WO2024029820A1

WO2024029820A1 - Remind method and apparatus for increasing compliance with learning digital therapeutics

Info

Publication number: WO2024029820A1
Application number: PCT/KR2023/010809
Authority: WO
Inventors: 한승현
Original assignee: (주)로완
Priority date: 2022-08-05
Filing date: 2023-07-26
Publication date: 2024-02-08
Also published as: KR20240020026A

Abstract

According to an embodiment of the present specification, a remind method for increasing the compliance with learning digital therapeutics may comprise the steps of: conducting user learning on the basis of content; detecting a user learning interruption input; identifying a user learning interruption time point; and setting a user learning reminder.

Description

Reminder method and device to increase compliance with learning digital therapeutics

The present invention relates to a reminder method and device for increasing compliance with learning digital therapeutics. More specifically, it is about a method of setting up the LaMind method to increase the learning compliance of users performing digital therapeutics learning.

As average life expectancy increases due to advances in medicine, aging is progressing worldwide. In the case of Korea, the proportion of the elderly population aged 65 or older was 7.2% of the total population in 2000, entering an aging society, and in 2017, the proportion of the elderly population exceeded 14%, entering an aging society. It is predicted that by 2030, the proportion of the elderly population in Korea will reach 24.3%, making Korea a super-aging society.

Due to the effects of this rapid aging, the dementia population is rapidly increasing. As of 2010, the global dementia population reached approximately 35.6 million, and by 2050, the dementia population is expected to increase approximately three-fold to 115.4 million. In Korea, the prevalence of dementia by future years, estimated based on age, gender, education, and residential area standardized dementia prevalence based on the 2005 population census, is 9.08% in 2012, 9.74% in 2020, 9.61% in 2030, and 2040. It is predicted that the dementia population will increase to 11.21% and 13.17% in 2050, exceeding 1 million in 2027, and reaching 2.12 million in 2050.

As the dementia population rapidly increases, the cost of dementia treatment and management is also increasing. The social cost of dementia is estimated to exceed the combined cost of all three diseases - cancer, heart disease, and stroke. In Korea, the total annual medical cost due to dementia was KRW 810 billion as of 2010, ranking second among geriatric diseases, 1 Dementia treatment costs per person are 3.1 million won per year, the highest among the five major chronic diseases. Additionally, Korea's total national dementia cost is estimated to double every 10 years to 8.7 trillion won per year.

In order to reduce the rapidly increasing social and economic burden of dementia, it is necessary to delay the onset of dementia through early detection and active treatment of high-risk dementia groups. As a conventional technology for this purpose, Registered Patent Publication No. 10-1295187 is a brain function improvement system and its operation that performs the function of preventing dementia by giving the user a level of achievement according to the score obtained according to the user's feedback regarding the selected content. Disclose the method.

However, conventional cognitive intervention treatments for dementia prevention and treatment require visits to institutions such as hospitals and are administered under the supervision of experts, so in most cases, high treatment costs of 50,000 to 120,000 won per treatment must be paid. In addition, cognitive intervention therapy programs using computing devices have been developed, but the high cost of up to 50 million won per device makes them difficult for general users to use. In the following, in consideration of the above-mentioned points, we will describe a method of using user viewpoint tracking as a digital therapeutic and a method to increase learning compliance of users using digital therapeutics.

[Prior art literature]

[Patent Document]

Korean Patent Publication No. 10-1295187

This specification relates to a reminder method and device for increasing compliance with learning digital therapeutics.

This specification relates to a method and device for confirming digital therapeutic learning compliance through the user learning progress status.

This specification relates to a method and device for setting different reminder methods to provide users based on learning compliance with digital therapeutics.

This specification relates to a method and device for determining a user's cognitive state based on user viewpoint tracking and providing content in consideration of the determined user's cognitive state.

The problem to be solved by the specification is not limited to the above, and can be expanded to various matters that can be derived by the embodiments of the invention described below.

According to an embodiment of the present specification, in the reminder method for increasing compliance with digital therapeutics learning, the steps include performing user learning based on content, detecting user learning interruption input, and confirming the point of user learning interruption. And it may include the step of setting a user learning reminder.

In addition, according to an embodiment of the present specification, in the cognitive ability development server that performs reminders to increase compliance with learning digital therapeutics, the cognitive ability development server includes a content provision module, an input module, an analysis module, and a machine learning module. Based on the content provision module, user learning including the content can be performed, user learning interruption input can be detected based on the input module, user learning interruption point can be confirmed, and user learning reminder setting can be performed. .

According to an embodiment of the present specification, user learning includes at least one major task and at least one detailed task within the major task, and user learning can be completed only when all of at least one major task is completed.

In addition, according to an embodiment of the present specification, the step of detecting the user learning interruption point determines whether the user learning interruption point is when at least one or more detailed tasks within the main task are completed or while performing at least one or more detailed tasks. It is possible to detect whether it is the time point and provide different reminder information based on the user learning reminder setting depending on the point in time when the user stops learning.

In addition, according to an embodiment of the present specification, when the time when the user stops learning is when at least one detailed task within the major task is completed, only reminder information inquiring about whether to continue or end learning is provided to the user, and the user If the learning interruption point is during a detailed task, reminder information related to learning restart can be provided in addition to the reminder information asking the user whether to continue or end learning.

In addition, according to an embodiment of the present specification, when the user's learning interruption point is during a detailed task, it is detected that learning is stopped based on either the user's personal reason or the reason for the influence of learning progress, and based on the user's personal reason When learning is interrupted, reminder information may be provided based on the first interface, and when learning is interrupted based on reasons affecting learning progress, reminder information may be provided based on the second interface.

In addition, according to an embodiment of the present specification, the reminder information provided based on the first interface is reminder information related to the user's learning restart, and the reminder information provided based on the second interface is reminder information related to the user's learning restart and This may be reminder information related to learning difficulty adjustment.

In addition, according to an embodiment of the present specification, when it is detected that learning is stopped based on any of the user's personal reasons or reasons affecting the learning progress, user status information is further derived, and the first An interface or a second interface may be determined.

Additionally, according to an embodiment of the present specification, user status information may be obtained through information tracking at least one of the user's face, the user's hands, and the user's body.

Additionally, according to an embodiment of the present specification, when a user learning reminder is set, a notification about the user learning progress may be provided or the user learning progress may be re-executed after a preset time based on the reminder setting information.

In addition, according to an embodiment of the present specification, when the user restarts user learning based on the user learning reminder setting, reminder setting-related information is provided to the user, and the user's input is detected and reminder setting-related information is provided. After data conversion, user learning can be restarted.

This specification can provide a reminder method to increase compliance with learning digital therapeutics.

The present specification may provide a method for confirming digital therapeutic learning compliance through the user learning progress status.

The present specification may provide a method of setting different reminders provided to users based on digital therapeutic learning compliance.

This specification can determine the user's cognitive state based on user viewpoint tracking and provide content by considering the determined user's cognitive state.

It should be understood that the effect of the present specification is not limited to the matters described above, and can be expanded to various contents that can be derived from the detailed description of the embodiments of the invention below.

1 is a schematic block diagram of a cognitive ability development system according to an embodiment.

Figure 2 is a flowchart showing each step of a cognitive ability development method according to an embodiment.

Figure 3 is a conceptual diagram illustrating a data processing process by a cognitive ability development method according to an embodiment.

Figure 4 is a diagram showing a method for tracking a user's viewpoint according to an embodiment.

FIG. 5A is a diagram illustrating a method of extracting user information and upgrading a digital therapeutic product based on user viewpoint tracking according to an embodiment.

Figure 5b is a diagram illustrating a method of extracting user information and upgrading a digital therapeutic product based on user viewpoint tracking according to an embodiment.

FIG. 5C is a diagram illustrating a method of extracting user information and upgrading a digital therapeutic product based on user viewpoint tracking according to an embodiment.

FIG. 5D is a diagram illustrating a method of extracting user information and upgrading a digital therapeutic product based on user viewpoint tracking according to an embodiment.

