KR20240033892A - User Behavior Scoring Method Using Computing Device - Google Patents

Abstract

A method for evaluating user behavior through a computing device is disclosed. This specification provides at least one customized management item including the user's goal behavior information for BS indicators based on the user's health checkup information and questionnaire information related to the user's lifestyle. Then, the user's behavior evaluation (BS) can be calculated by assigning a predetermined weight to the user's actual behavior information corresponding to the user's target behavior information related to each BS indicator collected through the user terminal. Accordingly, it is possible to provide a user behavior evaluation method that can synthesize life logs (diet, activity, weight, practice items) and evaluate all behaviors recorded by the user with a single quantified score (number, score). The user's behavioral evaluation calculated in this way can obtain more reliable information on the health status of customers in the financial industry, including the insurance industry, and provide various financial services such as calculating insurance premiums based on this.

Description

{User Behavior Scoring Method Using Computing Device}

This specification relates to a method and system for evaluating user behavior through a computing device.

Moral hazard in health insurance can be a factor that causes medical consumers to excessively use unnecessary medical services, lowering the efficiency of medical use and increasing medical costs. In contrast, behavioral hazard, which leads to underuse of medical services necessary for health, can refer to undesirable behavior resulting from mistakes or behavioral bias due to incorrect psychological bias of medical users.

For example, although drugs that lower glucose levels are necessary for diabetic patients, many diabetic patients do not take these drugs regularly. In addition, because patients do not regularly follow the professional treatment system instructed by the doctor after receiving diagnosis and treatment other than when taking medication is necessary, the risk of the patient's disease further increases. Ultimately, from this behavioral hazard perspective, the problem of increased health insurance burden may arise due to increased inefficiency in medical use.

In the field of health insurance and medical service use, there is a need for improvement in that the above moral hazard and behavioral hazard both lead to inefficiency in medical use.

In addition, recently, applications that provide various healthcare services collect the user's general health checkup information, disease and mortality probability information, physical activity information, diet and nutritional information, and recognize the user's health status information based on the collected information. We can provide services such as ordering or providing customized health information.

However, such services evaluate the customer's health using only fragmented data such as examination information or life log data, so they cannot evaluate the customer's health from a multifaceted perspective. In addition, the benefits provided to customers are also provided on a one-time basis, so the reality is that there is a lack of motivation to induce continuous inflow of customers.

In addition, by evaluating user behavior, a behavior evaluation system is needed to enable differentiated financial and insurance services by customer type by reflecting the results of customer behavior evaluation in the financial and/or insurance service field.

Accordingly, by considering the characteristics of each user, providing appropriate feedback for each user, and providing sufficient compensation according to the reliability of the collected information, we are developing a behavior evaluation system that can increase the reliability of the user's behavior evaluation results and increase customer satisfaction. Development is needed.

The purpose of this specification is to provide a user behavior evaluation method that can synthesize the user's life log (diet, activity, weight, practice items) and evaluate all actions recorded by the user with a single quantified score (number, score). Do it as

In addition, the purpose of this specification is to provide behavioral reinforcement factors for continuous data input by providing personalized rewards (points, coupons, insurance premium discounts, etc.) to users according to score changes through the user behavior evaluation system of this specification. .

In addition, this specification secures the objectivity and reliability of the user behavior evaluation results and recalculates the user behavior evaluation score on a predetermined period of time (e.g., day/week/month) by reflecting the user's health improvement performance. The purpose is to

The technical problems to be achieved by the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clear to those skilled in the art from the detailed description of the invention below. It will be understandable.

The user's behavior evaluation method according to an embodiment of the present specification may be implemented through a computing device running an application that provides the user's behavior evaluation results through a Behavior Scoring (BS) indicator.

The user's behavior evaluation method includes providing a survey item including a plurality of lifestyle items as the application is executed, and obtaining information on a plurality of survey items through the user's input; providing at least one customized management item including the user's goal behavior information for the BS indicator based on the obtained plurality of survey item information; - the BS indicators include activity, eating, sleep and weight - acquiring the user's actual behavior information corresponding to the user's target behavior information associated with each of the BS indicators over a predetermined period of time; and calculating a behavioral evaluation (BS) of the user by assigning a predetermined weight to the acquired actual behavior information, wherein the weight is determined by the type of the BS indicator and a continuous record of the acquired actual behavior information. It is granted differently depending on the number of days.

The step of calculating the user's behavior evaluation (BS) involves assigning different weights according to the type of the BS indicator among the acquired actual behavior information and the acquisition pattern of the actual behavior information to calculate the user's behavior evaluation (BS). Calculate , but the acquisition pattern of the actual behavior information may include direct input from the user or automatic input automatically collected through a sensor provided in a portable terminal carried by the user.

The method for evaluating the user's behavior includes recognizing the user through user authentication as the application is executed; and receiving the user's health checkup information from an external server, wherein the step of providing the customized management items may be provided based on the received health checkup information and the obtained survey item information. .

The survey items may include the user's usual activity level, target weight gain or loss, eating habits, drinking and smoking status, sleeping habits, and personal tendencies.

The user's actual behavior information includes the user's activity information among the BS indicators, and the user's activity information includes heart rate, step count, and activity time information acquired through the user's portable device registered in the application. The step of calculating the user behavior evaluation includes adding up the scores corresponding to the heart rate, number of steps, and activity time information to which different weights are assigned, and generating a first behavior evaluation score corresponding to the user's activity information. can be calculated.

If there is no portable device registered with the application, the user's activity information may be recognized based on the walking speed obtained through the computing device.

The user's actual behavior information includes meal information among the BS indicators, and the meal information includes diet records, diet videos, and the number of days of achieving the target nutrient ratio, and the step of calculating the user behavior evaluation is mutually A second behavior evaluation score corresponding to the user's meal information can be calculated by adding up the scores corresponding to the diet record, diet video, and the number of days to achieve the target nutrient ratio, each of which is given a different weight.

