Electronic device and user terminal for guiding goal setting and goal achievement based on body indicator, and operation method therefor

Abstract

Disclosed are an electronic device and a user terminal for guiding goal setting and goal achievement based on a body indicator, and an operation method therefor. The operation method for an electronic device may comprise the operations of: selecting, on the basis of a user input, a target cluster targeted by a target user from among a plurality of clusters based on a body indicator; determining, on the basis of time series data of body indicators acquired in advance from a plurality of users, a target route for guiding the target user from a starting cluster including the body indicator of the target user to the target cluster; determining, on the basis of the time series data, time and difficulty expected to be required for the body indicator of the target user to move from the starting cluster to a closest cluster on the target route; and providing the target user with the target route, the time, and the difficulty.

Description

Electronic device, user terminal and operating method thereof for setting goals and guiding goal achievement based on body indicators

[0001] An electronic device, a user terminal, and an operating method thereof for setting goals and guiding goal achievement based on body indicators are disclosed.

[0002] Devices that measure body composition by utilizing the different electrical resistances of fat and muscle are known. These devices measure bioimpedance by contacting electrodes to the target area, allowing them to determine body composition. Body composition can be used as an important indicator of a subject's health and fitness level. By analyzing various body components that cannot be determined solely through weight and height, body composition can be used as an indicator of an individual's health, nutritional status, and exercise effectiveness.

[0003] Among methods for measuring body composition, bioelectrical impedance analysis (BIA) uses electrodes to pass a small electric current through the body. BIA measures impedance based on the electrical characteristics of each body part obtained by passing this small current through the body, and estimates body composition based on this impedance.

[0004] The background technology described above is possessed or acquired during the process of deriving the present disclosure, and cannot necessarily be said to be a publicly known technology disclosed to the general public prior to the filing of the present disclosure.

[0005] The present disclosure provides an electronic device and a user terminal that utilize accumulated data to set and provide a final path with the highest probability of achieving a user's goal, and provide the difficulty and time required to achieve the user's goal.

[0006] The present disclosure provides an electronic device and a user terminal that can provide specific guidance on the degree of increase or decrease in body weight and muscle mass and the time required to achieve a goal based on the user's physical indicators by utilizing accumulated data.

[0007] According to one embodiment, a method of operating an electronic device may include an operation of selecting a target cluster targeted by a target user from among a plurality of clusters based on a body indicator based on a user input, an operation of determining a target route guiding the target user from a starting cluster including a body indicator of the target user to the target cluster based on time series data of body indicators acquired in advance from a plurality of users, an operation of determining an expected time and difficulty level for the body indicator of the target user to move from the starting cluster to a cluster closest to the target route based on the time series data, and an operation of providing the target route, the time, and the difficulty level to the target user.

[0008] The operation of determining the target route may include an operation of determining a plurality of candidate routes based on the starting cluster and the target cluster from among a plurality of routes determined in advance based on an index indicating location information of the body indicator, and an operation of determining a target route from among the plurality of candidate routes and determining the target route based on the target route.

[0009] The operation of determining the plurality of candidate paths may determine the plurality of candidate paths starting from an index indicating the physical indicator of the target user and ending at any one index included in the target cluster.

[0010] The operation of determining the expected time and difficulty may include an operation of extracting data from the time series data that has changed when compared to the previous data in an index indicating statistical location information for the body indicator, an operation of determining movement ratio information indicating a movement ratio from each index to each of the adjacent indexes based on the extracted data, and an operation of determining the difficulty based on the movement ratio information.

[0011] The operation of determining the difficulty based on the movement ratio information may include an operation of generating a histogram with the number of data included in the movement ratio information and the movement ratio information as axes, an operation of determining a cumulative distribution function based on the histogram, and an operation of determining the difficulty based on the cumulative distribution function.

[0012] The operation of determining the expected time and difficulty may include an operation of extracting data from the time series data that has changed when compared to the previous data in an index indicating statistical location information for the body indicator, an operation of determining time information indicating the time consumed per unit distance consumed in moving from each index to each of the adjacent indexes based on the extracted data, and an operation of determining the expected time based on the time information.

[0013] The operation of determining the above time information may determine the time information representing the time consumed per unit distance by dividing the time consumed in moving from each index to each of the adjacent indexes in a coordinate space using the body index as a coordinate by the movement distance of the index.

[0014] The above body indices may include body mass index (BMI) and percentage body fat (PBF).

[0015] The operation of selecting the target cluster may select one of two or more upper clusters with the highest score based on the body index as the target cluster based on the user input.

[0016] The operation of selecting the target cluster may select, based on the user input, one of the clusters having a score above average based on body indicators as the target cluster.

[0017] The operation of selecting the target cluster may select one of the clusters determined based on the period set by the target user as the target cluster based on the user input.

[0018] According to one embodiment, a method of operating a user terminal includes: obtaining a user input from a target user to select a target cluster targeted by the target user among a plurality of clusters based on a body index; displaying, in response to obtaining the user input, a target route providing a guide to reach the target cluster from a starting cluster including the body index of the target user; and displaying an expected time and difficulty level for the body index of the target user to move from the starting cluster to the closest cluster on the target route; and an electronic device communicating with the user terminal may determine, in response to receiving the user input from the user terminal, the target route, the time, and the difficulty level based on time series data of body indexes obtained in advance from a plurality of users, and provide the target route, the time, and the difficulty level to the user terminal.

[0019] According to one embodiment, an electronic device includes at least one processor including processing circuitry, and a memory storing instructions, wherein when the instructions are individually or collectively executed by the at least one processor, the electronic device may cause the electronic device to select, based on a user input, a target cluster targeted by a target user from among a plurality of clusters based on body indices, determine a target route guiding the target user from a starting cluster including a body indices of the target user to the target cluster based on time series data of body indices acquired in advance from a plurality of users, determine a time and a difficulty expected to be taken for the body indices of the target user to move from the starting cluster to a cluster closest to the target route based on the time series data, and provide the target route, the time, and the difficulty to the target user.

[0020] When the instructions are individually or collectively executed by the at least one processor, the electronic device may determine a plurality of candidate paths based on the starting cluster and the target cluster from among a plurality of paths predetermined based on an index indicating location information of the body index, and determine a target path from among the plurality of candidate paths to determine the target route based on the target path.

[0021] When the above instructions are individually or collectively executed by the at least one processor, the electronic device may determine a plurality of candidate paths starting from an index representing a physical indicator of the target user and ending at any one index included in the target cluster.

[0022] When the instructions are individually or collectively executed by the at least one processor, the electronic device may be configured to extract data from the time series data in which an index representing statistical position information for the body index has changed compared to the previous data, determine movement ratio information representing a movement ratio from each index to each of the adjacent indexes based on the extracted data, and determine the difficulty based on the movement ratio information.

[0023] When the above instructions are individually or collectively executed by the at least one processor, the electronic device may be configured to generate a histogram with the ratio and the number of data included in the movement ratio information as axes based on the movement ratio information, determine a cumulative distribution function based on the histogram, and determine the difficulty based on the cumulative distribution function.

[0024] When the instructions are individually or collectively executed by the at least one processor, the electronic device may be configured to extract data from the time series data indicating changes in an index representing statistical position information about the body index compared to the previous data, determine time information indicating a time consumed per unit distance consumed in moving from each index to each of the adjacent indexes based on the extracted data, and determine the expected time based on the time information.