FIG. 6A is a diagram illustrating a method for tracking a user's hand and enhancing a digital therapeutic agent according to an embodiment.

FIG. 6B is a diagram illustrating a method for tracking a user's hand and enhancing a digital therapeutic agent according to an embodiment.

FIG. 6C is a diagram illustrating a method for tracking a user's hand and improving digital therapeutics according to an embodiment.

FIG. 7A is a diagram illustrating a method for tracking the user's viewpoint and hand and upgrading a digital therapeutic agent according to an embodiment.

Figure 7b is a diagram illustrating a method for tracking the user's viewpoint and hand and upgrading a digital therapeutic agent according to an embodiment.

FIG. 8A is a diagram illustrating a method of adjusting a virtual image based on tracking according to an embodiment.

FIG. 8B is a diagram illustrating a method of adjusting a virtual image based on tracking according to an embodiment.

FIG. 9A is a diagram illustrating a method of determining user status based on tracking according to an embodiment.

FIG. 9B is a diagram illustrating a method of determining user status based on tracking according to an embodiment.

Figure 10 is a diagram showing a digital therapeutics learning task according to one embodiment.

Figure 11 is a diagram showing the progress of a digital therapeutics learning task according to an embodiment.

FIG. 12A is a diagram illustrating a method of determining learning compliance based on a user's learning completion point according to an embodiment.

Figure 12b is a diagram illustrating a method of providing a reminder based on a user's learning compliance, according to an embodiment.

FIG. 13A is a diagram illustrating a method of determining learning compliance based on a user's learning completion point according to an embodiment.

Figure 13b is a diagram illustrating a method of providing a reminder based on a user's learning compliance according to an embodiment.

FIG. 13C is a diagram illustrating a method of providing a reminder based on a user's learning compliance according to an embodiment.

FIG. 13D is a diagram illustrating a method of providing a reminder based on a user's learning compliance according to an embodiment.

Figure 14 is a diagram illustrating a method of providing a reminder by comparing a user's learning compliance with other users according to an embodiment.

FIG. 15A is a diagram illustrating a method of performing learning reset based on a reminder according to an embodiment.

FIG. 15B is a diagram illustrating a method of performing learning reset based on a reminder according to an embodiment.

FIG. 16 is a diagram illustrating a method of performing learning reset based on a reminder according to an embodiment.

Figure 17 is a flowchart showing a reminder method to increase compliance with learning digital therapeutics according to an embodiment.

In describing the embodiments of the present specification, if it is determined that a detailed description of a known configuration or function may obscure the gist of the embodiments of the present specification, the detailed description thereof will be omitted. In addition, in the drawings, parts that are not related to the description of the embodiments of the present specification are omitted, and similar parts are given similar reference numerals.

In the embodiments of the present specification, when a component is said to be “connected,” “coupled,” or “connected” to another component, this refers not only to a direct connection relationship, but also to an indirect relationship where another component exists in between. Connection relationships may also be included. In addition, when a component is said to "include" or "have" another component, this does not mean excluding the other component, but may further include another component, unless specifically stated to the contrary. .

In the embodiments of this specification, terms such as first, second, etc. are used only for the purpose of distinguishing one component from other components, and do not limit the order or importance of components unless specifically mentioned. No. Therefore, within the scope of the embodiments of the present specification, the first component in an embodiment may be referred to as a second component in another embodiment, and similarly, the second component in the embodiment may be referred to as the first component in another embodiment. It may also be called.

In the embodiments of the present specification, distinct components are intended to clearly explain each feature, and do not necessarily mean that the components are separated. That is, a plurality of components may be integrated to form one hardware or software unit, or one component may be distributed to form a plurality of hardware or software units. Therefore, even if not specifically mentioned, such integrated or distributed embodiments are also included in the scope of the embodiments of the present specification.

In this specification, a network may be a concept that includes both wired and wireless networks. At this time, the network may refer to a communication network in which data exchange between devices, systems, and devices can be performed, and is not limited to a specific network.

Embodiments described herein may have aspects that are entirely hardware, partly hardware and partly software, or entirely software. In this specification, “unit,” “device,” or “system” refers to computer-related entities such as hardware, a combination of hardware and software, or software. For example, in this specification, a part, module, device, or system refers to a running process, processor, object, executable, thread of execution, program, and/or computer. It may be a (computer), but is not limited thereto. For example, both an application running on a computer and the computer may correspond to a part, module, device, or system in the present specification.

Additionally, in this specification, the device may be a mobile device such as a smartphone, tablet PC, wearable device, and HMD (Head Mounted Display), as well as a fixed device such as a PC or a home appliance with a display function. Additionally, as an example, the device may be a cluster within a vehicle or an Internet of Things (IoT) device. That is, in this specification, a device may refer to devices capable of operating an application, and is not limited to a specific type. In the following, for convenience of explanation, the device on which the application runs is referred to as a device.

In this specification, the network communication method is not limited, and connections between each component may not be connected through the same network method. The network may include not only a communication method utilizing a communication network (for example, a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.), but also short-range wireless communication between devices. For example, a network can include objects and any communication method through which objects can be networked, and is not limited to wired communication, wireless communication, 3G, 4G, 5G, or other methods. For example, wired and/or networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), High Speed Downlink Packet Access (HSDPA), W-CDMA (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth, Zigbee, Wi-Fi, VoIP (Voice over Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and It may refer to a communication network using one or more communication methods selected from the group consisting of MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and ultrasonic communication. However, it is not limited to this.

Components described in various embodiments do not necessarily mean essential components, and some may be optional components. Accordingly, embodiments consisting of a subset of the elements described in the embodiments are also included in the scope of the embodiments of the present specification. In addition, embodiments that include other components in addition to the components described in the various embodiments are also included in the scope of the embodiments of the present specification.

Hereinafter, embodiments of the present specification will be examined in detail with reference to the drawings.

Referring to Figure 1, the cognitive ability development system 2 according to this embodiment includes a content provision module 21, an input module 22, an analysis module 23, and a machine learning module 24. do. Additionally, each module 21-24 may include one or more functional units.

The cognitive ability development system 2 according to embodiments and each module 21 to 24 or units included therein may be entirely hardware, or may have aspects that are partly hardware and partly software. For example, each module 21-24 or unit of the cognitive ability development system 2 may collectively refer to hardware and software related thereto for processing data of a specific format and content and/or exchanging data through electronic communication. In this specification, terms such as “unit,” “module,” “device,” “terminal,” “server,” or “system” are intended to refer to a combination of hardware and software driven by the hardware. For example, hardware may be a data processing device that includes a CPU or other processor. Additionally, software driven by hardware may refer to a running process, object, executable, thread of execution, program, etc.

Additionally, each element constituting the cognitive ability development system 2 is not necessarily intended to refer to a separate device that is physically distinct from each other. In other words, the content provision module 21, input module 22, analysis module 23, and machine learning module 24 in FIG. 1 are the hardware constituting the cognitive ability development system 2 and the operations performed by the hardware. It is only functionally divided according to the function, and each part does not necessarily have to be provided independently from each other. Of course, depending on the embodiment, it is possible for one or more of the content provision module 21, the input module 22, the analysis module 23, and the machine learning module 24 to be implemented as separate devices that are physically separated from each other. .

The content provision module 21 is configured to provide users with content for cognitive ability development. For example, the content providing module 21 is a part for transmitting content to the user device 1 so that the content can be presented to the user using a display means and a sound output means provided in the user device 1. You can. For the above operations, the cognitive development system 2 may communicate with the user device 1 via a wired and/or wireless network. In this specification, communication methods through wired and/or wireless networks may include objects and all communication methods through which objects can be networked, and are not limited to wired communication, wireless communication, 3G, 4G, or other methods. .

In FIG. 1, the user device 1 is shown in the form of a tablet computer, but this is an example. The user device 1 may be a mobile computing device such as a smartphone, a laptop computer, or a personal computer. It may be composed of any computing device such as a PC).