The user's actual behavior information includes weight information among the BS indicators, and the weight information includes at least one of weight record information and image information obtained through the user's input, and calculates the user behavior evaluation. The step of calculating a third behavior evaluation score corresponding to the weight information is by adding up the scores corresponding to the input weight record information, the same time input information of the weight record, and the image information, each of which is given a different weight. can do.

The user's actual behavior information includes sleep information among the BS indicators, and the sleep information includes at least one of total sleep time, bedtime record information, and wake-up time record information, and calculates the user behavior evaluation. In the step, a fourth behavior evaluation score corresponding to the sleep information may be calculated by adding up scores corresponding to the total sleep time, the bedtime record information, and the wake-up time record information, each of which is given different weights.

In the step of calculating the user's behavior evaluation (BS), bonus points may be awarded when the obtained actual behavior information satisfies the target behavior provided through the customized management item.

The step of calculating the user's behavior evaluation (BS) is performed when, in addition to the BS indicator, there is at least one action item among the first action item set by the user or the second action item recommended by the application. Bonus points may be awarded if the acquired actual behavior information satisfies the at least one action item above.

As the period during which the actual behavior information is continuously acquired increases, additional weight is given to the continuously acquired actual behavior information, so that the user's behavior evaluation can be calculated.

The step of calculating the user's behavior (BS) is when the BS indicator obtained through the user's input among the acquired actual behavior information and an image that can guarantee the reliability of the user's input are added. A high weight may be assigned.

The step of calculating the user's behavioral evaluation includes calculating a weekly BS; updating the customized management item based on the weekly BS; Calculating monthly BS; It may further include updating the customized management item based on the monthly BS.

The user behavior evaluation method includes monitoring a change trend in the user's behavior evaluation (BS) by monitoring at least one of the weekly BS or the monthly BS; When it is determined that the BS is increasing, providing compensation to the user, wherein the compensation may include at least one of points, coupons, and financial product discounts linked to a financial company.

The user's behavior evaluation system according to another embodiment of the present specification provides a survey item including a plurality of lifestyle (Life Style) items as the application providing the user's behavior evaluation results through BS indicators is executed, an input unit that obtains a plurality of survey item information through the user's input; a behavior information collection unit that acquires the user's actual behavior information corresponding to the user's target behavior information related to each BS indicator for a predetermined period of time; Based on the obtained plurality of survey item information, at least one customized management item containing the user's goal behavior information for the BS indicator is provided, and a predetermined weight is given to the obtained actual behavior information to determine the user's A processor that calculates a behavioral evaluation (BS); wherein the BS indicator includes activity information, meal information, weight information, and sleep information,

The weight is given differently depending on the type of the BS indicator and the number of days of continuous recording of the acquired actual behavior information.

In one embodiment of the present specification, a method for evaluating user behavior through a computing device synthesizes the user's life log (diet, activity, weight, practice items) and converts all actions recorded by the user into a single quantified score (number, score). It is possible to provide a user behavior evaluation method that can be evaluated.

In addition, this specification can provide behavioral reinforcement factors for continuous data input by providing personalized rewards (points, coupons, insurance premium discounts, etc.) to users according to score changes through the user behavior evaluation system of this specification.

In addition, this specification secures the objectivity and reliability of the user behavior evaluation results and recalculates the user behavior evaluation score on a predetermined period of time (e.g., day/week/month) by reflecting the user's health improvement performance. You can.

In addition, this specification provides monetary rewards to users through a rating information system that evaluates individual activity indicators using the same indicators, and provides values for objectively quantified data to reduce the moral hazard of customers. Issues can be resolved.

Additionally, by applying the user behavior evaluation results according to an embodiment of the present specification to insurance and financial products, various business models linked to the financial industry can be created.

The effects that can be obtained from the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below. .

The accompanying drawings, which are included as part of the detailed description to aid understanding of the present specification, provide embodiments of the present specification and explain technical features of the present specification together with the detailed description.
1 is a diagram for explaining a user behavior evaluation system according to an embodiment of the present specification.
Figure 2 is an exemplary block diagram of a computing device for implementing a user behavior evaluation method according to an embodiment of the present specification.
Figure 3 is a flowchart of a user behavior evaluation method according to an embodiment of the present specification.
Figure 4 is an example of personal information required for user behavior evaluation according to an embodiment of the present specification.
Figure 5 is an overall flowchart for calculating a user evaluation score by assigning predetermined weights to various BS indicators according to an embodiment of the present specification.
Figure 6 is an example of weights differently assigned to each BS indicator according to an embodiment of the present specification (a and b in Figure 6), and shows a specific example of a method for evaluating the user's actual behavior by reflecting the weight for each set BS indicator. This is a drawing for explanation.
Figures 7a and 7b are examples of calculating scores for user behavior evaluation based on user activity information according to an embodiment of the present specification.
Figures 8A to 8C are examples of calculating scores for user behavior evaluation based on the user's meal information according to an embodiment of the present specification.
9A to 9B are examples of calculating scores for user behavior evaluation based on the user's weight information according to an embodiment of the present specification.
10A to 10B are examples of calculating scores for user behavior evaluation based on the user's sleep information according to an embodiment of the present specification.
Figure 11 is an example of calculating a score for user behavior evaluation based on the user's action items according to an embodiment of the present specification.
The accompanying drawings, which are included as part of the detailed description to aid understanding of the present specification, provide examples of the present specification and explain technical features of the present specification together with the detailed description.