[0025] When the above instructions are individually or collectively executed by the at least one processor, the electronic device may be configured to determine the time information representing the time consumed per unit distance by dividing the time consumed in moving from each index to each of the adjacent indexes in a coordinate space using the body index as a coordinate by the movement distance of the index.

[0026] The above body parameters may include BMI and PBF.

[0027] According to one embodiment of the present disclosure, an electronic device and a user terminal can set and provide a route with the highest probability of achieving a user's goal based on accumulated data, and provide the user with the difficulty and time required to achieve the user's goal.

[0028] According to one embodiment of the present disclosure, an electronic device and a user terminal can provide specific guidance to a user regarding the degree of weight increase or decrease, degree of muscle mass increase or decrease, and time required to achieve a goal from the user's current state based on accumulated data.

[0029] FIG. 1 is a drawing for explaining a body index measuring device, a user terminal, and an electronic device according to one embodiment.

[0030] FIG. 2 is a diagram illustrating a cluster according to one embodiment.

[0031] FIG. 3 is a drawing for explaining the operation of an electronic device according to one embodiment.

[0032] Figure 4 is a flowchart for explaining a screen displayed by a user terminal according to one embodiment.

[0033] FIG. 5 and FIG. 6 are diagrams for explaining selection of a target cluster according to one embodiment.

[0034] FIG. 7 is a diagram for explaining determination of a target route according to one embodiment.

[0035] FIG. 8 is a flowchart illustrating a method of operation of an electronic device for determining difficulty and expected time according to one embodiment.

[0036] Figure 9 is a diagram for explaining time series data according to one embodiment.

[0037] Fig. 10 is a drawing for explaining the direction of movement according to one embodiment.

[0038] FIG. 11 is a diagram for explaining interpolated time series data according to one embodiment.

[0039] Fig. 12 is a diagram for explaining movement ratio information according to one embodiment.

[0040] FIG. 13 is a diagram for explaining a histogram and cumulative distribution function based on movement ratio information according to one embodiment.

[0041] FIG. 14 and FIG. 15 are diagrams for explaining the determination of expected time according to one embodiment.

[0042] Figure 16 is a flowchart for explaining the operation of a user terminal according to one embodiment.

[0043] FIG. 17 is a drawing for explaining an electronic device according to one embodiment.

[0044] Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, the scope of the patent application is not limited or restricted by these embodiments. The same reference numerals in each drawing represent the same components.

[0045] The embodiments described below may be modified in various ways. The embodiments described below are not intended to be limiting in their specific form, and should be understood to encompass all modifications, equivalents, and alternatives thereof.

[0046] While terms like "first" and "second" may be used to describe various components, these terms should be understood only to distinguish one component from another. For example, a "first" component may be referred to as a "second" component, and similarly, a "second" component may also be referred to as a "first" component.

[0047] The terms used in the examples are used only to describe specific embodiments and are not intended to limit the embodiments. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among the phrases, or all possible combinations thereof. In this specification, it should be understood that the terms "comprise" or "have" and the like specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0048] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments pertain. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0049] In addition, when describing with reference to the attached drawings, identical components will be assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted. When describing embodiments, if a detailed description of a related known technology is judged to unnecessarily obscure the gist of the embodiment, the detailed description will be omitted.

[0050] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0051]

[0052] FIG. 1 is a drawing for explaining a body index measuring device, a user terminal, and an electronic device according to one embodiment.

[0053] Referring to FIG. 1, an electronic device (100), a user terminal (110), and a body index measurement device (120) are illustrated. The electronic device (100) may be a device of a service provider providing the services described in this disclosure. For example, the electronic device (100) may be a server, but is not limited thereto.

[0054] The electronic device (100), the user terminal (110), and the body index measuring device (120) can communicate via wired / wireless communication (130). The wired / wireless communication (130) can include two-way communication and / or one-way communication. The wired / wireless communication (130) can include short-range communication (e.g., wireless fidelity (Wi-Fi), Bluetooth, ultra-wideband (UWB), near field communication (NFC), infrared data association (IrDA), or radio frequency identification (RFID)) and / or medium- to long-range communication (e.g., cellular communication or satellite communication).

[0055] A body indicator measuring device (120) can measure a user's body indicator. The body indicator measuring device (120) can transmit the user's body indicator to a user terminal (110) and / or an electronic device (100) via wired / wireless communication (130). The body indicator measuring device (120) can receive information such as the user's height, weight, and age in advance to measure the body indicator. The body indicator may include body composition and indicators related to the body other than body composition. For example, the body composition may include, but is not limited to, percent body fat (PBF), lean body mass (LBM), skeletal muscle mass (SMM), muscle mass, visceral fat, total body water (TBW), protein, minerals, fat mass, extracellular water ratio, abdominal fat percentage, and muscle mass by region. For example, body-related indicators may include, but are not limited to, body mass index (BMI), score, and basal metabolic rate (BMR).

[0056] The user terminal (110) can provide the user with the received body index of the user. The user terminal (110) can provide the user with a trend of changes in the user's body index. The user terminal (110) can include various computing devices such as a mobile phone, smart phone, tablet, e-book device, laptop, personal computer, desktop, or workstation, and various wearable devices such as a smart watch, smart glasses, or a head-mounted display (HMD).

[0057] The electronic device (100) may include a processor (101) and memory (103). However, this is merely an example and should not be construed as limiting other embodiments. For example, it will be apparent to those skilled in the art that the electronic device (100) may further include other general-purpose components.

[0058] The processor (101) may perform the overall function for controlling the electronic device (100). The processor (101) may control the electronic device overall by executing programs and / or instructions stored in the memory (103). The processor (101) may be implemented as a central processing unit (CPU), a graphics processing unit (GPU), an application processor (AP), etc., provided in the electronic device (100), but is not limited thereto. The number of processors (101) may be one or more. For example, the processor (101) may have a multi-core processor structure such as a dual core, a quad core, or a hexa core. The processor (101) may control the operations of the electronic device (100) by executing instructions stored in the memory (103). For example, the processor (101) may correspond to a plurality of processors that collectively perform a plurality of operations by dividing them among the processors.

[0059] The memory (103) may be hardware that stores data processed and data to be processed within the electronic device (100). In addition, the memory (103) may store applications, drivers, etc. to be driven by the electronic device (100). In addition, the memory (103) may include instructions. The memory (103) may include one or more memories. The instructions stored in the memory (103) may be stored in one memory. The instructions stored in the memory (103) may be divided and stored in multiple memories. The memory (103) may include volatile memory such as dynamic random access memory (DRAM) and / or nonvolatile memory.

[0060] The electronic device (100) can store the received body indicators of the user for service provision. The electronic device (100) can store the body indicators of the user and / or other users. The electronic device (100) can accumulate the body indicators received from the user and / or other users. The electronic device (100) can manage the accumulated body indicators as time-series data for each user. The electronic device (100) can provide a service to the user and / or other users based on the accumulated data. For example, the electronic device (100) can provide a service for setting goals and achieving goals based on body indicators to the user and / or other users. To provide a service for setting goals and achieving goals, the electronic device (100) can perform clustering on the accumulated data to create multiple clusters. The electronic device (100) can provide a service based on the multiple clusters. The clusters will be described below.

[0061]

[0062] FIG. 2 is a diagram illustrating a cluster according to one embodiment.

[0063] Referring to FIG. 2, a cluster map (200) is illustrated. The electronic device can accumulate body parameters measured from users. Based on two or more of the accumulated body parameters, the electronic device can distinguish types of users (e.g., clusters).