In one embodiment, the cognitive development system 2 performs, at least in part, the function of an application server that allows the application to perform its functions by communicating with an application (or app) running on the user device 1. can do. In addition, the cognitive ability development system 2 is, at least in part, a web server that provides a web page accessible through a web browser running on the user device 1. It can also perform a function.

However, this is an example, and in other embodiments, the cognitive ability development system 2 itself may be configured in the form of a device used by the user, in which case the user can directly use the content through the cognitive ability development system 2. Therefore, the user device 1 shown in FIG. 1 may be omitted.

In embodiments of the present invention, cognitive ability development content presented through the content provision module 21 may be related to a plurality of cognitive areas. In this specification, a plurality of cognitive domains refers to a multifaceted field for encouraging users to engage in activities to improve cognitive abilities from different perspectives in order to improve cognitive abilities, such as preventing or slowing down the decline in cognitive abilities due to dementia, etc. means.

For example, in one embodiment, the cognitive ability development system 2 may divide content that needs to be managed to improve cognitive ability into five cognitive areas: vascular disease management, cognitive training, exercise, nutritional management, and motivation enhancement. . At this time, the cognitive ability development content provided by the cognitive ability development system (2) aims to improve the ability of one or multiple cognitive domains among the five cognitive domains described above, and is developed with the goal of strengthening a specific cognitive domain. This can be done, and in this specification, this is referred to as the characteristics of cognitive ability development content. For example, cognitive ability development content may take the form of a game aimed at strengthening abilities in a specific field.

The input module 22 is configured to receive the user's learning data about content provided to the user through the content providing module 21 from the user device 1 . Alternatively, when the cognitive ability development system 2 is implemented in the form of a user device such as a user's personal computer, the input module 22 can receive learning data through an input means (not shown) such as a keyboard or touch screen. .

The analysis module 23 is configured to analyze the learning data input to the input module 22 and calculate performance information for a plurality of cognitive areas. For example, the analysis module 23 may determine performance information based on cognitive scores for content (e.g., games) in each cognitive domain, time spent on content in each domain, progress rate and/or participation rate for each domain, etc. .

To this end, in one embodiment, the analysis module 23 includes a result analysis unit 231 that analyzes the cognitive score, which is the user's learning result for each area, and/or the time and content spent on achieving the goal of the content in each area. It may include a process analysis unit 232 that analyzes information on consumption time, such as the user's response time.

Also in one embodiment, the analysis module 23 further includes a profiling unit 233. In this embodiment, the user can input his/her personal information, such as gender, age, living environment (number of household members, etc.), and medical history, such as whether or not he or she has been diagnosed with dementia in the past, through the input module 22. The profiling unit 233 is configured to determine a reference value for deriving performance information from learning data based on such personal information. At this time, the standard value is information about the section for converting the recognition score or consumption time into a performance value, weight information to be applied to the recognition score or consumption time, or the user's gender/age/living environment/medical history, etc. affect the performance value. It can refer to any other arbitrary way of adjusting the value to affect .

The machine learning module 24 stores the results of learning by machine learning using a pre-entered training data set, and reports performance information obtained by the analysis module 23. It functions to determine the characteristics of user-customized content by applying the learning results of machine learning.

For the above operation, the machine learning module 240 includes a machine learning model learning unit 241 that performs machine learning using a training data set, and a machine learning unit that determines customized content for the user's performance information based on the learning results. It may include a model determination unit 242.

In one embodiment, the input module 22 may further perform the function of receiving a training data set and inputting it to the machine learning model learning unit 241 for machine learning. At this time, the input module 22 may include a data labeling unit 222. The data labeling unit 222 trains the data included in the training data set using customized content information (i.e., correct answer) determined by experts or clinical results as suitable for improving the cognitive ability of the user who provided the data. Data in the data set may be labeled, and the labeled data may be provided to the machine learning model learning unit 241.

Additionally, the input module 22 may further include a personal information processing unit 223. The personal information processing unit 223 may perform masking on personal information so that sensitive personal information of each user is not exposed when the machine learning model learning unit 241 performs learning using the training data set. there is. In this case, masking may mean deleting the sensitive information itself from the training data set, or dividing, combining, or dividing the data so that it is impossible to match the data records with the individual who provided them even if the information is not deleted. It can refer to reconstruction.

The machine learning model determination unit 242 of the machine learning module 24 determines the type of user corresponding to the user's performance information based on the data previously accumulated by the machine learning model learning unit 241, and determines the type of user corresponding to the user's performance information. You can determine the characteristics of customized content that is suitable for you. For example, determining the type of user may mean classifying or clustering users based on patterns of performance shown by the user in multiple cognitive domains, and the machine learning model determination unit 242 ) can use these types to determine customized content suitable for improving the cognitive abilities of the corresponding user category or group.

Figure 2 is a flowchart showing each step of a cognitive ability development method according to an embodiment. For convenience of explanation, the cognitive ability development method according to this embodiment will be described with reference to FIGS. 1 and 2.

Referring to FIG. 2, first, as a machine learning-based learning process, the input module 22 of the cognitive ability development system 2 can receive a training data set (S11). The training data set may be input or transmitted to the input module 22 from the administrator's user device, or may be received from an external server (not shown).

As a preprocessing process for performing machine learning using the training data set, the data labeling unit 222 of the input module 22 uses customized content information determined based on expert or clinical information based on each data in the training data set. Thus, the data can be labeled (S12). Additionally, the personal information processing unit 223 of the input module 22 may remove sensitive information from data in the training data set or perform masking to prevent sensitive information from being associated with the individual data provider (S12).

The machine learning module 24 of the cognitive ability development system 2 can receive data for which labeling and masking have been completed and use this to generate learning results for determining customized content from the learning data (S13). For example, the learning result refers to determining the type of user based on the pattern of the learning data and determining the characteristics of the content suitable to be applied to that type of user, such as the type and number of cognitive areas corresponding to the content. It may be for.

The machine learning learning process (S11-S13) described above may be performed by the machine learning model learning unit 241 of the machine learning module 24. However, in another embodiment, the machine learning module 24 may receive algorithms and parameters corresponding to machine learning results from an external device or server and operate based on them. In this case, the above-described learning process (S11- S13) may be omitted.

In order to provide customized content to target users based on learning results by machine learning, first, the content provision module 21 of the cognitive ability development system 2 provides cognitive ability development content with characteristics of a plurality of cognitive areas to the user. Can be provided (S21).

Next, the input module 22 of the cognitive ability development system 2 may receive the user's learning data about the provided content (S22). For example, learning data refers to the cognitive score achieved by a user solving a task provided through content in a game-like format, the time the user spends on completing the task, or a series of tasks within the content. It may include the user's progress rate or participation rate.

In one embodiment, the input module 22 may further receive personal information of a user participating in the content simultaneously with receiving the learning data or before or after receiving the learning data (S23). For example, personal information may include, but is not limited to, information such as the user's gender/age/living environment/medical history.

The analysis module 23 of the cognitive ability development system 2 may derive performance information suitable for determining the type of user indicated by the learning data from the user's learning data (S24). For example, performance information refers to the recognition score, time spent, progress rate, participation rate, etc. achieved by the user for the content, or converts this information into a performance value based on certain criteria (grade, threshold, weight, etc.) It may have been converted to .

The machine learning module 24 of the cognitive ability development system 2 can determine user-customized content based on the performance information derived by the analysis module 23 (S25). This may mean determining the characteristics (type, difficulty, time limit, etc.) of cognitive ability development content suitable to be presented next to that user based on the type of user determined through the machine learning model, which is described in detail. This will be described later.

Referring to FIG. 3, in the cognitive ability development method according to embodiments of the present invention, information such as usage history, progress rate, recognition score, and/or participation rate is derived from the user's learning data, and this is used as performance information to provide the user with information such as use history, progress rate, recognition score, and/or participation rate. You can decide on customized content to be provided.

