28 shows the time interval between words over time. The time interval between words of a patient with reduced cognitive ability may be larger than the time interval between words of a person with normal cognitive ability.

[0374] The electronic device (100) can evaluate cognitive ability based on typing speed or time intervals between words.

[0375] FIG. 29 is a diagram illustrating the effect of time series data according to one embodiment.

[0376] Table (2900) of FIG. 29 shows the comparison results between a model that considers time series changes and a model that does not consider time series changes. Time series changes can represent past users' typing patterns.

[0377] Analyzing cognitive ability by considering past users' typing patterns can be more effective in terms of precision, recall, accuracy, and classification performance compared to cases where it is not.

[0378] FIG. 30 is a diagram illustrating the operation of acquiring feature data according to one embodiment.

[0379] Referring to FIG. 30, the electronic device (100) can receive user input. The electronic device (100) can obtain key log information (3010) based on the user input.

[0380] The electronic device (100) can obtain typing information (3020) based on key log information (3010). For example, the typing information may include at least one of the number of characters, the number of words, the number of enter keys, the number of space keys, the number of delete keys, or the number of typings.

[0381] The electronic device (100) can acquire feature data (3030) based on typing information (3020). For example, the feature data may include at least one of typing speed, touch pressure, pause time, delete key ratio, typing error rate, pause time (time interval between words), recommended word selection frequency, finger gesture speed, hand tremor frequency during swipe, and linearity during swipe.

[0382] FIG. 31 is a diagram illustrating the operation of acquiring representative data according to one embodiment.

[0383] Referring to FIG. 31, the electronic device (100) can obtain representative data (3100) based on the feature data (3030) of FIG. 30. The representative data (3100) may include representative values. The representative values ​​may include at least one of an average value, a minimum value, a maximum value, a variance value, a standard deviation value, and a change amount. In FIG. 31, only the average value, the variance value, and the change amount are described. Depending on the implementation example, the representative values ​​may be changed.

[0384] FIG. 32 is a drawing for illustrating a screen related to cognitive ability according to one embodiment.

[0385] Referring to FIG. 32, the electronic device (100) may provide a screen (3200) related to cognitive ability. The screen (3200) may include at least one of a UI (3210) for displaying a cognitive ability value, a UI (3220) for displaying a previous cognitive ability value, a UI (3230) for displaying the cognitive ability values ​​of users classified into a similar group to the user, a UI (3240) for explaining the cognitive ability value, a UI (3250) for guiding additional tests, and a UI (3260) for initialization.

[0386] When user input selecting UI (3250) is received, the electronic device (100) can provide a screen for additional testing related to cognitive ability.

[0387] When user input selecting UI (3260) is received, the electronic device (100) can initialize the cognitive ability.

[0388] FIG. 33 is a drawing for illustrating a screen related to cognitive ability according to one embodiment.

[0389] Referring to FIG. 33, the electronic device (100) may provide a screen (3300) related to cognitive ability. The screen (3300) may include at least one of a UI (3310) showing a history of cognitive ability values ​​that have changed over time, a UI (3320) showing an explanation related to cognitive ability values, and a UI (3330) for explaining the causes affecting cognitive ability values.

[0390] FIG. 34 is a drawing for illustrating a system including an electronic device (100) and a server (200) according to one embodiment.

[0391] Referring to FIG. 34, the electronic device (100) may be connected to a server (200). For example, the electronic device (100) may be connected to the server (200) through a communication circuit. The server (200) may be a server that provides services related to cognitive ability. The electronic device (100) may store an application for providing a cognitive ability judgment service. The server (200) may be a server that manages applications.

[0392] Referring to the embodiment (3410) of FIG. 34, the electronic device (100) can acquire a cognitive ability value. The electronic device (100) can acquire a cognitive ability value in an on-device form. The electronic device (100) may include an AI model (40). The electronic device (100) can transmit the cognitive ability value to a server (200). The server (200) can receive the cognitive ability value from the electronic device (100). The server (200) can store the cognitive ability value. When a user's device other than the electronic device (100) is connected to the server (200), the server (200) can provide the cognitive ability value to the other user's device.

[0393] Referring to the embodiment (3420) of FIG. 34, the electronic device (100) can transmit typing information (or key log information) to the server (200). The server (200) can receive the typing information (or key log information) from the electronic device (100). The server (200) can obtain a cognitive ability value based on the typing information (or key log information). The server (200) can transmit the cognitive ability value to the electronic device (100). Specific operations related to this are described in FIG. 35.