[0064] For convenience of explanation, this disclosure focuses on user type classification based on BMI and PBF. However, it will be apparent to those skilled in the art that the description of this disclosure can also be applied to user type classification based on body parameters other than BMI and PBF (e.g., SMM, LBM).

[0065] In this disclosure, only some of the clusters (e.g., cluster (210) and cluster (220)) are shown for convenience of explanation. Therefore, it should be understood that more clusters exist on the cluster map (200). In this disclosure, for convenience of explanation, the case of nine percentile divisions is described. However, it will be apparent to those skilled in the art that the following description can be equally applied to cases where the number of percentile divisions is less than or greater than nine. Below, a method for generating multiple clusters is described.

[0066] An electronic device can display users in two dimensions with BMI (e.g., y-axis) and PBF (e.g., x-axis) as axes. The electronic device can display a plurality of users in two dimensions with BMI and PBF as axes using BMI and PBF among accumulated body indices. The electronic device can divide the plurality of users into a plurality of percentile sections for each of BMI and PBF. The percentile section can indicate a proportion of the plurality of users included in the section. The plurality of percentile sections can include, but are not limited to, less than 1%, 1% or more but less than 5%, 5% or more but less than 15%, 15% or more but less than 35%, 35% or more but less than 65%, 65% or more but less than 85%, 85% or more but less than 95%, 95% or more but less than 99%, and 99% or more. A higher percentage in a percentile section can indicate a proportion of people with a higher BMI or PBF. For example, the BMI of a person whose BMI is in the lower 1% of the percentiles may be lower than the BMI of a person whose BMI is in the 99th% or higher of the percentiles. For example, the PBF of a person whose PBF is in the lower 1% of the percentiles may be lower than the PBF of a person whose PBF is in the 99th% or higher of the percentiles.

[0067] As the electronic device divides the two-dimensional space with BMI and PBF as axes into 9 percentile partitions, 81 (e.g., 9 x 9) cells can be determined. Each cell can have an index. The index of each cell can represent statistical position information of the body index. The index can be expressed as (BMI_Index, PBF_Index). For example, people included in the cell with the index (1,1) can represent people whose BMI and PBF are both less than the top 1% of all users. For example, people included in the cell with the index (3,6) can represent people whose BMI is 5% or more but less than 15% of all users, and whose PBF is 65% or more but less than 85% of all users.

[0068] Since the index represents statistical location information, it may be determined differently by gender. For example, men and women included in the same index may have different PBF and / or BMI. For example, a man included in index (4,3) may have a BMI of 15, and a woman may have a BMI of 25. Since the index represents statistical location information, it may be determined differently by age. For example, a man in his 20s included in index (4,3) may have a BMI of 15, and a man in his 50s may have a BMI of 22. Since the index is determined differently by gender and / or age, body parameters may differ depending on gender and / or age even if they correspond to the same index.

[0069] An electronic device can perform clustering based on the physical characteristics of individuals included in each index. Individuals included in adjacent indices may share clinical characteristics. The electronic device can cluster one or more adjacent indices to determine multiple clusters and generate a cluster map (200). The electronic device can group indices with common or similar clinical characteristics to determine multiple clusters and generate a cluster map (200). A cluster may include one or more indices. For example, cluster (210) may include (9,1), (8,1), (9,2), and (8,2). For example, cluster (220) may include (9,3), (9,4), (9,5), (9,6), and (9,7). Each cluster may have a nickname. For example, cluster (210) may have a nickname related to the characteristics of the corresponding cluster, such as "Long Live the World's Strongest Man."

[0070] An electronic device can represent multiple clusters in three dimensions. For example, the electronic device can represent multiple clusters as a three-dimensional cluster map (250). The electronic device can represent multiple clusters in three dimensions with PBF, BMI, and score as axes. The electronic device can determine a score of a cluster based on a body index. For example, the electronic device can determine a score by inputting a representative value among the body indexes included in the cluster into a predetermined formula. A cluster with a higher score can include body indexes that users consider ideal. A cluster (210) can be represented as a cluster (260) in the three-dimensional cluster map (250).

[0071] Below, we describe a method for guiding goal setting and goal achievement based on the multiple clusters described above.

[0072]

[0073] FIG. 3 is a drawing for explaining the operation of an electronic device according to one embodiment.

[0074] The operations described below may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel. Furthermore, some operations may be omitted in some embodiments. Operations (310) to (340) may be performed by at least one component (e.g., a processor) of the electronic device.

[0075] According to one embodiment, instructions stored in memory by at least one processor may be individually or collectively executed and may cause the electronic device to perform the following operations:

[0076] In operation (310), the electronic device may select a target cluster targeted by the target user from among a plurality of clusters based on body indicators based on user input.

[0077] The target user may be a user requesting guidance on goal setting and goal achievement based on body measurements. The electronic device may receive user input indicating a selection of one of the top two or more clusters with the highest scores. The electronic device may select a target cluster from among the top two or more clusters with the highest scores based on the user input. The selection of the target cluster is further described in FIGS. 4 to 6.

[0078] In operation (320), the electronic device can determine a target route that guides the target user from a starting cluster containing the target user's body indicators to the target cluster based on time series data of body indicators acquired in advance from a plurality of users.

[0079] The method for determining the target route is described later in Fig. 7.

[0080] In operation (330), the electronic device can determine, based on time series data, the expected time and difficulty level for the target user's body indicators to move from the starting cluster to the closest cluster on the target route.

[0081] An electronic device can extract data from time-series data that indicates changes in an index representing statistical location information about a body's physical characteristics compared to the previous data. The electronic device can then determine the expected time and difficulty based on the extracted data. Methods for determining the expected time and difficulty are described below in FIGS. 8 through 15.

[0082] In action (340), the electronic device may provide the target route, time, and difficulty to the target user.

[0083] The electronic device can transmit the target route to the user terminal. The user terminal can display the received target route. The electronic device can transmit to the user terminal the estimated time and difficulty required to move from the starting cluster to the closest cluster along the target route. The user terminal can display the estimated time and difficulty required to move from the starting cluster to the closest cluster along the target route.

[0084] The electronic device can determine the expected time and difficulty it will take for the target user to move to the next cluster along the target route until the target user reaches the target cluster along the target route. If a change in cluster is identified, the electronic device can determine the expected time and difficulty it will take to move from the changed cluster to the closest cluster along the target route. If a change in cluster is identified until the target user reaches the target cluster, the electronic device can determine and provide the expected time and difficulty it will take to move from the changed cluster to the closest cluster along the target route.

[0085] Electronic devices can provide a competitor option that allows the target user to compare their progress toward achieving their goals with those of their competitors. The device can set competitors among multiple users within the same cluster who have chosen the same goal as the target user. For example, the device can automatically select the top three users with the greatest changes in biometric indicators after a specified period (e.g., five days) of goal setting. For example, the device can set users directly designated by the target user as competitors.

[0086] An electronic device can acquire time-series data on changes in physical parameters and / or scores of competitors, and display them alongside the target user's change trends. For example, the electronic device can align the target user's and competitor's goal-setting dates to the same reference date and display the target user's and competitor's change trends together.

[0087] The electronic device can update the competitor configuration accordingly when a competitor leaves or when the target user provides input to change or delete a competitor.

[0088] The electronic device may provide additional information to determine how many other users the target user has designated as a competitor.

[0089] Below, the screen displayed by the user terminal is described.