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings, but identical or similar components will be assigned the same reference numbers regardless of reference numerals, and duplicate descriptions thereof will be omitted. The suffixes “module” and “part” for components used in the following description are given or used interchangeably only for the ease of preparing the specification, and do not have distinct meanings or roles in themselves. In addition, this In describing the embodiments disclosed in the specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in the specification, the detailed descriptions will be omitted. In addition, the attached drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings, and all changes included in the spirit and technical scope of this specification are not limited. , should be understood to include equivalents or substitutes.

Terms containing ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is said to be "connected" or "connected" to another component, it may be directly connected to or connected to that other component, but may also be "directly connected" to the other component. When it is mentioned that "there is" or "is directly connected," it should be understood that there are no other components in between.

Singular expressions include plural expressions unless the context clearly dictates otherwise.

In this specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

FIG. 1 is a diagram illustrating a system (hereinafter referred to as BSS) for implementing a method for evaluating user behavior through a computing device according to an embodiment of the present specification.

Referring to FIG. 1, a system for implementing a method for evaluating user behavior through a computing device according to an embodiment of the present specification includes a Behavior Scoring System (BSS) server 100, a user terminal 10, and a medical device. It may include an information provision server (20), a financial company server (30), and a healthcare server (40).

The service implemented through the BSS system can calculate the user's behavior evaluation using personal medical data provided by the medical information provision server 20 and the BS index defined in this specification.

The user terminal 10 is a terminal of a behavior evaluation subject (user), and can be linked with the financial company server 30, the behavior evaluation server 100, and the healthcare server 40 through a predetermined network.

The medical information provision server 20 may be provided with a database in which personal medical information is stored. According to one embodiment, the medical information provision server 20 may be a server operated by the National Health Insurance Corporation. In addition, according to one embodiment, the medical information provision server 20 stores health examination data performed by the National Health Insurance Corporation, and can be provided to the user terminal 10 or the financial company server 30 through user authentication at a hospital or hospital. It may include servers operated by public institutions. In addition, according to one embodiment, the medical information provision server 20 may be a server operated by the Health Insurance Review and Assessment Service, and the data provided from the medical information provision server 20 may include medication history, etc. . The public institutions may include the Korea Disease Control and Prevention Agency in addition to the National Health Insurance Corporation and the Health Insurance Review and Assessment Service, and medical information shared from the medical information provision server 20 may be provided in the form of information that follows the standard system of health and medical terminology. .

Health insurance is a national single insurance system and is exclusively managed by the National Health Insurance Corporation, and the insured people are divided into those who subscribe to workplace and local insurance, and the participation rate in receiving national checkups in 2016 is about 84.6% of all citizens, and health insurance The Insurance Corporation stores and manages the data of examinees and provides it to appropriate researchers.

The behavior evaluation server 100 may be a computing device that implements the user behavior evaluation method provided in this specification. The behavior evaluation server 100 may implement a behavior evaluation method through an application installed on the user terminal 10 or the financial company server 30/healthcare server 40. For example, when a user wants to subscribe to a specific insurance product provided by a financial company, the financial company server 30 server runs the user terminal ( You can receive behavioral evaluation results through 10). The specific application is also installed on the financial company server 30, and the behavior evaluation server 100 reports the behavioral evaluation results performed on the server to the user terminal 10 and/or the financial company server 30 and the healthcare service provider through the specific application. It may be provided to the server 40, etc.

Meanwhile, according to an embodiment of the present specification, the behavior evaluation server 100 may be linked with an SNS server (not shown) and additionally provide behavior evaluation information through SNS. For example, the user executes the user's SNS function through the terminal 10, and a chatbot service that provides behavioral evaluation information can be used as an additional function of the SNS.

Meanwhile, in the case of the system shown in FIG. 1, personal authentication may be performed over the network to provide user behavior evaluation information. For example, after user authentication is completed through the user terminal 10, the behavior evaluation server 100 provides personal medical information, etc. to the user terminal 10, the financial company server 30/healthcare server 40, etc. You can.

The behavior evaluation server 100 uses a wired or wireless communication method with the user terminal 10, the medical information provision server 20, the financial company server 30, and the healthcare server 40 to establish a predetermined network. can be connected through The predetermined network may include, for example, a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a broadband network (BBN), It may include networks such as the Internet. Additionally, the network may include at least one of network topologies including bus, link, mesh, star-bus, tree, or hierarchical network, but the present specification is not limited thereto.

FIG. 2 is an exemplary block diagram of a computing device for implementing a method for evaluating user behavior through a computing device according to an embodiment of the present specification. As described above, the computing device 100 can perform all of the functions of the behavior evaluation server shown in FIG. 1. For convenience of explanation, the behavior evaluation server of FIG. 1 is referred to as the computing device 100 of FIG. 2. Let's call it and explain it. Additionally, the description will be made assuming that the behavior evaluation method performed in the behavior evaluation server is also implemented through the processor 170 of the computing device 100.

The computing device 100 may include an input unit 110, a communication unit 120, a memory 130, a DB 140, an AI processor 150, a chatbot service unit 160, and a processor 170.

The input unit 110 may be a means for interfacing with various input/output devices. For example, input devices may include devices such as a microphone, keyboard, or mouse, and output devices may include displays and speakers. Additionally, the input unit 110 may include a device with integrated input and output, such as a touch screen. The input unit 110 may be provided independently from the computing device 110 or may be integrated into one device.

The communication unit 120 may provide a function for the computing device 100 to communicate with another device (eg, the user terminal 10 of FIG. 1, etc.) through a network. Requests, commands, data, files, etc. generated by the processor 170 of the computing device 100 according to the program code stored in a recording device such as memory 130 are transmitted to other devices connected to the network through the communication unit 120. It can be. Additionally, the communication unit 120 may receive commands, data, files, etc. from another device (eg, the user terminal 10 of FIG. 1) connected through a network. Commands, data, etc. received through the communication unit 120 may be transmitted to the processor 170 and/or memory 130.