As an example, the following describes a method of providing advanced digital therapeutics by tracking the user's viewpoint when analyzing the user and providing content based on the cognitive ability development system 2 described above. As an example, a digital therapeutic may be a software medical device. More specifically, the user's cognitive state information is analyzed based on the above-described cognitive ability development system (2), and content is provided to the user based on the analyzed user's cognitive state information to improve the user's cognitive ability to prevent disease (e.g. dementia). ) may be a software medical device aimed at the prevention, management, and treatment of As an example, a digital therapeutic may be a software medical device that collects, manages, and stores user data to enable customized analysis and treatment for patients. As an example, a digital therapeutic may mean improving a user's cognitive ability by analyzing the user and providing content based on the cognitive ability development system 2 as described above, and is not limited to a specific embodiment. Below, for convenience of explanation, a digital treatment performed based on the cognitive ability development system 2 is described, but may not be limited thereto.

As an example, the cognitive ability development system 2 may determine the user's cognitive state by analyzing user information using user viewpoint tracking. At this time, a camera may be used to track the user's viewpoint. As a specific example, a camera may be mounted on the user device 1 and track the viewpoint (or gaze), face, hands, and other body of the user who receives content. For example, the camera is a webcam that can be mounted on a Windows OS-based tablet or desktop and can extract the user's viewpoint (or gaze), face, palm, and other coordinates of the body. At this time, the cognitive ability development system (2) can determine the user's cognitive state by continuously tracking the coordinate values extracted through the camera, and through this, the cognitive ability development system (2) as a digital therapeutic agent can accurately recognize the user's state. You can.

As a specific example, FIG. 4 is a diagram showing a method for tracking a user's viewpoint according to an embodiment. Referring to FIG. 4, the camera 3 can be installed in the tablet 1 and other devices described above. As an example, the camera 3 may be attached to the top of a device that provides content to a user or may be combined with the device, and is not limited to a specific embodiment. Here, as described above, the camera 3 can derive coordinate values by extracting at least one of the user's hand 41, face 42, and viewpoint (or gaze 43). At this time, the cognitive ability development system 2 can determine the user's cognitive state by tracking changes in coordinate values extracted through the camera 3. For example, when the camera 3 tracks the user's hand 41, the center point of the user's hand 41 may be set as a reference point for the coordinate value, and tracking may be performed based on this. As another example, coordinate values of each point of the user's hand 41 may be derived, and tracking of the user's hand 41 may be performed through the average of the derived coordinate values. As another example, when the camera 3 tracks the face 42, the center point of the face 42 may be set as a reference point for the coordinate value, and tracking may be performed based on this. As another example, coordinate values of each point of the face 42 may be derived, and tracking of the face 42 may be performed by averaging the derived coordinate values. As another example, when the camera 3 tracks the viewpoint 43, the center point of the user's eyes may be set as a reference point for the coordinate value, and tracking may be performed based on this. As another example, coordinate values of each point of the user's eyes can be derived, and tracking of the user's eyes can be performed by averaging the derived coordinate values.

Through the above, the cognitive ability development system can track the user's hands (41), face (42), viewpoint (43), and other bodies, and through this, determine the user's state and provide content tailored to the user. User cognitive ability can be improved efficiently.

As a specific example, FIGS. 5A to 5D are diagrams illustrating a method of extracting user information and upgrading a digital therapeutic product based on user viewpoint tracking according to an embodiment. Referring to FIG. 5A, the cognitive ability development system 2 may provide content to the user based on user cognitive state information. As an example, content may be provided to the user through the content provision module 21 of the cognitive ability development system 2. At this time, the user can perform learning based on the content provided by the cognitive ability development system 2, and through this, the cognitive ability development system 2 can check the user's cognitive state. As a specific example, the input module 22 of the cognitive ability development system 2 may be configured to receive user learning data about content provided to the user through the content provision module 21 from the user device 1. For example, when the cognitive ability development system 2 is implemented in the form of a user device such as a user's personal computer, the input module 22 can receive learning data through an input means (not shown) such as a keyboard or touch screen. there is.

At this time, the analysis module 23 may acquire user cognitive state information by analyzing the learning data input to the input module 22. As an example, user cognitive state information may be configured to calculate performance information for a plurality of cognitive areas. The analysis module 23 may determine performance information based on at least one of the cognitive score for the content (e.g. game) of each cognitive domain, the time consumed for the content of each domain, and the progress rate and participation rate for each domain, through which User cognitive state information can be derived.

As a specific example, referring to FIG. 5A, the cognitive ability development system 2 may provide question information 501 and answer information 502 to the user as content. As an example, the user can derive answer information as learning data based on the question information 501 and input it into the answer information 502. Additionally, as an example, the cognitive ability development system 2 may provide

related content

51, 52, 53, 54, 55, 56, and 57 in consideration of learning. At this time, learning data can be generated by the user entering related content (51, 52, 53, 54, 55, 56, 57) into the answer information (502), and the cognitive ability development system (2) generates the generated learning data. can be obtained. As a specific example, in FIG. 5A, consonants/vowels may be provided to the user as fruit-shaped content to input an answer to a question. At this time, the user can complete the answer information 502 by selecting a fruit containing a consonant or vowel corresponding to the answer information, and through this, learning data can be generated. At this time, as an example, the learning data may include information about whether the user inputs the answer information 502 corresponding to a preset value. In other words, information about whether the user answered correctly can be obtained.

As another example, the learning data may further include time information at which the user inputs an answer into the answer information 502. For example, if the input time of the user's answer information 502 is short, the user's cognitive state may be judged to be of a high level, and if the input time of the user's answer information 502 is long, the user's cognitive state may be judged to be of a low level. As another example, the cognitive ability development system 2 may generate learning data by further tracking user viewpoint information. More specifically, referring to FIG. 5b, the cognitive ability development system 2 derives the coordinate value for the user viewpoint 43 through the camera 3 and tracks the viewpoint 43, as described above. You can. As a specific example, the user's viewpoint 43 checks the question information 501 and then moves toward checking the answer information 502, and then moves to content 51, 52, 53, 54, 55 for inputting the answer information 502. , 56, 57). Through this, the cognitive ability development system 2 can check how the user perceives the provided content.

As an example, referring to FIG. 5C, it can be confirmed that the time point 43 at which the user is tracked in the question information 501 and the answer information 502 is tracked more than a preset number of times. That is, it can be confirmed that the user reads the question information 501 and the answer information 502 multiple times, which confirms that the user's cognitive state is at a low level. As another example, the user's viewpoint 43 is maintained for a long period of time in the

content

51, 52, 53, 54, 55, 56, 57 for inputting the answer information 502, but the answer information 502 is not derived. In this case, the cognitive ability development system 2 can confirm that the user's cognitive state is at a low level. In other words, the cognitive ability development system 2 can check the user's cognitive state through tracking the user's viewpoint 43, and through this, the user's cognitive state can be recognized more accurately.

As an example, referring to FIG. 5C, the cognitive ability development system 2 may further display timeline information 503 as information for determining the user's cognitive state, and based on this, the user may provide answer information 502. You can recognize the time needed to derive it.

At this time, the cognitive ability development system 2 can control the provided content based on the above-described user cognitive state information. As a specific example, referring to FIG. 5D, the cognitive ability development system 2 tracks the user's viewpoint 43 through the front camera 3, and selects each word fragment (consonant/vowel) based on the viewpoint 43. ), you can adjust the surrounding shape (banana, cucumber, lettuce, etc.), color, and size, or provide a hint. For example, in FIG. 5D, the

contents

51, 52, 53, and 55 input into the answer information 502 may be increased in size in consideration of the low user cognitive state, and the color may be changed to a color that is easily visible. there is. As another example, when the user's gaze moves quickly, the input time for the answer information 502 is short, and the user's cognitive status level is high. At this time, because the user's cognitive status level is high, the problem difficulty can be increased by reducing the size of the provided content (51, 52, 53, 54, 55, 56, 57) and making the colors similar. In other words, the cognitive ability development system 2 can check the user's cognitive state through tracking the user's viewpoint 43 and provide content suitable for the confirmed user's cognitive state.