[0090]

[0091] Figure 4 is a flowchart for explaining a screen displayed by a user terminal according to one embodiment.

[0092] Referring to FIG. 4, a screen of a user terminal providing selection of a target cluster is illustrated. The user terminal may be a terminal of the target user. The user terminal may run an app that provides the target user's body indices obtained from a body indices measurement device. The user terminal may provide goal setting and goal achievement based on the body indices through the app. Upon receiving a user input requesting goal setting and goal achievement based on the body indices, the user terminal may display a screen (400).

[0093] The user terminal can obtain a user input for selecting a target cluster (415) targeted by the target user from among multiple clusters based on body indicators from the target user.

[0094] Referring to screen (400), the user terminal may provide the target user with a selection of two or more top clusters having the highest scores based on body indices. For example, the user terminal may provide the target user with a selection of any one of the three top clusters having the highest scores, i.e., cluster (401), cluster (403), and cluster (405). The user terminal may obtain a user input for selecting any one of the clusters as the target cluster (415). The user terminal may transmit the user input to an electronic device. The electronic device may select any one of the two or more top clusters having the highest scores based on body indices as the target cluster (415) based on the user input. When the target cluster (415) is selected, the electronic device may determine a target route (413) and transmit it to the user terminal. The user terminal may receive the target route (413) from the electronic device in response to transmitting the user input to the electronic device.

[0095] On the screen (410), the user terminal may display a target route (413). The user terminal may display the target route (413) that provides a guide to reach the target cluster (415) from the starting cluster (411) that includes the target user's body indicators. The target route (413) may include the starting cluster (411) and the target cluster (415). The target route (413) may include transit clusters. For example, the target route (413) may include eight transit clusters. The transit clusters may be clusters that are passed through while traveling from the starting cluster (411) to the target cluster (415) along the target route (413).

[0096] Referring to screen (410), the user terminal may roughly display the expected time it will take to reach the target cluster (415) from the starting cluster (411). For example, the user terminal may display "The total time required is estimated to be 24 to 36 months."

[0097] Referring to screen (420), an animation (421) may be provided in which the target user's body index moves from the starting cluster (411) to the cluster closest to the starting cluster (e.g., the next cluster (430)) on the target route. The user terminal may display detailed information for moving the target user's body index from the starting cluster (411) to the cluster closest to the starting cluster (411) on the target route (413) (e.g., the next cluster (430) or a transit cluster).

[0098] Referring to screen (420), the user terminal can display information (423) on changes in at least a portion of body parameters required to move from the starting cluster (411) to the next cluster (430). Referring to screen (420), the user terminal can display the expected time (427) and difficulty (425) that it will take for the body parameters of the target user to move from the starting cluster (411) to the closest cluster (e.g., the next cluster) on the target route (413). The user terminal can receive and display the expected time (427) and difficulty (425) that it will take for the body parameters of the target user to move from the starting cluster (411) to the closest cluster on the target route (413) from the electronic device.

[0099] Below, the method of selecting a target cluster (415) is further described.

[0100]

[0101] FIG. 5 and FIG. 6 are diagrams for explaining selection of a target cluster according to one embodiment.

[0102] Referring to FIG. 5, a user terminal may display a screen (500) that provides a target user with a selection of one of the clusters with a score above average based on a body index. The user terminal may receive a user input for selecting one of the clusters with a score above average as the target cluster. The user terminal may transmit the user input to an electronic device. The electronic device may select one of the clusters with a score above average based on a body index as the target cluster based on the user input.

[0103] Referring to FIG. 6, the user terminal may display a screen (610) that provides the target user with a selection for one of the clusters determined based on a period set by the target user.

[0104] Referring to screen (610), the user terminal may provide the target user with a selection of one of the clusters expected to be reached within three months based on a three-month period set by the target user. The user terminal may receive user input regarding one of the clusters. The user terminal may transmit the user input to an electronic device. The electronic device may select one of the clusters determined based on the period set by the target user (e.g., three months) as the target cluster based on the user input.

[0105] Referring to screen (620), the user terminal may provide the target user with a selection of one of the clusters expected to be reached within 12 months based on a 12-month period set by the target user. As the period increases, clusters further away from the starting cluster may be provided. The user terminal may receive user input regarding one of the clusters. The user terminal may transmit the user input to an electronic device. The electronic device may select one of the clusters determined based on the period (e.g., 12 months) set by the target user as the target cluster based on the user input.

[0106] Below, we explain how to determine the target route.

[0107]

[0108] FIG. 7 is a diagram for explaining determination of a target route according to one embodiment.

[0109] Once the target cluster (415) is determined, the electronic device can determine a target route (413) from the starting cluster (411) to the target cluster (415). The starting cluster (411) is a cluster that includes an index (711) of the latest body indicators obtained from the target user, and the target cluster (415) may be a cluster selected by the target user as described above in FIGS. 5 to 7 . The target cluster (415) may include one or more indices (715). For convenience of explanation, the starting cluster (411) may also be referred to as a cluster in which the target user is currently included.

[0110] The electronic device can determine a target path based on the index and convert the target path into a target route (413) based on the cluster. The electronic device can determine a target path starting from the target user's index (711) to reach at least one index (715) included in the target cluster (415).

[0111] An electronic device may include a plurality of predetermined paths. The electronic device may predetermine the plurality of paths based on time-series data of multiple users. The electronic device may determine a trend in the indexes of multiple users based on the time-series data of the multiple users. The electronic device may predetermine the plurality of paths based on the trend in which the indexes change.

[0112] The multiple paths may have different starting indices and / or target indices. For example, one of the multiple paths may be a route starting from a first index and reaching a second index, and one of the multiple paths may be a route starting from a third index and reaching a fourth index.

[0113] The electronic device can determine a plurality of candidate routes (751, 753, 755, 757) starting from the target user's index (711) among the plurality of routes and ending at any one index (715) included in the target cluster (415).

[0114] The electronic device can determine a plurality of candidate paths (751, 753, 755, 757) starting from the target user's index (711) and arriving at one of the indexes (715) included in the target cluster (415) within a specific movement range from among the plurality of paths. The specific movement range can be determined as shown in the following mathematical expression 1.

[0115]

[0116] PBF A may be PBF_i included in the target user's index (711). BMI A may be BMI_i included in the target user's index (711). PBF Bmay be PBF_i included in any one index (715) included in the target cluster (415). BMI B may be BMI_i included in one of the indexes (715) included in the target cluster (415). C may be a setting value set by the user. C may be an integer greater than or equal to 0.

[0117] For example, if C is 0, the target user's index (711) is (2,7), and one index (715) included in the target cluster (415) is (9,3), so the specific movement range can be determined as 11. The electronic device can extract a plurality of candidate paths (751, 753, 755, 757) from the plurality of paths to reach the index (715) from the index (711) with 11 movements.

[0118] For example, if C is 2, the target user's index (711) is (2,7), and one index (715) included in the target cluster (415) is (9,3), so a specific movement range can be determined as [11,13]. The electronic device can extract a plurality of candidate paths from the plurality of paths to reach the index (715) from the index (711) with 11 to 13 movements.

[0119] The electronic device can determine one of the plurality of candidate paths (751, 753, 755, 757) as the target path. The electronic device can determine the score increase / decrease trend according to the index change for the plurality of candidate paths (751, 753, 755, 757). The electronic device can determine the target path based on the score increase / decrease trend. A detailed description of the index score is omitted because the description of the cluster score is equally applicable.