The memory 130 is a computer-readable recording medium and may include a non-volatile permanent mass storage device such as random access memory (RAM), read only memory (ROM), and a disk drive. Here, non-volatile large-capacity recording devices such as ROM and disk drives may be included in the computing device 100 as a separate permanent storage device that is separate from the memory 130. Additionally, an operating system and at least one program code may be stored in the memory 130. These software components may be loaded into the memory 130 from a computer-readable recording medium separate from the memory 130. Recording media that can be read by a separate computer may include computer-readable recording media such as floppy drives, disks, tapes, DVD/CD-ROM drives, and memory cards. According to one embodiment, software components may be loaded into the memory 130 through the communication unit 120 rather than a computer-readable recording medium. For example, software components may be loaded into the memory 130 of the computing device 100 through a computer program installed through an installation file received over a network.

The database 140 may temporarily store personal medical information provided from the medical information provision server 20, according to an embodiment of the present specification. Additionally, the database 140 may temporarily store various user behavioral information and biometric information collected from the user's wearable device 15.

The AI processor 150 can learn a neural network using a program stored in the memory 130. In particular, the AI processor 150 includes a learning unit (not shown) that learns a neural network to calculate the user's behavior evaluation based on information collected from the user's wearable device 15 or information input through the user terminal 10. ) may include. The data learning unit can learn standards for what learning data to use to determine classification and recognition of data, and how to classify and recognize data using the learning data. According to one embodiment, the AI processor 150 uses survey data related to lifestyle input through the user terminal 10, action items set through user input, and target behavior information set through user input into a user behavior evaluation model. You can create a behavior evaluation model by defining it as learning data.

The chatbot service unit 160 can provide the user's behavior evaluation results through the chatbot.

The processor 170 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Commands may be provided to the processor 170 by the memory 130 or the communication unit 120. The processor 170 may be configured to execute received instructions according to program codes stored in a recording device such as memory 130.

Figure 3 is a flowchart of a user behavior evaluation method according to an embodiment of the present specification. Figure 4 is an example of personal information required for user behavior evaluation according to an embodiment of the present specification. The user behavior evaluation method can be implemented through the processor 170 of the computing device shown in FIG. 2. More specifically, the user behavior evaluation method can be implemented as the application for user behavior evaluation installed on the computing device shown in FIG. 2 is executed. Hereinafter, for convenience of explanation, the description will be made assuming that the user behavior evaluation method is implemented through the processor 170 of the computing device (100 in FIG. 2).

Referring to FIG. 3, the processor 170 may recognize the user through user authentication as the user behavior evaluation application is executed (S300). User recognition can be achieved through various personal information authentication methods as disclosed.

The processor 170 can connect to an external server through a wireless communication unit and receive the user's health checkup information from the external server based on pre-established personal authentication information (S310).

This specification is an indicator used to evaluate the user's behavior, and the purpose is to compile all the user's life logs (diet, activity, sleep, weight, practice items) and evaluate all actions recorded by the user into a single quantified number. do. Among the indicators used to evaluate the user's behavior, among diet, activity, weight, sleep, and practice items, a customized management area is selected based on the current user's lifestyle (e.g., diet recommendation according to the user's health status, calorie intake) It may include recommendations, target heart rate recommendations, target step recommendations, target weight recommendations, target sleep time recommendations, and specific action item recommendations). Here, in order to provide a customized management area, the application can refer to the user's health checkup information, and accordingly, the processor 170 obtains the user's health checkup data from the server of the National Health Insurance Service based on the obtained personal authentication information. can do.

As the application is executed, the processor 170 may provide survey items including a plurality of lifestyle items and obtain information on a plurality of survey items through the user's input (S320).

Referring to FIG. 4, the processor 170 acquires the user's basic information (age, gender, height, weight) to obtain survey information and determines the user's BMI (body mass index) based on the basic information. ) is calculated. The processor 170 provides various survey information to determine daily activity level, occupation, goals, meals, and lifestyle habits, and obtains survey item information through user input. The user's MET (Metabolic equivalent of task, used as a unit representing the amount of physical activity) can be derived through the questionnaire items about the amount of usual activity. In addition, through goal survey items related to body weight (muscle mass, eating habits), personalized nutrient ratios (carbohydrate, protein, fat ratio), Kcal, number of steps, etc. can be derived and provided. In addition, lifestyle patterns can be referred to through the occupational survey items, and habits that need correction can be checked through the diet or lifestyle habits survey items.

Referring again to FIG. 3, the processor 170 may provide at least one customized management item including the user's goal behavior information for the BS indicator based on the obtained plurality of survey item information (S330 ). Here, the BS indicator includes activity information, meal information, sleep information, and weight information. The BS indicator is lifelog data, and of course, other items may be added in addition to the activity, meal, sleep, and weight information described above.

The processor 170 acquires the user's actual behavior information in response to customized BS indicators provided based on health checkup information and survey information (S340).

Here, the user's actual behavior information may include first input information directly input by the user, and second input information automatically collected and automatically input through the user's portable terminal registered in the application. Here, since the first input information must be directly input by the user through a predetermined input interface provided by the application, the user's input behavior itself can be considered as an item for evaluating the behavior. Meanwhile, the second input information (e.g., heart rate, number of steps, etc.) is obtained through a sensor provided in the device through the user's direct behavior while carried by the user or attached to the user's body. It may be information obtained through . Therefore, the second input information may have higher reliability of collected information compared to the first input information.

The collected actual behavior information of the user (the above-described first input information and/or second input information) may be data that is continuously input for a predetermined period of time. This specification can assign a high score to continuously input data by steadily recording data and assigning weight to the records and actions of users who perform continuous actions.

The processor 170 may calculate the user's behavior evaluation (BS) by assigning a predetermined weight to the obtained user's actual behavior information (S350).