As a more specific example, the cognitive ability development system 2 may track the user's viewpoint 43 and count how many times the user reads the question information 501. For example, if the user checks the question information 501 more than a preset number of times, the user's cognitive status rating may be low. In this case, to reduce the difficulty of the question, increase the size of the content or make the color more noticeable as described above. It can be changed, and through this, efficient content can be provided. As another example, when tracking the user's viewpoint 43 and passing the correct word fragment more than a preset number of times, the cognitive ability development system 2 counts the preset number of times to determine when to provide a hint or zoom in on the correct word fragment. It may be, and may not be limited to a specific form.

As another example, the cognitive ability development system 2 can derive the user's viewpoint 43 as well as the user's facial expression information through the camera 3. For example, when the user squints his eyes or moves his face closer to the screen, the cognitive ability development system 2 may increase the size of the content provided to the user. In other words, it may be the case that the user does not properly recognize the content, so the cognitive ability development system (2) can enable the user to receive the content efficiently by increasing the size of the content, and perform advancement by efficiently measuring the cognitive ability. can do.

Additionally, as an example, FIG. 6A is a diagram illustrating a method of tracking a user's hand and enhancing a digital therapeutic agent according to an embodiment. As an example, the above-mentioned camera 3 can track not only the user's viewpoint 43 but also the user's hand 41. Here, the cognitive ability development system 2 can update the answer information 502 when it detects that the coordinates of the tracked user's hand 41 and the coordinates on the screen of the provided content match. Specifically, referring to Figure 6b, the cognitive ability development system 2 can track the user's hand 41 and detect an input that selects the specific content 55 at the location of the specific content 55. For example, when the cognitive ability development system 2 detects that the coordinates of each of the user's hands 41 are gathered within a preset distance, it can detect an input for selecting specific content 55 at that location. That is, the grasping motion of the user's hand 41 can be detected, and specific content 55 can be selected based on this. Afterwards, the cognitive ability development system 2 may detect that the specific content 55 selected based on the camera 3 moves from the first location to the second location. At this time, the content 61 provided at the second location may be a location that provides input for providing specific content 550 as answer information 502. That is, when the user's hand 41 selects specific content 550 and moves it to the blender 61, the cognitive ability development system 2 can recognize this and provide it as answer information 502. In other words, the cognitive ability development system 2 can recognize the user's hand 41 and track the recognized hand. At this time, the cognitive ability development system 2 may check the user's cognitive state based on the movement of the user's hand 41. For example, when the cognitive ability development system 2 detects that the user's hand 41 is quickly selected and moves from the first position to the second position within a preset time, it can determine that the user's cognitive state is at a high level. On the other hand, when the cognitive ability development system 2 detects that the movement speed of the user's hand 41 is slow and the user cannot perform the operation to select specific content 55, it can determine that the user's cognitive state is at a low level. In other words, the cognitive ability development system 2 can determine the user's cognitive state by tracking the user's hand 41.

Additionally, as an example, referring to FIG. 6C, the cognitive ability development system 2 may determine the user's cognitive state based on time information for deriving final answer information based on tracking the user's hand 41. As an example, in Figure 6c, the answer information 502 based on the question information 501 is "mother" and the user's hand 41 selects "ㅗ", "ㅊ", "ㅣ", and "ㄴ" to select the above-mentioned items. Movement to position 2 (i.e. blender, 61) can be detected, and time information required to derive the corresponding answer can be derived. For example, as described above, the cognitive ability development system 2 may determine that the user's cognitive status level is low if it takes a long time to derive the final answer information 502, and if the time to derive the final answer information 502 is short, the cognitive ability development system 2 may determine that the user's cognitive status level is low. It can be determined that the user's cognitive status level is high.

As another example, the cognitive ability development system 2 can detect both the user's viewpoint 43 and the user's hand 41. More specifically, referring to FIGS. 7A and 7B , the user can check question information 501 in the provided content and generate answer information 502 by selecting and moving specific content. At this time, the user's viewpoint 43 can move to each content based on the question information 501 and the answer information 502, and the user's hand 41 can move to each content for the answer information 502. there is. In other words, the user can recognize the content provided from the cognitive ability development system 2 with his eyes, select it with his hand, and perform a specific action. Considering the above, the cognitive ability development system 2 can detect both the user's viewpoint 43 and the user's hand 41, and can determine the user's cognitive state through this. At this time, as an example, the cognitive ability development system 2 can recognize both the user's viewpoint 43 and the user's hand 41 through one camera 3. As another example, the cognitive ability development system 2 may include a plurality of cameras 3 and track the user's viewpoint 43 and the user's hand 41 based on the plurality of cameras 3. At this time, as an example, individual weights may be assigned to information tracked based on the user's viewpoint 43 and information tracked based on the user's hand 41, and the user's cognitive state grade is determined by reflecting the values considering the weights. It can be determined, and through this, the cognitive ability development system 2 can check the user's cognitive state.

As another example, referring to FIG. 8A, the cognitive ability development system 2 provides a virtual object 71 and at least one of the user's face 42 and the user's body 44 based on the virtual object 71. By tracking either one, you can determine the user's cognitive state. At this time, as an example, the cognitive ability development system 2 can enable the user to easily recognize the virtual object 71 through control of the virtual object 71. As an example, the cognitive ability development system 2 may adjust the color surrounding the word fragment based on the background color. As a specific example, when there is a purple curtain, the cognitive ability development system 2 may display the virtual object 71 in a different color so that the user can easily recognize it. In addition, as an example, referring to FIG. 8b, the cognitive ability development system 2 recognizes real objects in the background (e.g. windows, chairs, picture frames, etc.) The position of an object can be adjusted dynamically. For example, in FIG. 8B, the virtual object 71 may not be identifiable to the user due to the real object, and taking this into consideration, the virtual object 71 may be moved to an area where the user can identify it. More specifically, the cognitive ability development system 2 can select the location of the virtual object by considering the brightness and complexity of the real object and the color and transparency of the virtual object 71. At this time, the cognitive ability development system 2 can detect the space where the virtual object 71 is displayed through the camera and derive brightness and complexity information by recognizing the real object in the space. At this time, the cognitive ability development system 2 can determine a position that the user can easily recognize by deriving the color and transparency information of the virtual object 71 and comparing it with the above-described real object information.

As a specific example, brightness may be the degree to which light enters due to ambient illumination or a window. Additionally, as an example, complexity may be a background recognized based on wallpaper or a picture frame with a complicated picture. For example, the color and transmittance of the virtual object 71 may be considered complementary or similar to the background, or if the transmittance is high, the transmittance may be lowered or moved. As another example, the cognitive ability development system 2 can also adjust the user's location. As an example, the cognitive ability development system 2 may determine the user's cognitive state by tracking the user's face 41 or the user's body 44 that imitates the virtual object 71. At this time, in cases where complexity is high, such as when there are many physical objects described above around the user, or when user recognition is difficult due to high brightness, the cognitive ability development system 2 may display information to adjust the user's location, and based on this, Thus, the user's movements can be recognized after the user's position has been adjusted.

As another example, FIGS. 9A and 9B are diagrams showing a method of determining a user's cognitive state based on tracking the user's viewpoint 43. As an example, in FIGS. 9A and 9B, a plurality of

contents

91, 92, 93, 94, 95, 96, 97, 98, and 99 are arranged, and the user's viewpoint 43 moving accordingly can be tracked. At this time, the cognitive ability development system 2 informs the user that an operation to check all the plurality of contents (91, 92, 93, 94, 95, 96, 97, 98, 99) is required, and the user selects the plurality of contents. (91, 92, 93, 94, 95, 96, 97, 98, 99) can be confirmed by looking. At this time, the cognitive ability development system 2 can track the user viewpoint 43 and obtain information on the time when the user viewpoint 43 moves from the first content 91 to the last content 99 and back again. At this time, the cognitive ability development system 2 may determine that the level of the user's cognitive state is high if the time information is short, and may determine that the level of the user's cognitive state is low if the time information is long.