[0394] FIG. 35 is a diagram illustrating the operation of obtaining a cognitive ability value from a server (200) according to one embodiment.

[0395] The operations S3505, S3510, S3511, S3512, S3513, S3515, S3520, S3525, and S3530 of FIG. 35 may correspond to the operations S905, S910, S911, S912, S913, S915, S920, S925, and S930 of FIG. 9. Redundant description is omitted.

[0396] The electronic device (100) can obtain typing information (S3505). The electronic device (100) can transmit the typing information to the server (200) (S3506). As another example, the electronic device (100) can transmit key log information to the server (200).

[0397] The server (200) can receive typing information from the electronic device (100). The server (200) can acquire feature data based on the typing information (S3510). The server (200) can identify whether the previously stored first representative data is stored (S3511).

[0398] If the previously stored first representative data is not stored (S3511-N), the server (200) can obtain the first representative data based on the feature data (S3512). The server (200) can store the first representative data (S3513). The server (200) can wait for subsequent typing information.

[0399] If the first representative data that has been stored is stored (S3511-Y), the server (200) can obtain the second representative data by updating the first representative data based on the feature data (S3515).

[0400] The server (200) can store the second representative data (S3520). The server (200) can obtain a cognitive ability value based on the second representative data (S3525). The server (200) can transmit the cognitive ability value to the electronic device (100) (S3526).

[0401] The electronic device (100) can receive a cognitive ability value from the server (200). The electronic device (100) can provide a cognitive ability value (S3530). For example, the electronic device (100) can display the cognitive ability value through a display (140). For example, the electronic device (100) can output the cognitive ability value through a speaker.

[0402] FIG. 36 is a drawing for explaining a method of controlling an electronic device (100) according to one embodiment.

[0403] Referring to FIG. 36, a control method for an electronic device (100) includes the steps of: obtaining key log information when user input is received (S3610); obtaining typing information representing the user's typing result based on the key log information (S3620); obtaining feature data by extracting a preset feature based on the typing information (S3630); obtaining a second representative data by updating the first representative data based on the feature data when the feature data is obtained while the first representative data representing the user's typing pattern is stored in the electronic device (100) (S3640); and obtaining a cognitive ability value corresponding to the user based on the second representative data (S3650).

[0404] Key log information includes key information included in user input and information on the time when the key information was received, and typing information may include at least one of the number of characters, the number of words, the number of times a target key was entered, or the number of times it was typed.

[0405] Feature data may include at least one of a delete key ratio, typing speed, typing pause time, or key travel time.

[0406] The step of acquiring feature data (S3630) acquires the first typing time, the last typing time, and the number of typings based on key log information, and can acquire the typing speed by dividing the value obtained by subtracting the first typing time from the last typing time by the number of typings.

[0407] The step of acquiring feature data (S3630) can acquire the typing start time of the second word and the typing end time of the first word based on key log information including the first word and the second word, and acquire the typing stop time by subtracting the typing end time of the first word from the typing start time of the second word.

[0408] The step of acquiring feature data (S3630) can acquire the typing time of the second character and the typing time of the first character based on key log information including the first character and the second character, and acquire the key movement time by subtracting the typing time of the first character from the typing time of the second character.

[0409] The control method may include a step of determining that the user's cognitive ability is declining as the deletion key ratio increases, the typing speed decreases, the typing pause time increases, or the key movement time increases.

[0410] The first representative data may be data representing the user's timing pattern prior to the point in time when user input is received.

[0411] The first representative data includes at least one of an average value or a variance value to represent the feature data, and the step of obtaining the second representative data (S3640) can obtain the second representative data by updating at least one of the average value or variance value included in the first representative data based on the feature data.

[0412] The control method may include the step of generating a guide screen containing cognitive ability values ​​and the step of displaying the guide screen.

[0413] The methods according to the various embodiments of the present disclosure described above can be implemented in the form of an application that can be installed on an existing electronic device.

[0414] The methods according to the various embodiments of the present disclosure described above can be implemented by software upgrades or hardware upgrades alone for existing electronic devices.

[0415] The various embodiments of the present disclosure described above may also be performed through an embedded server equipped in an electronic device, or through an external server among at least one of the electronic device and the display device.