The processor 170 may calculate the user's behavior evaluation by assigning different weights according to the type of BS indicator and the acquisition pattern of the actual behavior information among the acquired user's actual behavior information. The acquisition pattern of the actual behavior information may mean the input pattern of the behavior information. For example, the input pattern of behavioral information may include direct input in which the user directly inputs his or her behavioral information, and automatic input that is automatically collected through a sensor provided in a portable terminal carried by the user. Therefore, the weight given to behavioral information directly input by the user and the weight to behavioral information automatically recognized through a portable terminal may be different from each other. However, in the case of behavioral information directly input by the user, when uploading a photo within the application, it may be designed to be given more weight than simple input information (text input, etc.).

Here, the weight may be assigned differently depending on the type of BS indicator and the number of days of continuous recording of the acquired actual behavior information. The number of consecutive record days may mean 2 consecutive days, 3 consecutive days, 4 consecutive days, 5 consecutive days, etc. Meanwhile, in the case of a combination of discontinuous records, in the case of 2 consecutive days + 2 consecutive days, there is a 4-day behavioral information input record, but a score equal to twice the weight corresponding to the 2 consecutive days is given. However, it is desirable that the weight corresponding to the input record of 4 consecutive days is designed to have a larger value than the weight of 2 consecutive days + 2 consecutive days.

Hereinafter, a method of evaluating user behavior through BS indicators to which different weights are assigned according to an embodiment of the present specification will be described in more detail with reference to FIG. 5.

Figure 5 is an overall flowchart for calculating a user evaluation score by assigning predetermined weights to various BS indicators according to an embodiment of the present specification. Hereinafter, the flowchart shown in FIG. 5 may be implemented by operations on the processor 170.

Referring to FIG. 5, the processor 170 links the user's general health checkup information through personal authentication (510). The processor 170 collects survey items related to lifestyle. The above survey items are intended to collect user responses to survey items such as daily activity level, purpose of weight gain or loss, eating/lifestyle habits, drinking/smoking, sleeping habits, and personal tendencies.

The processor 170 may provide a customized management area based on the collected lifestyle (S530). Additionally, the user can record the user's actual behavioral information corresponding to the customized management area provided through the application in the application. Actual behavioral information recorded in the application can be classified into the user's actively recorded information and automatically collected items.

For example, in the case of diet management among the customized management areas provided by the application, the nutrient ratio (carbohydrate, protein, fat ratio) and daily calorie intake tailored to the user can be calculated and provided (531). In the case of activity information among the customized management areas provided by the application, the user's target heart rate and/or walking speed and number of steps can be provided (532). Among the customized management areas provided by the application, weight information can be provided by setting a target weight (533). Among the customized management areas provided by the application, sleep information can be provided by setting sleep habits (534). In the case of action items among the customized management areas provided by the application, action items may be set directly by the user or recommended by the application (535).

The processor 170 can receive actual behavior information corresponding to each BS indicator (meal, activity, weight, sleep, action item) (540). The actual behavior information can be applied to behavior evaluation by collecting information recorded every day for 7 days.

The processor 170 can classify and receive information actually entered by the user among the meal information recommended through the application in the form of meal records, photos, and simple questionnaires. At this time, the meal information entered may be actively entered directly by the user (541).

The processor 170 can assign different weights to diet records, photos, and simple survey items. For example, the highest weight may be given to the diet record. This is because the diet record, which is information that can confirm the user's eating pattern and kcal intake, is input, so the content provided to the user can be diverse. For example, when a user sets a goal, the nutrient ratio and calories appropriate for the goal may be presented as target values. And in the case of photo recording, kcal information cannot be obtained directly, but GPS information can be collected together, and meal patterns including meal plans (home meals, eating out, etc.) and time information can be confirmed based on location. Additionally, if a photo is uploaded along with the meal record, it can be used as authentication information for the meal record. In addition, in the case of a simple questionnaire, it is impossible to directly derive kcal related to meals, but it is possible to collect information on whether people eat regularly and on irregular eating habits such as late-night snacking or overeating.

Likewise, the processor 170 may use passive information automatically collected through the user's portable terminal linked to the application in the case of activity information recommended through the application, and items such as heart rate, walking speed, number of steps, and exercise may be collected. You can.

The processor 170 may collect weight records, photos, etc. through the user's active recording of target weight information presented through the application (541). Sleep habits are also collected through the user's active records, and practice items may include the user's active records or passive records automatically collected through the user's mobile terminal linked to the application (543).

The processor 170 may collect the user's actual behavioral information for items recommended or set through the application, and may assign a score to each collected behavioral information. For example, in the case of meal information among BS indicators, points may be given for diet records (S11), points for photo records (S12), and points for simple questionnaires (S13). In other words, among the BS indicators, meal information can include detailed items, and diet records, photo records, and simple questionnaires correspond to detailed items, and points can be given for each detailed item. In addition, in the case of activity information among BS indicators, a score for heart rate (S21), a score for walking speed (S22), a score for the number of steps (S23), and a score for exercise (S24) may be given, respectively. The same applies to weight information, sleep information, and action items among BS indicators.

Meanwhile, in Figure 5, scores corresponding to each BS indicator (meal, activity, weight, sleep, practice items) can be calculated. More specifically, in the case of meal information among the BS indicators, the processor 170 obtains a score (S1 = S11, S12, S13) that is the sum of the scores for the detailed items of the meal information. The processor 170 similarly calculates the scores (S2, S3, S4) by adding up the scores for the detailed items of each BS indicator for activity information (S2), weight information (S3), sleep information (S4), and action items (S5). ,S5) is obtained.

Meanwhile, in order to perform the final user behavior evaluation, the processor 170 may reflect predefined weights for each BS indicator. For example, 30% is given to meal information (S1), 40% to activity information (S2), 20% to weight information (S3), 5% to sleep information (S4), and 5% to action items (S5). Therefore, the end user evaluation (Behavior Scoring) can be calculated by reflecting the weights in the scores of each BS indicator described above and adding up the scores of each BS indicator.