Additionally, as an example, the user cognitive status level may be set to a plurality of levels based on preset conditions. As an example, a weight may be assigned to each condition that classifies each grade, and the user's cognitive state grade may be determined based on the weight information. As an example, as described above, a first weight is given to the time information at which the answer information 502 is derived based on the user viewpoint 43, and the user confirms the question information 501 based on the user viewpoint 43. A second weight may be assigned to the number of times. At this time, the cognitive ability development system 2 can derive the final value by considering each weight and check the user's cognitive status grade corresponding to the area from which the final value was derived.

As another example, when the user's hand 41 is additionally recognized, a third weight is given to the time information at which specific content is selected based on the user's hand 41 and the final answer is derived, and the user's hand 41 A fourth weight may be assigned to the user's action of selecting specific content based on tracking. At this time, the cognitive ability development system 2 reflects all of the first weight, second weight, third weight, and fourth weight to derive the final value, and confirms the user cognitive status grade corresponding to the area from which the final value was derived. You can. In other words, the user cognitive status rating can be performed through comparison with the final value derived by the cognitive ability development system 2. Additionally, as an example, the cognitive ability development system 2 may generate learning data based on the user cognitive state grade information described above, and adjust content to match the user cognitive state grade based on the generated learning data. In addition, the generated learning data can be fed back and transmitted to the cognitive ability development system (2), and the cognitive ability development system (2) can efficiently provide content appropriate for the user's cognitive status level by reflecting the above-mentioned information. .

Figure 10 is a diagram showing a digital therapeutics learning task according to one embodiment. The above-described contents can be provided to the user device 1 through the cognitive ability development system 2, and at least one of the user's hand 41, face 42, and viewpoint (or gaze 43) is tracked. By doing this, digital treatments can be provided by checking the level of learning about the content. Here, referring to FIG. 10, the above-mentioned cognitive ability development system (2) divides the content that needs to be managed to improve cognitive ability into five cognitive areas: vascular disease management, cognitive training, exercise, nutritional management, and motivation enhancement. You can. Thereafter, the cognitive ability development system 2 can create each task through content related to each task that needs to be managed by the user device 1. For example, each task is content created in consideration of contents that need to be managed, and the user can learn about each task. In addition, through the above-described tracking method for users, it is possible to derive recognition scores for each task, time spent progress, participation rate, and other information to determine and provide customized content to the user. Here, user-customized content may be provided on a one-time basis. As another example, user-customized content may be in the form of providing a plurality of tasks based on a certain cycle. For example, digital therapeutics require users to continuously learn to improve cognitive abilities such as dementia, and taking the above into consideration, user-customized content may be provided at a certain cycle. Here, referring to FIG. 10, the cognitive ability development system 2 includes cognitive tasks such as “making kimchi (101),” “remembering the card from before (102),” “growing tomatoes (103),” and “how much is it?” 104)" Assignments can be provided to users. In addition, the cognitive ability development system 2 can provide a task 105 related to the number of exercises as an exercise task, and can provide a “mind meal method 106” and a “diet management task 107” as a nutrition task. In other words, the cognitive ability development system 2 can provide a plurality of tasks to the user through the user device 1, taking the digital therapeutic effect into account. Here, the user performs the above-described tasks through the user device 1. Only then can the effectiveness of digital treatment be expected. However, users with low compliance may deviate from learning. If digital treatment is not used regularly, the effectiveness of digital treatment is significantly reduced, so there is a need to prevent this. For example, , Digital therapeutics require the patient to spend a certain amount of time and focus on performing the digital therapeutic treatment, and the effect can be achieved only when the patient performs the digital therapeutic treatment periodically for a set period of time, but it may not be easy to force this. Therefore, the cognitive ability development system (2) When providing the learning shown in Figure 10 to the user with this digital treatment, a method of reminding may be necessary to take user withdrawal into account. Specifically, the user must perform the tasks of the prescribed digital treatment according to a series of procedures. There is a need. In other words, there is a need for the user to perform the tasks according to a series of procedures considering the effectiveness of the digital treatment. For example, the user needs to complete all the assigned tasks every day, but considering the case where the user stops arbitrarily In particular, if the user studies alone at home rather than with other users or administrators at an institution or facility, learning interruptions may be frequent, and a reminder method may be needed for this. there is.

Here, the cognitive ability development system 2 can track the user through the user device 1 as described above, detect user input, and recognize the user's degree of task performance. At this time, the cognitive ability development system 2 can check whether the user has completed the final progress of the given task or finished it midway. In other words, the cognitive ability development system 2 can provide tasks according to a preset cycle by considering the digital treatment effect. As an example, the cognitive ability development system 2 may provide the user with the above-described cognitive tasks, exercise tasks, and nutritional tasks every day, but this is only an example and is not limited to the above-described embodiment. However, for convenience of explanation, the following description is based on the case where the cognitive ability development system 2 provides cognitive tasks, exercise tasks, and nutritional tasks to users every day. However, it may not be limited to this.

Figure 11 is a diagram showing the progress of a digital therapeutics learning task according to an embodiment. Referring to FIG. 11, the cognitive ability development system 2 may provide progress stage information (111, 112, 113, 114, 115, 116, 117) for each task to the user device 1. Here, as an example, the cognitive ability development system 2 may preset the learning order for each task. In other words, the user may have to perform each task in a preset order. As another example, the cognitive ability development system 2 may not restrict the learning order for each task. In other words, the user only needs to perform each task, and the order may be irrelevant. Here, the progress stage information for each task described above (111, 112, 113, 114, 115, 116, 117) may indicate the progress of each task. For example, if the user completes a specific task, it may be displayed as “Task Completed,” if the user stops the task in the middle, it may be displayed as “Continue,” and if the user has not started the task, it may be displayed as “Start.” It may not be limited to this example.

Here, as an example, the user may complete only a few detailed tasks among the seven tasks in the three categories and then stop. As another example, the user can stop progressing in the middle of completing a detailed task.

Here, the cognitive ability development system 2 can detect the user's interruption point and set different reminder methods based on this. Specifically, the cognitive ability development system 2 can detect that the user stops learning after completing a detailed task. In other words, it is possible to detect that the user completes learning about a detailed task and stops learning before starting another task. At this time, the cognitive ability development system 2 may recognize that the user is performing learning by changing the learning time or other circumstances. In other words, the cognitive ability development system 2 can determine general learning disruption and provide a general reminder message or motivational video.

On the other hand, the cognitive ability development system (2) can detect that the user stops midway through a detailed task. At this time, the cognitive ability development system 2 may further derive the user's status information. More specifically, if the user gives poor results while performing a detailed task and then stops learning, the user's learning difficulty level may not match, and difficulty adjustment can be performed taking this into consideration. Additionally, in the case described above, the cognitive ability development system 2 may also provide information about the average success rate of aborted missions or the mission departure rate of other users when providing a reminder to the user. In other words, the cognitive ability development system 2 can help the user proceed with learning without giving up by taking into account the user's point of interruption in a detailed task and providing additional information when reminded.

As another example, the cognitive ability development system 2 can detect that the user stops performing a detailed task while producing high learning results. Here, the cognitive ability development system 2 may provide a reminder to the user, provide the type and necessity of remaining missions, and also provide information that current grades are excellent. In other words, the cognitive ability development system 2 can provide additional information that allows the user to continue learning without stopping.

As another example, the cognitive ability development system 2 may provide learning information of others along with user learning information. In addition, as an example, the cognitive ability development system 2 can provide a platform to share the user's learning results with others, and through this, related information can be shared with other users to provide motivation for learning. . In other words, the cognitive ability development system 2 can set reminder information differently considering the time when the user stops learning.