[0416] According to a specific example of the present disclosure, the various embodiments described above may be implemented as software comprising instructions stored on a machine-readable storage medium (e.g., a computer). The machine may include an electronic device according to the disclosed embodiments, which is a device capable of calling instructions stored from the storage medium and operating according to the called instructions. When instructions are executed by a processor, the processor may perform a function corresponding to the instructions directly or by using other components under the control of the processor. Instructions may include code generated or executed by a compiler or an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory" means only that the storage medium does not contain a signal and is tangible, and does not distinguish whether data is stored semi-permanently or temporarily in the storage medium.

[0417] According to one embodiment of the present disclosure, the method according to the various embodiments described above may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or online through an application store. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a storage medium such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0418] Each component (e.g., module or program) according to the various embodiments described above may be composed of a single or multiple entities, and some of the aforementioned sub-components may be omitted, or other sub-components may be additionally included in the various embodiments. Generally or additionally, some components (e.g., module or program) may be integrated into a single entity to perform the functions performed by each of the respective components prior to integration in the same or similar manner. The operations performed by the module, program, or other components according to the various embodiments may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations added.

[0419] Although preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the scope of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical spirit of the present disclosure.

Claims

In electronic devices, Memory for storing instructions; and at least one processor including processing circuitry; and When the above instructions are executed individually or collectively by the at least one processor, the electronic device, When user input is received, key log information is obtained, and Based on the above key log information, typing information representing the user's typing result is obtained, and Feature data is obtained by extracting pre-set features based on the above typing information, and When the feature data is acquired while the first representative data representing the typing pattern of the above user is stored in the memory, the second representative data is acquired by updating the first representative data based on the feature data, and An electronic device that obtains a cognitive ability value corresponding to the user based on the second representative data above. In paragraph 1, The above key log information is, It includes key information included in the above user input and information on the time when the above key information is received, and The above typing information is, An electronic device comprising at least one of a number of characters, a number of words, a number of inputs of a target key, or a number of typings. In paragraph 1, The above feature data is, An electronic device comprising at least one of a delete key ratio, a typing speed, a typing pause time, or a key travel time. In paragraph 3, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Based on the above key log information, the initial typing time, the final typing time, and the number of typings are obtained, and An electronic device that obtains the typing speed by dividing the value obtained by subtracting the first typing time from the final typing time by the number of typings. In paragraph 3, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Based on the key log information including the first word and the second word, the typing start time of the second word and the typing end time of the first word are obtained, and An electronic device that obtains the typing stop time by subtracting the typing end time of the first word from the typing start time of the second word. In paragraph 3, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Based on the key log information including the first character and the second character, the typing time of the second character and the typing time of the first character are obtained, and An electronic device that obtains the key movement time by subtracting the typing time of the first character from the typing time of the second character. In paragraph 3, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that determines that the user's cognitive ability is reduced as the above deletion key ratio increases, the above typing speed decreases, the above typing pause time increases, or the above key movement time increases. In paragraph 1, The above first representative data is, An electronic device, which is data representing the timing pattern of the user prior to the point in time when the user input is received. In paragraph 8, The above first representative data is, It includes at least one of an average value or a variance value to represent feature data, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that obtains the second representative data by updating at least one of the mean value or the variance value included in the first representative data based on the feature data. In paragraph 1, The above electronic device is, Includes a display; When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Generate a guide screen including the above cognitive ability value, and An electronic device that displays the guide screen through the above display. In a method for controlling an electronic device, When user input is received, a step of obtaining key log information; A step of obtaining typing information representing the user's typing result based on the above key log information; A step of obtaining feature data by extracting pre-set features based on the above typing information; A step of obtaining a second representative data by updating the first representative data based on the feature data when the first representative data representing the typing pattern of the user is stored in the electronic device; and A control method comprising the step of obtaining a cognitive ability value corresponding to the user based on the second representative data above. In Paragraph 11, The above key log information is, It includes key information included in the above user input and information on the time when the above key information is received, and The above typing information is, A control method comprising at least one of a character count, a word count, a number of inputs of a target key, or a number of typings. In Paragraph 11, The above feature data is, A control method comprising at least one of a delete key ratio, a typing speed, a typing pause time, or a key travel time. In Paragraph 13, The step of acquiring the above feature data is, Based on the above key log information, the initial typing time, the final typing time, and the number of typings are obtained, and A control method for obtaining the typing speed by dividing the value obtained by subtracting the first typing time from the final typing time by the number of typings. In Paragraph 13, The step of acquiring the above feature data is, Based on the key log information including the first word and the second word, the typing start time of the second word and the typing end time of the first word are obtained, and A control method for obtaining the typing stop time by subtracting the typing end time of the first word from the typing start time of the second word.

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