Above, in FIG. 5, the concept of calculating a user behavior evaluation by assigning a predetermined weight to the user's actual behavior information collected (or input) through an application according to an embodiment of the present specification has been explained in detail.

Below, a specific example of assigning a score to the user's actual behavior information collected (or input) according to an embodiment of the present specification and calculating a score for each BS indicator will be described.

Figure 6 is an example of weights differently assigned to each BS indicator according to an embodiment of the present specification (a and b in Figure 6), and shows a specific example of a method for evaluating the user's actual behavior by reflecting the weight for each set BS indicator. This is a drawing for explanation.

Figure 6(a) is an example of the weekly score distributed to each BS indicator. For example, based on the user's behavioral evaluation of 100 points, points are distributed to activity (50 points), diet (20 points), weight (20 points), sleep (5 points), and practice items (5 points). Additionally, Figure 6(b) is an example of monthly scores distributed to each BS indicator. In the case of monthly scores, there is no change in weekly score distribution for activity, diet, sleep, and practice items, but in the case of weight, the weekly weight score of 20 points is subdivided into weekly weight record (10 points) and monthly weight change record (10 points). It is composed of:

Meanwhile, referring to (c) of FIG. 6, the user's actual behavior information corresponding to an arbitrary BS indicator (each detailed item A1, A2, and A3 included in the arbitrary BS indicator) will be collected (or recorded). This is an example of data in which a predetermined score is given to each detailed item (A1, A2, A3).

Item 61 refers to the detailed items (A1, A2, A3) of any BS indicator described above. For example, if the BS indicator is activity information, the detailed items (A1, A2, A3) may be heart rate, number of steps, and activity time, respectively (see the items illustrated in FIGS. 7 to 11 below).

The evaluation unit 62 can be divided into weeks, days, or months. The recording unit 63 is divided into days. In other words, the actual behavior information collected (or recorded) is entered on a daily basis, and for A1 items, user behavior evaluations are calculated on a weekly basis, and for A3 items, user behavior evaluations are calculated on a monthly basis.

The grade for each item (D) (64) means the numerical value (N11, N12, N13) linked to the number of consecutive input days item (65). If the A1 item is entered on 1 day, the grade for each item is the N11 value, Item A2 was entered for 2 consecutive days and the grade for each item was N12, and item A3 was entered for 3 consecutive days and the grade for each item was N13 (N11 < N12 < N13). Here, the grade for each item corresponding to the number of consecutive input days may be defined differently for each BS indicator, and may be stored and managed in the application in the form of a table for each type of BS indicator.

The item % (W) (65) corresponds to the weight for each BS indicator described in FIG. 5, and is assigned to each subitem A1, A2, and A3 constituting an arbitrary BS indicator, and is the weight assigned to each subitem. The sum of is 100.

As shown in (c) of FIG. 6, the processor 170 multiplies the detailed items (A1, A2, and A3) of the BS indicator by the grade for each item (64) and the weight (65) for each detailed item, respectively, to obtain a score for each item. Calculate detailed scores (N21, N22, N23). Then, the detailed scores for each item go through multiple scale steps according to a predetermined ratio. Then, rescaling is applied considering the final score (see (a) and (b) of FIG. 6) distributed to the BS indicators including the above detailed items (A1, A2, A3), and the end user Calculate the Behavioral Assessment (BS).

Below, an example of calculating user behavior evaluation for each BS indicator will be described.

Figures 7A and 7B are examples of calculating scores for user behavior evaluation based on user activity information according to an embodiment of the present specification.

The detailed items that make up the BS indicator (activity information) include heart rate (701), number of steps (704), and activity time (705). Meanwhile, weights are assigned to each detailed item (721): 80% for heart rate (701, 702, 703), 15% for number of steps (704), and 5% for activity time (705). Additionally, referring to (a) of FIG. 6, activity information among BS indicators is allocated 50 points during user behavior evaluation. Therefore, the highest score of user behavior evaluation corresponding to activity information is 50 points (761).

Referring to FIGS. 7A and 7B, heart rate information may be further subdivided and classified into 60% or less (701), 60-70% (702), and 70% or more (703). According to an embodiment of the present specification, the exercise zone varies depending on the heart rate zone, and a customized heart rate exercise zone may be recommended based on the user's age (Jensen et al., 2013) (DiPietro et al., 2019) (American College of Sports, 2000). Based on prior research, the user behavior evaluation method according to an embodiment of the present specification may provide a heart rate that is 70% of the maximum heart rate, which is the heart rate region in which fat burning is best induced, as the target heart rate.

In addition, in the case of heart rate, based on 30 minutes a day, it is divided into a first heart rate zone (711) when exercising for more than 30 minutes and a second heart rate zone (712) when exercising for less than 30 minutes, and the heart rate is 60%. Even in the case below (701), the ratings (D, 720) for each item of the first heart rate zone (711) and the second heart rate zone (712) are set differently.

As described above, the grade for each item (D, 720) is a value linked to the number of consecutive collection (or recording) days (731), and the grade for each item is designed to increase as the number of consecutive days increases.

For step count information, the application can suggest a minimum of 7000 steps according to the American College of Sport (ASCM) guidelines, and 7000 steps is the number of steps that has the same health-promoting effect as exercising according to the World Health Organization (WHO) guidelines. . Step count information may also be linked to the number of daily steps required to achieve weight loss goals.

If, according to the embodiment of the present specification, to obtain the highest score (50 points) in activity information among BS indicators,

1) If you enter a record of exercising for more than 30 minutes a day for 7 consecutive days in the heart rate range of 60-70% (702), you must obtain the highest score (30 points, 753) in the heart rate category.