[0120] For example, based on the score trend, an electronic device may determine the path with the smallest average score increase or decrease as the target path. For example, based on the score trend, an electronic device may determine the path with the largest average score increase or decrease as the target path. For example, based on the score trend, an electronic device may determine the path with a decreasing score initially but increasing score later as the target path.

[0121] The electronic device can convert the target path into a target route. The target path may be a path of an index in two dimensions, or a root of a cluster in three dimensions. If the target path is a movement path of an index unit, the target route may be a root of a cluster unit. For example, if the candidate path (753) is determined to be the target path, the electronic device can convert the candidate path (753) into a target route (413).

[0122] Below, a method is described for determining the expected time and difficulty it will take for a target user's body parameters to move from a starting cluster (411) to the closest cluster (e.g., cluster (710)) on the target route (413) as the target route is determined.

[0123]

[0124] FIG. 8 is a flowchart illustrating a method of operation of an electronic device for determining difficulty and expected time according to one embodiment.

[0125] The operations described below may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel. Furthermore, some operations may be omitted in some embodiments. Operations (810) to (850) may be performed by at least one component (e.g., a processor) of the electronic device.

[0126] According to one embodiment, instructions stored in memory by at least one processor may be individually or collectively executed and may cause the electronic device to perform the following operations:

[0127] In operation (810), the electronic device can extract data from the time series data that has changed when compared to the previous data, the data representing the statistical location information for the body index.

[0128] The extraction of data is further described in Figures 9 and 11.

[0129] In operation (820), the electronic device can determine movement direction information indicating the rate of movement from each index to each of the adjacent indexes based on the extracted data.

[0130] In operation (830), the electronic device can determine the difficulty level based on the movement direction information.

[0131] The electronic device can generate a histogram based on the movement ratio information, with the ratio and the number of data included in the movement ratio information as axes. The electronic device can determine a cumulative distribution function based on the histogram. The electronic device can determine the difficulty level based on the cumulative distribution function. The method for determining the difficulty level is further described in FIGS. 12 and 13.

[0132] In operation (840), the electronic device can determine time information representing the time consumed per unit distance consumed in moving from each index to each of the adjacent indexes based on the extracted data.

[0133] The electronic device can determine time information representing the time consumed per unit distance by dividing the time consumed in moving from each index to each adjacent index in a coordinate space using body indices as coordinates by the distance traveled by the index.

[0134] In operation (850), the electronic device can determine an expected time based on time information.

[0135] The method of determining time information and determining the expected time is further described in FIGS. 14 and 15.

[0136]

[0137] Figure 9 is a diagram for explaining time series data according to one embodiment.

[0138] Referring to Figure 9, a portion of time-series data of body parameters previously acquired from multiple users is illustrated. The time-series data may be a list of data measured chronologically for each user. For convenience of explanation, the data illustrated in Figure 9 is referred to as time-series data (900), although it is a portion of the time-series data of body parameters previously acquired from multiple users.

[0139] The time series data (900) may include a plurality of fields. The time series data (900) may include, but is not limited to, a plurality of fields, including UID (user identification) (e.g., user identification number), Datetimes (e.g., measurement date), AGE (e.g., age), SEX (e.g., sex), HT (height) (e.g., height), WT (e.g., weight), SMM (e.g., skeletal muscle mass), PBF (e.g., body fat percentage), BMI (e.g., body mass index), BMI_index, PBF_index, Datasource, and ClusterIndex, Clusterchange. Some of the plurality of fields may be obtained in advance before measuring the body index. For example, AGE, SEX, and HT may be obtained in advance before measuring the body index. Some of the plurality of fields may be obtained through measuring the body index. For example, WT, SMM, PBF, BMI, BMI_index, PBF_index, Datasource and ClusterIndex, Clusterchange can be obtained through measurements of body indices.

[0140] BMI_index can indicate the location of users who measured body indices relative to the BMI axis on the cluster map based on measured body indices. PBF_index can indicate the location of users who measured body indices relative to the PBF axis on the cluster map based on measured body indices. Datasource can indicate whether the body indices were obtained through actual measurement (e.g., real) or obtained through interpolation (e.g., fake). ClusterIndex is an index that indicates statistical location information of users who measured body indices, and can be determined based on BMI_index and PBF_index. ClusterIndex can be expressed as (BMI_index, PBF_index). Clusterchange can indicate whether an index (e.g., ClusterIndex) has changed compared to the previous data (e.g., change history). If the index has changed, TRUE can be displayed, and if the index has not changed, FALSE can be displayed. For example, the index of data with Datetimes "2021-06-26" is (7,7) and the index of data with Datetimes "2021-07-03" has changed to (7,6), so Clusterchange with Datetimes "2021-07-03" may display True.

[0141] An electronic device can extract data from time-series data that indicates changes in an index compared to the previous data. Based on the extracted data, the electronic device can determine movement ratio information, which indicates the rate of movement from each index to each adjacent index.

[0142] When using only data acquired through actual measurements (e.g., data with a "real" data source), if the date interval (e.g., the interval between Datetimes) is large, it may be difficult to determine when the index changed. For example, if a user measures their body parameters once a month and the index changes compared to the previous data, it may be difficult to determine the exact point in time when the index changed. If the point in time of the index change is unknown, there may be errors in the movement rate information. Electronic devices can fill in the gaps in the data through interpolation. Time-series data generated through interpolation is described later in Figure 11.

[0143] Based on the extracted data, the electronic device can determine the index movement direction of each user over a specific period (e.g., one year). The index movement direction is described below. This index movement direction can be used as reference.

[0144]

[0145] Fig. 10 is a drawing for explaining the direction of movement according to one embodiment.

[0146] Referring to FIG. 10, a graph (1000) is shown that visualizes the movement direction of the index for a specific period of time for users whose BMI_i (e.g., BMI_index) is 2 or 3 or whose PBF_i (e.g., PBF_index) is 3 or 4. Since the two-dimensional space with BMI and PBF as axes is divided into 9 percentile sections, it should be understood that a total of 81 graphs may exist, but only 4 are shown for convenience of explanation. In addition, although only gender is distinguished in FIG. 10, it is obvious to those skilled in the art that additional graphs visualizing the movement direction of the index distinguished by age group can be created.

[0147] Graph (1010) represents the direction of movement of the indices for users with BMI_i of 2 and PBF_i of 3 (e.g., index (2,3)) during a specific period. For example, some of the females with BMI_i of 2 and PBF_i of 3 may have moved toward arrow (1011). For example, some of the females included in the index (2,3) according to arrow (1011) may have moved to (5,4). For example, some of the males with BMI_i of 2 and PBF_i of 3 may have moved toward arrow (1013). For example, some of the males included in the index (2,3) according to arrow (1013) may have moved to the index (4,5). The boldness (e.g., thickness) of the arrow may be related to the magnitude of the movement rate. A bolder arrow may indicate that more users have moved in that direction.

[0148] Graph (1020) represents the direction of movement of the index for users with BMI_i of 2 and PBF_i of 4 during a specific period. For example, some of the females with BMI_i of 2 and PBF_i of 4 may have moved toward arrow (1021). For example, some of the females included in the index (2,4) according to arrow (1021) may have moved to (4,4). For example, some of the males with BMI_i of 2 and PBF_i of 4 may have moved toward arrow (1023). For example, some of the males included in the index (2,4) according to arrow (1023) may have moved to the index (3,3). The boldness of the arrow may be related to the magnitude of the movement ratio. A bolder arrow may indicate that more users have moved in that direction.