At this time, as an example, the cognitive ability development system 2 may generate user status information when the user stops learning. As an example, the user status information may include information about whether the user stopped in the middle of a detailed task, or completed the detailed task and stopped before completing the entire task, as described above. Additionally, as an example, the user status information may include the user's learning ability recognition information based on the above-described user tracking information. As an example, the cognitive ability development system 2 may derive information on the user's learning ability status through user tracking, as described above, for tasks performed up to the time the user stopped the task. For example, through user tracking, if a user reads the same sentence a lot or is not learning properly, the learning ability state may be determined to be low. Additionally, the cognitive ability development system 2 may obtain user status information by considering time information from the time the user started the task to the time the user stopped the task. For example, if a user stops a task after a long time has elapsed since starting the task, the user may have stopped learning due to other reasons rather than a problem in learning. As another example, the cognitive ability development system 2 may generate user status information related to learning interruption based on the user's average learning ability or digital therapy learning level. As another example, the cognitive ability development system 2 may continuously update external state information about the user and generate user state information by reflecting the user's current cognitive ability. Here, as an example, the user status information is big data and may be information that reflects various information about the user to determine whether the user is appropriately learning during the learning process, and is not limited to a specific embodiment.

At this time, the cognitive ability development system 2 may set different reminders for stopping learning through user status information. In other words, the cognitive ability development system 2 can determine the reminder information provided when the user stops learning by reflecting the user status information.

As a specific example, FIG. 12A is a diagram showing a method of determining learning compliance based on the user's learning completion point according to an embodiment, and FIG. 12B is a diagram showing a method of providing a reminder based on the user's learning compliance according to an embodiment. This is a drawing showing the method.

At this time, referring to FIG. 12A, the user can stop learning after completing making kimchi (101) and remembering the previous card (102) as detailed tasks among the cognitive tasks (major tasks). In other words, the user can stop learning after completing learning about the detailed task. Here, the cognitive ability development system 2 may determine that the user's cessation of learning is based on the user's reasons rather than the impact of learning. As another example, the cognitive ability development system 2 may further consider other state information of the user and determine that learning interruption is based on the user's reasons rather than the impact of learning. At this time, the cognitive ability development system 2 may provide a reminder and “Continue (121)” and “End (122)” buttons, and may also provide schedule information for future learning. For example, the cognitive ability development system is “Restart after 5 minutes (123)”, “Restart after phone call (10 minutes) (124)”, “Restart after meal (1 hour later) (125)” and “Direct input (126)” buttons can be provided as reminder information. Here, the user can select specific information from the reminder information, and based on this, the cognitive ability development system 2 can set a notification, which will be described later.

Figures 13a to 13d are diagrams showing a case where a user stops learning during learning. Referring to Figure 13a, the user can stop learning while completing the detailed tasks of making kimchi (101) and remembering the previous card (102), and growing tomatoes (103) as detailed tasks among the cognitive tasks (major tasks). there is. In other words, the user can stop learning while completing the detailed task. Here, the cognitive ability development system 2 may determine that the user's interruption of learning is an effect of learning. As another example, the cognitive ability development system 2 may determine that learning interruption is based on the impact of learning by further considering other state information of the user. At this time, referring to FIG. 13B, the cognitive ability development system 2 can derive learning information 131 about growing tomatoes 103 as a detailed task. As an example, the learning information 131 may be the above-described recognition score, time spent, progress rate, participation rate, and other information, and is not limited to a specific embodiment. Additionally, the cognitive ability development system 2 may compare the learning information 131 with the learning information 132 of other users. As an example, referring to FIG. 13C, the cognitive ability development system 2 considers the user's status information and learning ability described above and compares the current learning information 131 with the learning information 132 of other users to help the user learn. You can check whether learning is interrupted due to its impact on . At this time, referring to FIG. 13D, the cognitive ability development system 2 provides a reminder and “Continue (121)” and “End (122)” buttons, along with schedule information for subsequent learning. can be provided. As an example, the cognitive ability development system includes “Restart in 5 minutes (131),” “Restart after phone call (10 minutes) (132),” “Lower difficulty value (133),” and “Get hints (134).” " button can be provided as reminder information. In other words, the cognitive ability development system 2 may determine that the reason the user stopped learning was due to the learning impact, and provide reminder information considering the learning impact.

Here, as an example, the reminder information may be set to one of the first interface information based on user reasons as shown in FIG. 12 and the second interface information based on learning influence as shown in FIG. 13 . That is, the cognitive ability development system 2 may provide a reminder as either the first interface information or the second interface information described above. Here, the cognitive ability development system 2 assigns weight to each of the above-described user status information, user-related information, and other information, derives each value, and selects either the first interface information or the second interface information. You can choose.

As a specific example, a positive weight may be provided for information unrelated to the learning progress in relation to the user's reasons. On the other hand, with regard to learning progress, a negative weight may be provided to information that may determine that learning has been stopped depending on learning influence. Here, the cognitive ability development system 2 may apply the above-described weight to the information that can be derived and provide a first interface if the final value is greater than 0, and provide a second interface if the final value is less than 0. That is, the cognitive ability development system 2 may set limiter information differently based on various information related to learning interruption, and is not limited to the above-described embodiment.

Figure 14 is a diagram illustrating a method of providing a reminder by comparing a user's learning compliance with other users according to an embodiment. Referring to FIG. 14, the cognitive ability development system 2 may further provide additional information to the user along with reminder information. As an example, referring to FIG. 14, the cognitive ability development system 2 may recognize that the user is stopping learning based on information related to learning. Here, the cognitive ability development system 2 may provide additional information along with reminder information related to learning. As an example, the additional information may be information 141 indicating the user's learning status in relation to user learning. Also, as an example, it may be information 141 that compares the user's learning status information with that of other users, and the user can continue learning through the information. In other words, the additional information 141 may be information that allows the user to proceed further without giving up learning. Here, as an example, the additional information 141 may be compensation information provided to the user, through which further motivation for learning can be provided to the user.

Additionally, as an example, the cognitive ability development system 2 may immediately set a reminder (rest and resume) in addition to continuing and ending based on the user's interruption of learning. Here, the cognitive ability development system 2 can present detailed items and send a message or automatically re-run the digital treatment program after a set period of time depending on the selected items. At this time, as an example, the cognitive ability development system 2 may provide question information according to previously selected replay details before re-executing the digital treatment program. As a specific example, the cognitive ability development system 2 may ask whether the user has been to the bathroom in response to the above-described “try again in 5 minutes” reminder information, provide an inquiry as to whether there was any discomfort while urinating, and receive user input and convert it into data. As another example, when the reminder information is “try again after eating,” the cognitive ability development system 2 may inquire about the meal information from the user and convert the user input into data.

As another example, when the reminder information is the above-mentioned “Retry after phone call,” the cognitive ability development system 2 may provide inquiries about who and what kind of call was made and how the user feels, and may receive user input and convert it into data. As an example, inquiry provision and user input may be performed based on at least one of text and voice, and are not limited to a specific form.

As another example, FIGS. 15A and 15B are diagrams illustrating a method of performing learning reset based on a reminder according to an embodiment. Referring to FIG. 15A, the cognitive ability development system 2 can detect user input according to learning interruption. At this time, the cognitive ability development system 2 can provide the above-described “Continue (121)” and “End (122)” information to the user and detect the user’s final completion of learning. Here, the cognitive ability development system 2 provides the above-described reminder information to the user, and the above-mentioned "Restart after 5 minutes (123)", "Restart after phone call (10 minutes) (124)", " The “Restart after meal (1 hour later) (125)” and “Direct input (126)” buttons can be provided as reminder information. Afterwards, the cognitive ability development system 2 can detect user input corresponding to the reminder information and determine whether to notify the digital treatment reset and re-run the program according to the corresponding information. At this time, as an example, referring to FIG. 15B, the cognitive ability development system 2 may re-execute the digital therapeutic agent based on the alarm and re-execution information. Here, the cognitive ability development system 2 may provide selected reminder-related information to the user. As an example, the cognitive ability development system 2 may provide information about “Have you finished eating (151)” based on the user’s learning interruption due to eating and detect user input. Afterwards, the cognitive ability development system (2) improves the learning effect by converting information about learning restart and user personal information into data such as “Which menu did you eat (152)” and “Do you want to start learning again (153)” You can do it.

FIG. 16 is a diagram illustrating a method of performing learning reset based on a reminder according to an embodiment. Referring to FIG. 16, the cognitive ability development system 2 may provide long-term schedule setting information based on user learning cessation. As an example, the user may stop learning for a long period of time rather than in a short period of time as described above. As an example, if the user has a reason to stop learning and needs to change the time and date, the cognitive ability development system 2 provides the user with information 161, 162 about the long-term plan and allows the user to reset learning. You can.