2) The number of steps must be over 7000 steps and the highest score (10 points, 751) must be obtained through 7 days of continuous recording.

3) In terms of activity time, you must obtain the highest score (10 points, 752) by recording more than 210 minutes of exercise over 7 days.

For reference, referring to FIG. 7B, when a record of exercising for more than 30 minutes a day for 7 consecutive days is entered in the heart rate zone 703 of 70% or higher, the score is 25 points (754), which is less than the highest score (30) in the heart rate field. . This is because in the user behavior evaluation design process of this specification, the heart rate zone where fat burning is best induced was set as the target heart rate at 70% of the maximum heart rate, and the score corresponds to the user's actual heart rate measured according to the target heart rate setting. Of course, may vary.

Figures 8A to 8C are examples of calculating scores for user behavior evaluation based on the user's meal information according to an embodiment of the present specification.

The detailed items that make up the BS indicator (meal information) include meal records (801), photos (802), and nutrient ratios (carbohydrate, protein, fat ratio) (803). Meanwhile, weights are assigned to each detailed item: 30% for meal records (801), 40% for photos (802), and 30% for nutrient ratios (803). Here, the reason why photos are given a higher weight than meal records may be because the reliability of the input data is high. Additionally, referring to (a) of FIG. 6, meal information among BS indicators is allocated 20 points among user behavior evaluations. Therefore, the highest score of user behavior evaluation corresponding to meal information is 20 points (761).

Meanwhile, according to an embodiment of the present specification, the processor 170 may further segment and collect the depth of the collected (or recorded) information for at least one detailed item (801, 802, 803) according to the type of BS indicator. there is. For example, the processor 170 may collect the meal record 820 among the meal records 801 by dividing them into breakfast meal records, lunch meal records, and dinner meal records (830). Additionally, for example, the processor 170 may divide the nutrient ratio 803 item into a goal achievement area and a goal non-achievement area and assign a matching score to each area.

As mentioned above, in order to obtain the highest score (20) through meal information among the user's actual behavior records,

1) In the meal record (801), enter the breakfast meal record for 7 consecutive days to obtain the highest score (851), enter the lunch meal record for 7 consecutive days to obtain the highest score (852), and enter the dinner meal record for 7 consecutive days (852). Get the highest score by entering daily (853)

2) For photos (802), enter morning photo records for 7 consecutive days to obtain the highest score (854), enter lunch photo records for 7 consecutive days to obtain the highest score (855), and enter evening photo records for 7 consecutive days to obtain the highest score (854). Enter for 7 days and get the highest score (856)

3) In the nutrient ratio (803), the ratio of carbohydrates, protein, and fat in the consumed meal must be entered for 7 consecutive days to achieve the target ratio and obtain the highest score (857).

Figures 9A and 9B are examples of calculating scores for user behavior evaluation based on the user's weight information according to an embodiment of the present specification.

Figure 9A is an example of the score given to the user's actual behavior in response to weight information among the BS indicators, and the detailed items constituting the BS indicator (weight information) are weight record (901), same time record (902), and record, respectively. Includes photos (903). The method of awarding points for each detailed item is the same as illustratively described in FIGS. 7 and 8. However, in the case of weight information among BS indicators, the processor 170 calculates weekly scores and monthly scores differently. Figure 9A is an example of obtaining weekly scores for weight information.

Meanwhile, in the case of Figure 9B, in the process of calculating the monthly score among the items constituting the BS index (weight information), the weight change item 904 is additionally reflected in the score. For example, the processor 170 subdivides the weight change item into achievement of target weight (911), non-achievement of target weight (912), and maintenance of existing weight within a certain range (913), and each item corresponding to each subdivision item. Depending on the grade, user behavior evaluation of weight change can be calculated. In relation to weight information, the weight change item 904 is additionally reflected in the process of calculating the final monthly score by adding up the weekly scores, so that whether the target weight has been achieved can be practically reflected in the user behavior evaluation.

Figures 10A and 10B are examples of calculating scores for user behavior evaluation based on the user's sleep information according to an embodiment of the present specification.

Detailed items constituting the BS indicator (sleep information) may include total sleep time (1001), bedtime (1002), wake-up time (1003), and sleep quality record (1004). Here, bedtime (1002) and wake-up time (1003) refer to records of actual sleeping and waking up times, and total sleep time (1001) is determined by recording bedtime (1002) and wake-up time (1003). It may refer to the information being calculated. Meanwhile, in the case of sleep quality records, the weight for the item is set to 0%, so it may be excluded from the actual user's behavior evaluation calculation process.

Figure 11 is an example of calculating a score for user behavior evaluation based on the user's action items according to an embodiment of the present specification.

Detailed items that make up BS indicators (practice information) can be divided into performance (recorded performance) and record (practice process). In the case of action items, the processor 170 assigns a high score to the reliability of the information collected (or recorded), and in the case of record items 1101, information is automatically collected or input through the user's portable terminal linked to the application. Regarding (record), the rating for each item can be set higher than the recorded items through the user's subjective opinion. Likewise, in the case of the authentication item 1102, if the authentication information is an uploaded photo, the rating for each item can be set high.

The user behavior evaluation result according to an embodiment of the present specification may be the final sum of the scores for each BS indicator calculated in FIGS. 7 to 11.

The BS index disclosed in this specification, or the weight given to each of the at least one detailed item constituting each BS index, may be designed in various modifications, and in particular, the target BS index according to the current health status of the individual user. In order to achieve this, the weight may be designed to automatically change adaptively according to the user's personal status.