[0149]

[0150] FIG. 11 is a diagram for explaining interpolated time series data according to one embodiment.

[0151] Referring to FIG. 11, a portion of time series data interpolated from time series data of body indices previously acquired from multiple users is illustrated. For convenience of explanation, the data illustrated in FIG. 11 is referred to as interpolated time series data (1100), although it is a portion of the interpolated time series data. The interpolated time series data (1100) includes the same multiple fields as the time series data (900) of FIG. 9, and thus a detailed description thereof will be omitted.

[0152] In the interpolated time series data (1100), data obtained through actual measurements may be displayed as real in the Datasource, and interpolated data may be displayed as fake.

[0153] An electronic device can perform interpolation when the difference between the measurement date of a specific data in time series data and the measurement date of the previous data (e.g., Datetime) exceeds a threshold time (e.g., 1 day). The threshold time can vary depending on the configuration. For example, the threshold time can be determined as 1 day, 3 days, or 1 week. For example, the electronic device can determine that data with Datetime "2021-07-03" is 1 week apart from the previous data with Datetime "2021-06-26." For example, since 1 week exceeds the threshold time (e.g., 1 day), the electronic device can perform interpolation.

[0154] The electronic device can perform data interpolation using various interpolation methods, such as linear interpolation, polynomial interpolation, Lagrange interpolation, and Newton interpolation. For example, the electronic device can perform data interpolation using linear interpolation. The electronic device can estimate the median of the measurement dates of data having adjacent indices as the point where the indices change. The adjacent indices can represent indices where the difference between BMI_i and / or PBF_i is 1.

[0155] The electronic device can perform data interpolation on a daily basis. The electronic device can estimate the point at which the index changes through interpolation. Although not shown in the interpolated time series data (1100), the interpolated data can also be determined to have Clusterchange set to TRUE.

[0156] The electronic device can extract data from the interpolated time series data (1100) that has changed when compared to the previous data. Based on the extracted data, the electronic device can determine movement ratio information indicating the rate of movement from each index to each of the adjacent indexes.

[0157] The movement ratio information is explained below.

[0158]

[0159] Fig. 12 is a diagram for explaining movement ratio information according to one embodiment.

[0160] In Fig. 12, for convenience of explanation, only the movement ratio information for which BMI_i (e.g., BMI_index) is 4 or 3 or PBF_i (e.g., PBF_index) is 3 or 4 is shown. It should be understood that since the two-dimensional space with BMI and PBF as axes is divided into 9 percentile sections, a total of 81 pieces of movement ratio information can exist, but only 4 are shown for convenience of explanation.

[0161] An electronic device can determine movement ratio information, which indicates the rate of movement from each index to each adjacent index, based on data extracted from time-series data. An index can move to an index adjacent to the index. For example, an index can move to at least one of up, down, left, right, upper-left, upper-right, lower-left, and lower-right. For example, in the movement ratio information (1210), users with a BMI_i of 4 and a PBF_i of 3 can indicate that the index moved up, down, left, right, and upper-left.

[0162] The boldness of an arrow may be related to the magnitude of the movement rate. A bolder arrow may indicate that more people moved in that direction. For example, in the movement rate information (1210), the arrow (1220) is the boldest, which may indicate that users with a BMI_i of 4 and a PBF_i of 3 moved the most to an index with a BMI_i of 4 and a PBF_i of 2. For example, it may indicate that the largest proportion, 30%, of users with a BMI_i of 4 and a PBF_i of 3 moved along the arrow (1220).

[0163] The electronic device can generate a histogram based on the movement rate information. The electronic device can then generate a cumulative distribution function based on the histogram. This is described later in FIG. 13.

[0164]

[0165] FIG. 13 is a diagram for explaining a histogram and cumulative distribution function based on movement ratio information according to one embodiment.

[0166] The electronic device can generate a histogram (1300) based on the movement ratio information and the number of data included in the movement ratio information as axes.

[0167] In the histogram (1300), a path can represent an arrow. The total number of paths can represent the total number of arrows included in the movement ratio information. The total number of paths can be up to 648 (e.g., 81 x 8), since each of the 81 indices can have up to 8 movement directions.

[0168] In the histogram (1300), the movement ratio per pass for the entire index may refer to the ratio indicated by the arrow in the movement ratio information. In the histogram (1300), the number of data corresponding to the movement ratio may represent the number of arrows in the movement ratio information.

[0169] For example, the arrow (1220) of the movement ratio information (1210) of FIG. 12 indicates 30%, so it can be included in the movement ratio per pass of 0.3 for the entire index.

[0170] The electronic device can generate a cumulative distribution function (1310) based on the histogram (1300). The cumulative distribution function (1310) can represent the cumulative proportion of passes based on the movement ratio per pass for the entire index. For example, referring to the cumulative distribution function (1310), the proportion of passes with a movement ratio per pass of 0% to 0.05% can represent 19% of the total.

[0171] The electronic device may divide the cumulative distribution function (1310) into a plurality of compartments. For example, the electronic device may divide the cumulative distribution function (1310) into a first compartment (1340), a second compartment (1350), and a third compartment (1360), but is not limited thereto. The first compartment (1340) may be a compartment corresponding to a difficulty level of "high," the second compartment (1350) may be a compartment corresponding to a difficulty level of "medium," and the third compartment (1360) may be a compartment corresponding to a difficulty level of "low."

[0172] For example, referring to the cumulative distribution function (1310), suppose that the cumulative proportion of a pass with a movement ratio of 0.02 is 0.05 (e.g., top 5%). Since the cumulative proportion of a pass with a movement ratio of 0.02 is 0.05, which means that it rarely occurs, the difficulty of a pass with a movement ratio of 0.02 may correspond to "high". For example, referring to the cumulative distribution function (1310), suppose that the cumulative proportion of a pass with a movement ratio of 0.25 is 0.9 (e.g., top 90% or bottom 10%). The cumulative proportion of a pass with a movement ratio of 0.25 may correspond to "low" difficulty because the cumulative proportion is 0.9.

[0173] An electronic device can generate one or more boundary lines (1320, 1330) to divide a cumulative distribution function (1310) into a plurality of compartments. The electronic device can generate one or more boundary lines (1320, 1330) based on at least one of an inflection point of the cumulative distribution function (1310) and a point where the rate of change exceeds a critical ratio. The electronic device can generate a plurality of compartments representing different levels of difficulty based on one or more boundary lines (1320, 1330).

[0174] The electronic device can determine the expected difficulty level for the target user's body parameters to move from the starting cluster to the nearest cluster on the target route based on the cumulative distribution function (1310).

[0175] Below we explain how to determine the expected time.

[0176]

[0177] FIG. 14 and FIG. 15 are diagrams for explaining the determination of expected time according to one embodiment.

[0178] Based on the extracted data, time information indicating the time consumed per unit distance spent moving from each index to each adjacent index can be determined. The electronic device can determine time information indicating the time consumed per unit distance by dividing the time consumed moving from each index to each adjacent index in a coordinate space using body coordinates by the distance traveled by the index. A method for determining the time consumed per unit distance is described below.

[0179] Even if users have the same index, their actual positions within the index may be different. For example, even if users C and D are both included in an index with a BMI_index of 7 and a PBF_index of 3, the actual position indicated by user C's body index may be point (1410), and the actual position indicated by user D's body index may be point (1450).