Here, as an example, the cognitive ability development system 2 may provide notifications to the user in consideration of the user's status. As a specific example, the cognitive ability development system 2 may provide a notification before a preset date or time according to the date and time set by the user. However, considering that the digital treatment provided by the cognitive ability development system (2) can be effective only after continuous learning, the cognitive ability development system (2) can provide notifications in consideration of the user's condition. there is. Specifically, the cognitive ability development system 2 may be linked with the user device 1. Here, the user has postponed learning for a long time, but if the user reuses or begins using the user device 1, the user may be sufficiently prepared to perform learning. Accordingly, the cognitive ability development system 2 may provide an alarm when the user starts or restarts the user device 1 to perform digital therapeutic learning. As another example, the cognitive ability development system 2 may force user learning based on the user device 1. For example, when the user starts or restarts to use the user device 1, the user is ready to learn, so the cognitive ability development system 2 performs the task as the above-described content upon starting the user device 1. It can run automatically. In other words, before starting the user device 1, the user must perform tasks provided as a digital therapeutic before using the user device 1. Through this, the cognitive ability development system 2 can enable the user to continuously perform learning, and is not limited to the above-described embodiment.

Figure 17 is a flowchart showing a reminder method to increase compliance with learning digital therapeutics according to an embodiment. Referring to FIG. 17, user learning can be performed based on the content. (S1710) After that, the user learning interruption input can be detected (S1720) and the user learning stopping point can be confirmed. (S1730) At this time, the user User learning reminder settings can be performed based on the time of learning interruption, as described above (S1740).

Here, in the above-described operation, the cognitive ability development server 2 or the cognitive ability development system 2 may include a content provision module 21, an input module 22, an analysis module 23, and a machine learning module 24. You can. At this time, user learning including content is performed based on the content provision module 21, user learning interruption input is detected based on the input module 22, user learning interruption point is confirmed, and user learning reminder is provided. You can perform settings.

At this time, as an example, user learning includes at least one major task and at least one detailed task within the major task, and user learning can be completed only when at least one major task is completed. For example, the major task may be the cognitive task, exercise task, or nutritional task described above. For example, the major task may be a task divided into the five cognitive areas of vascular disease management, cognitive training, exercise, nutritional management, and motivation enhancement, and is not limited to a specific embodiment.

In addition, as an example, when detecting the point in which user learning is interrupted, it is detected whether the point in time when the user stops learning is when at least one or more detailed tasks within the main task are completed or when at least one or more detailed tasks are being performed. can do. At this time, different reminder information may be provided based on the user learning reminder setting depending on when the user stops learning. For example, if the time when the user stops learning is when at least one detailed task within the major task is completed, only reminder information inquiring about whether to continue or end learning may be provided to the user. That is, as described above, only “Continue” and “Quit” information can be provided. On the other hand, if the user's learning interruption point is during a detailed task, reminder information related to learning restart can be provided in addition to the reminder information asking the user whether to continue or end learning. Additionally, if the user's learning interruption point is during a detailed task, it is possible to detect that learning is interrupted based on either the user's personal reasons or reasons affecting the learning progress.

At this time, if learning is stopped based on the user's personal reasons, reminder information is provided based on the first interface, and if learning is stopped based on reasons affecting the learning progress, reminder information is provided based on the second interface. You can. In addition, the reminder information provided based on the first interface may be reminder information related to the user's learning restart, and the reminder information provided based on the second interface may be reminder information related to the user's learning restart and reminder information related to learning difficulty adjustment, , which is the same as described above. In addition, when it is detected that learning is interrupted based on either the user's personal reasons or reasons affecting the learning progress, user status information can be further derived, and the first interface or the second interface can be determined based on the user status information. . Here, the user status information may be obtained through tracking information on at least one of the user's face, the user's hands, and the user's body, and may be the status information described above. Additionally, when a user learning reminder is set, a notification about the user learning progress may be provided or the user learning progress may be re-executed after a preset time based on the reminder setting information. Here, when the user restarts user learning based on the user learning reminder setting, information related to the reminder setting is provided to the user, and user learning can be restarted after detecting the user's input and converting the information related to the reminder setting into data. there is.

The embodiments described above can be implemented at least partially as a computer program and recorded on a computer-readable recording medium. Computer-readable recording media on which programs for implementing the embodiments are recorded include all types of recording devices that store data that can be read by a computer. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, and optical data storage devices. Additionally, computer-readable recording media may be distributed across computer systems connected to a network, and computer-readable codes may be stored and executed in a distributed manner. Additionally, functional programs, codes, and code segments for implementing this embodiment can be easily understood by those skilled in the art to which this embodiment belongs.

Although the above-described specification has been described with reference to the embodiments shown in the drawings, these are merely illustrative examples, and those skilled in the art will understand that various modifications and modifications of the embodiments are possible therefrom. However, such modifications should be considered within the scope of technical protection of this specification. Therefore, the true technical protection scope of the present specification should be determined to include other implementations, other embodiments, and those equivalent to the claims according to the technical spirit of the appended claims.

This specification relates to an invention that provides a method of checking digital therapeutic learning compliance through the user's learning progress, and can be used industrially.

Claims

In the reminder method to increase compliance with learning digital therapeutics,

Proceeding with user learning based on content;

Detecting the user learning interruption input;

Confirming when the user stops learning; and

A reminder method comprising performing the user learning reminder setting.
According to claim 1,

The reminder method, wherein the user learning includes at least one major task and at least one detailed task within the major task, and the user learning is completed only when all of the at least one major task is completed.
According to claim 2,

The step of detecting the user learning interruption point detects whether the user learning interruption point is a point in time when the at least one or more detailed tasks in the main task are completed or a point in the middle of performing the at least one or more detailed tasks. do,

A reminder method that provides different reminder information based on the user learning reminder setting depending on when the user stops learning.
According to claim 3,

If the time when the user's learning is stopped is when the at least one detailed task in the major task is completed, only reminder information is provided to the user to inquire whether to continue or end the learning,

When the time when the user's learning is stopped is during the detailed task, a reminder method that further provides reminder information related to restarting the learning in addition to reminder information inquiring about whether the learning continues and ends to the user.
According to claim 4,

If the user's learning interruption point is during the detailed task, it is detected that the learning is stopped based on either the user's personal reason or the learning progress impact reason, and if the learning is stopped based on the user's personal reason, A reminder method that provides the reminder information based on a first interface, and provides the reminder information based on a second interface when the learning is stopped based on reasons affecting the learning progress.
According to claim 5,

The reminder information provided based on the first interface is reminder information related to the user's learning restart,

The reminder information provided based on the second interface is reminder information related to the user's learning restart and reminder information related to adjustment of learning difficulty.
According to claim 5,

When detecting that the learning is interrupted based on either the user's personal reason or the learning progress impact reason, user status information is further derived, and based on the user status information, the first interface or the second interface How to decide, remind.
According to claim 7,

A reminder method, wherein the user status information is obtained through tracking information on at least one of the user's face, the user's hands, and the user's body.
According to claim 1,

When the user learning reminder is set, a reminder method for providing a notification about the user learning progress or re-executing the user learning progress after a preset time based on the reminder setting information.
According to clause 9,

When the user restarts the user learning based on the user learning reminder setting, reminder setting-related information is provided to the user, and after detecting the input for the user and converting the reminder setting-related information into data, the user Remind method to restart learning.
A computer program stored on a computer-readable medium coupled with hardware to execute the method according to any one of claims 1 to 10.
In the cognitive ability development server that performs reminders to increase compliance with learning digital therapeutics,

The cognitive ability development server,

content delivery module;

input module;

analysis module; and

Includes a machine learning module,

Proceed with user learning including content based on the content provision module,

Detect the user learning interruption input based on the input module,

Confirm the point at which the user stops learning, and

A cognitive ability development server that performs a reminder and performs the user learning reminder setting.