The above-described present invention can be implemented as computer-readable code on a program-recorded medium. Computer-readable media includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. It also includes those implemented in the form of carrier waves (e.g., transmission via the Internet). Accordingly, the above detailed description should not be construed as restrictive in all respects and should be considered illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

Claims (16)

In the behavior scoring (Behavior Scoring) method of a user through a computing device running an application that provides the user's behavior evaluation results through BS indicators,
Providing survey items including a plurality of lifestyle items as the application is executed, and obtaining information on a plurality of survey items through the user's input;
providing at least one customized management item including the user's goal behavior information for the BS indicator based on the obtained plurality of survey item information; -The BS indicators include activity, eating, sleep and weight-
Obtaining the user's actual behavior information corresponding to the user's target behavior information related to each BS indicator during a predetermined period of time; and
Calculating the user's behavioral evaluation (BS) by assigning a predetermined weight to the obtained actual behavioral information,
The user's behavior evaluation method, characterized in that the weight is given differently depending on the type of the BS indicator and the number of days of continuous recording of the acquired actual behavior information.
According to claim 1,
The step of calculating the user's behavioral evaluation (BS) is,
Calculate the user's behavior evaluation (BS) by assigning different weights according to the type of BS indicator among the acquired actual behavior information and the acquisition pattern of the actual behavior information,
The acquisition pattern of the actual behavioral information is,
A user behavior evaluation method comprising direct input from the user or automatic input automatically collected through a sensor provided in a portable terminal carried by the user.
According to claim 1,
Recognizing a user through user authentication as the application is executed; and
Further comprising: receiving health checkup information of the user from an external server,
The step of providing the customized management items is based on the received health examination information and the obtained survey item information.
According to claim 1,
The above survey items are:
A user's behavior evaluation method comprising the user's usual activity level, target weight gain and loss, eating habits, drinking and smoking status, sleeping habits, and personal tendencies.
According to claim 1,
The user's actual behavior information includes the user's activity information among the BS indicators,
The user's activity information includes heart rate, step count, and activity time information obtained through the user's portable device registered in the application,
The step of calculating the user behavior evaluation is,
A user's behavior evaluation method characterized by calculating a first behavior evaluation score corresponding to the user's activity information by adding up scores corresponding to the heart rate, step count, and activity time information to which different weights are assigned.
According to claim 5,
A method for evaluating user behavior, characterized in that when there is no portable device registered with the application, the user's activity information is recognized based on the walking speed obtained through the computing device.
According to claim 1,
The user's actual behavior information includes meal information among the BS indicators,
The meal information includes diet records, diet videos, and the number of days to achieve the target nutrient ratio,
The step of calculating the user behavior evaluation is,
The user's behavior, characterized in that calculating a second behavior evaluation score corresponding to the user's meal information by adding up the scores corresponding to the diet record, diet video, and the number of days of achieving the target nutrient ratio, each of which is given different weights. Assessment Methods.
According to claim 1,
The user's actual behavior information includes weight information among the BS indicators,
The weight information includes at least one of weight record information and image information obtained through the user's input,
The step of calculating the user behavior evaluation is,
Characterized by calculating a third behavior evaluation score corresponding to the weight information by adding up the scores corresponding to the input weight record information, the same time input information of the weight record, and the image information, each of which is given a different weight. A method of evaluating user behavior.
According to claim 1,
The user's actual behavior information includes sleep information among the BS indicators,
The sleep information includes at least one of total sleep time, bedtime record information, and wake-up time record information,
The step of calculating the user behavior evaluation is,
The user's fourth behavior evaluation score corresponding to the sleep information is calculated by adding up the scores corresponding to the total sleep time, the bedtime record information, and the wake-up time record information, each of which is given different weights. Behavioral assessment methods.
According to claim 1,
The step of calculating the user's behavioral evaluation (BS) is,
A user behavior evaluation method characterized in that bonus points are awarded when the acquired actual behavior information satisfies the target behavior provided through the customized management item.
According to claim 1,
The step of calculating the user's behavioral evaluation (BS) is,
If, in addition to the BS indicator, there is at least one action item among the first action item set by the user or the second action item recommended by the application, the obtained actual action information satisfies the at least one action item. A user behavior evaluation method characterized by awarding bonus points when
According to claim 1,
As the period during which the actual behavior information is continuously acquired increases, additional weight is given to the continuously acquired actual behavior information to calculate the user's behavior evaluation.
According to claim 1,
The step of calculating the user's behavior (BS) is,
User behavior evaluation, characterized in that when the BS indicator obtained through the user's input among the obtained actual behavior information and an image that can guarantee the reliability of the user's input are added, a high weight is assigned. method.
According to claim 1,
The step of calculating the user's behavioral evaluation is,
Calculating weekly BS;
updating the customized management item based on the weekly BS;
Calculating monthly BS;
updating the customized management item based on the monthly BS;
A user behavior evaluation method further comprising:
According to claim 14,
Monitoring a change trend in the user's behavioral evaluation (BS) by monitoring at least one of the weekly BS or the monthly BS;
When it is determined that the BS is rising, providing compensation to the user;
The reward is a method for evaluating user behavior, characterized in that it includes at least one of points, coupons, and financial product discounts linked to a financial company.
A user's behavior scoring (Behavior Scoring) system through a computing device running an application that provides the user's behavior evaluation results through Behavior Scoring (BS) indicators,
An input unit that provides survey items including a plurality of lifestyle items as the application is executed and obtains information on a plurality of survey items through the user's input;
a behavior information collection unit that acquires the user's actual behavior information corresponding to the user's target behavior information related to each BS indicator for a predetermined period of time;
Based on the obtained plurality of survey item information, at least one customized management item containing the user's goal behavior information for the BS indicator is provided, and a predetermined weight is given to the obtained actual behavior information to determine the user's A processor that calculates a behavioral rating (BS);
The BS indicator includes activity information, meal information, weight information, and sleep information,
The user's behavior evaluation system, wherein the weight is given differently depending on the type of the BS indicator and the number of days of continuous recording of the acquired actual behavior information.

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