[0180] Assume that both users C and D moved from an index where BMI_index is 7 and PBF_index is 3 to an index where BMI_index is 7 and PBF_index is 2. For example, assume that user C moved from point (1410) to point (1420), and user D moved from point (1450) to point (1460).

[0181] The actual distance traveled by user C's body index may be distance (1430). The actual distance traveled by user D's body index may be distance (1470). For example, distance (1430) may be shorter than distance (1470). The actual distance traveled may be referred to as the Euclidean distance.

[0182] In one embodiment, an electronic device may apply different weights to each body index when determining the distance traveled. The electronic device may apply greater weight to body indexes that are relatively more difficult to change. For example, the electronic device may apply a greater weight to BMI_index. For example, the electronic device may apply a weight of 0.7 to BMI_index and a weight of 0.3 to PBF_index. By applying a greater weight to BMI_index, a distance may be determined to be longer when the BMI decreases more significantly, even if the degree of change in body index is similar.

[0183] An electronic device can determine time information by dividing the user's time spent changing an index by the distance traveled by the user's body index, and normalizing the time spent per unit distance. The distance traveled by the body index can be a Euclidean distance or a weighted distance. For example, assume that user C took 50 days to travel a distance (1430) and user D took 50 days to travel a distance (1470). If the user's time spent changing an index is normalized to the time spent per unit distance by dividing the user's time spent changing an index by the distance traveled by the user's body index, the time spent per unit distance for user C may be greater.

[0184] The extracted time series data includes the measurement date, PBF, BMI, BMI_Index, PBF_Index, and ClusterChange, so that the electronic device can determine time information representing the time consumed per unit distance by dividing the time consumed to move from each index to each adjacent index in a coordinate space using body index as a coordinate by the distance traveled. The time information may be a histogram representing the time consumed per unit distance.

[0185]

[0186] Referring to Fig. 15, time information (1500) indicating the consumption time per unit distance of users who moved from index (5,3) to the left (e.g., (5,2)) is illustrated. In Fig. 15, for convenience of explanation, only the time information (1500) indicating the consumption time per unit distance of users who moved from index (5,3) to the left (e.g., (5,2)) is illustrated. However, since the two-dimensional space with BMI and PBF as axes is divided into nine percentile sections, it should be understood that more time information can be determined since it is possible to move in eight directions (e.g., up, down, left, right, upper left, upper right, lower left, and lower right) in a total of 81 indices.

[0187] The electronic device can determine, based on the time information (1500), the time it is expected to take for the target user's body indicator to move from the starting cluster to the closest cluster on the target route.

[0188] The electronic device can determine the expected time using any one of the mode, median, mean, and 2 sigma in the time information (1500). The electronic device can generate a baseline (1510) based on any one of the mode, median, mean, and 2 sigma. The electronic device can determine the time spent per unit distance that intersects the baseline (1510) as the expected time.

[0189] The electronic device can determine the expected time by selecting one of the following methods: mode, median, mean, or 2-sigma, depending on the target user's preferences. The target user's preferences can be input through settings. For example, a target user with a challenging personality may desire quick achievements, so the electronic device may determine the expected time based on 2-sigma. For example, a target user with a stable personality may desire certain achievements, so the electronic device may determine the expected time based on the mean.

[0190]

[0191] Figure 16 is a flowchart for explaining the operation of a user terminal according to one embodiment.

[0192] The operations described below may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel. Furthermore, some operations may be omitted in some embodiments. Operations (1610) to (1630) may be performed by at least one component (e.g., a processor) of the user terminal.

[0193] According to one embodiment, instructions stored in memory by at least one processor may be individually or collectively executed and may cause a user terminal to perform the following operations.

[0194] In operation (1610), the user terminal can obtain a user input for selecting a target cluster targeted by the target user from among a plurality of clusters based on body indicators from the target user.

[0195] In operation (1620), the user terminal may, in response to obtaining user input, display a target route that provides a guide to reach the target cluster from the starting cluster containing the target user's body indicators.

[0196] In operation (1630), the user terminal may display the expected time and difficulty it will take for the target user's body indicator to move from the starting cluster to the closest cluster on the target route.

[0197] An electronic device communicating with a user terminal may, in response to receiving user input from the user terminal, determine a target route, time, and difficulty based on time-series data of body parameters previously acquired from multiple users. The electronic device may then provide the target route, expected time, and difficulty to the user terminal.

[0198] Actions (1610) to (1650) have been described in detail in FIGS. 1 to 15. Therefore, detailed descriptions of actions (1610) to (1650) are omitted.

[0199]

[0200] FIG. 17 is a drawing for explaining an electronic device according to one embodiment.

[0201] According to one embodiment, an electronic device (1700) can guide goal setting and goal achievement based on body index. The electronic device (1700) can include, but is not limited to, various computing devices such as mobile phones, smart phones, tablets, e-book devices, laptops, personal computers, desktops, workstations, or servers, various wearable devices such as smart watches, smart glasses, or HMDs, various home appliances such as smart speakers, smart TVs, or smart refrigerators, smart cars, smart kiosks, IoT devices, WADs, drones, robots, or body index measuring devices.

[0202] The electronic device (1700) may include a processor (1710) and a memory (1720). The processor (1710) may perform the overall function of controlling the electronic device (1700). The processor (1710) may control the electronic device (1700) overall by executing programs and / or instructions stored in the memory (1720). The processor (1710) may be implemented as a CPU, GPU, AP, etc. provided in the electronic device (1700), but is not limited thereto. The number of processors (1710) may be one or more. For example, the processor (1710) may have a multi-core processor structure such as a dual-core, quad-core, or hexa-core. The processor (1710) may control the operations of the electronic device (1700) by executing instructions stored in the memory (1720). For example, the processor (1710) may correspond to a plurality of processors that collectively perform a plurality of operations by dividing them among the processors.

[0203] The memory (1720) may be hardware that stores data processed and data to be processed within the electronic device (1700). In addition, the memory (1720) may store applications, drivers, etc. to be driven by the electronic device (1700). In addition, the memory (1720) may include instructions. The memory (1720) may include one or more memories. The instructions stored in the memory (1720) may be stored in a single memory. The instructions stored in the memory (1720) may be divided and stored in multiple memories. The memory (1720) may include volatile memory such as DRAM and / or non-volatile memory.

[0204] The electronic device (1700) can select a target cluster targeted by the target user from among a plurality of clusters based on body indices based on user input. The electronic device (1700) can determine a target route that guides the target user from a starting cluster containing the target user's body indices to the target cluster based on time series data of body indices previously acquired from a plurality of users. The electronic device (1700) can determine, based on the time series data, the expected time and difficulty level for the target user's body indices to move from the starting cluster to the closest cluster along the target route. The electronic device (1700) can provide the target user with the target route, time, and difficulty level.

[0205] The electronic device (1700) can determine a plurality of candidate paths based on a starting cluster and a target cluster from among a plurality of predetermined paths based on an index indicating location information of a body index. The electronic device (1700) can determine a target path from among the plurality of candidate paths and determine a target route based on the target path.

[0206] The electronic device (1700) can determine a plurality of candidate paths starting from an index representing a physical indicator of a target user and ending at any one index included in a target cluster.

[0207] The electronic device (1700) can extract data from time series data indicating changes in an index representing statistical positional information about a body index compared to the previous data. Based on the extracted data, the electronic device (1700) can determine movement ratio information indicating the rate of movement from each index to each adjacent index. The electronic device (1700) can determine the difficulty level based on the movement ratio information.

[0208] The electronic device (1700) can generate a histogram based on the movement ratio information and the number of data included in the movement ratio information as an axis. The electronic device (1700) can determine a cumulative distribution function based on the histogram. The electronic device (1700) can determine the difficulty level based on the cumulative distribution function.

[0209] The electronic device (1700) can extract data from time-series data indicating changes in an index representing statistical location information for a body index compared to the previous data. Based on the extracted data, the electronic device (1700) can determine time information indicating the time consumed per unit distance to move from each index to each adjacent index. Based on the time information, the electronic device (1700) can determine an expected time.

[0210] The electronic device (1700) can determine time information representing the time consumed per unit distance by dividing the time consumed to move from each index to each adjacent index in a coordinate space using body indices as coordinates by the movement distance of the index.

[0211] Body parameters may include BMI and PBF.

[0212] The electronic device (1700) may select one of two or more top clusters with the highest score based on body parameters as a target cluster based on user input.

[0213] The electronic device (1700) can select, based on user input, one of the clusters having a score above average based on body parameters as a target cluster.

[0214] The electronic device (1700) can select one of the clusters determined based on a period set by the target user as the target cluster based on user input.

[0215] It is obvious to those skilled in the art that the description of the operations described in FIGS. 1 to 16 can be equally applied to the operation of the electronic device (1700) described above.

[0216]

[0217] Meanwhile, the method according to the present invention can be written as a program that can be executed on a computer and implemented in various recording media such as a magnetic storage medium, an optical reading medium, and a digital storage medium.

[0218] Implementations of the various technologies described herein may be implemented as digital electronic circuitry, or as computer hardware, firmware, software, or combinations thereof. Implementations may be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., a machine-readable storage medium (computer-readable medium) or a radio signal, for processing by the operation of a data processing device, e.g., a programmable processor, a computer, or multiple computers, or for controlling the operation thereof. A computer program, such as the computer program(s) described above, may be written in any form of programming language, including compiled or interpreted languages, and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may be deployed to be processed on one computer or multiple computers at a single site, or to be distributed across multiple sites and interconnected by a communications network.

[0219] Processors suitable for processing a computer program include, for example, both general-purpose and special-purpose microprocessors, and any one or more processors of any type of digital computer. Typically, a processor will receive instructions and data from read-only memory or random-access memory, or both. Components of a computer may include at least one processor for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer may include, or be coupled to receive data from, transmit data to, or both, one or more mass storage devices, such as magnetic, magneto-optical, or optical disks, for storing data. Information carriers suitable for embodying computer program instructions and data include, for example, semiconductor memory devices, magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as compact disk read only memory (CD-ROM), digital video disks (DVD), magneto-optical media such as floptical disks, read only memory (ROM), random access memory (RAM), flash memory, erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), etc. The processor and memory may be supplemented by, or included in, special purpose logic circuitry.

[0220] Additionally, the computer-readable medium may be any available medium that can be accessed by a computer, and may include both computer storage media and transmission media.

[0221] While this specification contains details of a number of specific implementations, these should not be construed as limitations on the scope of any invention or what may be claimed, but rather as descriptions of features that may be unique to particular embodiments of particular inventions. Certain features described herein in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments, either individually or in any suitable subcombination. Furthermore, although features may operate in a particular combination and may initially be described as being claimed as such, one or more features from a claimed combination may in some cases be excluded from that combination, and the claimed combination may be modified into a subcombination or variation of a subcombination.

[0222] Likewise, while operations are depicted in the drawings in a particular order, this should not be construed as requiring that those operations be performed in the particular or sequential order depicted to achieve desired results, or that all depicted operations be performed. In certain instances, multitasking and parallel processing may be advantageous. Furthermore, the separation of the various device components of the embodiments described above should not be construed as requiring such separation in all embodiments, and it should be understood that the program components and devices described may generally be integrated together in a single software product or packaged into multiple software products.

[0223] Meanwhile, the embodiments of the present invention disclosed in this specification and drawings are merely specific examples presented to aid understanding and are not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that other modifications based on the technical concepts of the present invention are possible in addition to the embodiments disclosed herein.

Claims

1. In the method of operating an electronic device, An action of selecting a target cluster targeted by a target user from among multiple clusters based on body indicators, based on user input; An operation of determining a target route that guides the target user from a starting cluster that includes the target user's body indicators to the target cluster based on time series data of body indicators obtained in advance from multiple users; Based on the above time series data, an operation of determining the expected time and difficulty it will take for the target user's body indicator to move from the starting cluster to the closest cluster on the target route; and An action of providing the target route, time, and difficulty to the target user. including, How it works.

2. In paragraph 1, The action of determining the above target route is: An operation of determining a plurality of candidate paths based on the starting cluster and the target cluster among a plurality of predetermined paths based on an index indicating location information of the above body indicator; and An operation of determining a target path among the plurality of candidate paths and determining the target route based on the target path. including, How it works.

3. In paragraph 2, The operation of determining the above multiple candidate paths is: Starting from an index representing the physical indicator of the target user, determining a plurality of candidate paths that arrive at an index included in the target cluster. How it works.

4. In paragraph 1, The action of judging the expected time and difficulty above is, An operation of extracting data from the time series data that has changed when compared to the previous data, wherein the index representing statistical location information for the above body indicators is compared; An operation of determining movement ratio information indicating the movement ratio from each index to each of the adjacent indexes based on the extracted data; An action to determine the difficulty level based on the above movement ratio information including, How it works.

5. In paragraph 4, The operation of determining the difficulty based on the above movement ratio information is: An operation of generating a histogram with the number of data included in the ratio and the movement ratio information as axes based on the movement ratio information; An operation of determining a cumulative distribution function based on the above histogram; and An operation to determine the difficulty level based on the above cumulative distribution function. including, How it works.

6. In paragraph 1, The action of judging the expected time and difficulty above is, An operation of extracting data from the time series data that has changed when compared to the previous data, wherein the index representing statistical location information for the above body indicators is compared; An operation of determining time information representing the time consumed per unit distance consumed in moving from each index to each of the adjacent indexes based on the extracted data; and An action to determine the expected time based on the above time information. including, How it works.

7. In paragraph 6, The action of determining the above time information is: In a coordinate space with the above body index as a coordinate, the time spent moving from each index to each of the adjacent indexes is divided by the movement distance of the index to determine the time information representing the time spent per unit distance. How it works.

8. In paragraph 1, The above physical indicators are, Including BMI (body mass index) and PBF (percentage body fat), How it works.

9. In paragraph 1, The action of selecting the above target cluster is: Based on the user input, one of the two or more top clusters with the highest score based on the body index is selected as the target cluster. How it works.

10. In paragraph 1, The action of selecting the above target cluster is: Based on the user input, one of the clusters having a score above average based on body parameters is selected as the target cluster. How it works.

11. In paragraph 1, The action of selecting the above target cluster is: Based on the user input, one of the clusters determined based on the period set by the target user is selected as the target cluster. How it works.

12. In the method of operating the user terminal, An action of obtaining user input for selecting a target cluster targeted by the target user from among a plurality of clusters based on body indices from the target user; In response to obtaining the user input, an action of displaying a target route that provides a guide to reach the target cluster from the starting cluster that includes the body indicators of the target user; and An action that indicates the expected time and difficulty it will take for the target user's body indicators to move from the starting cluster to the closest cluster on the target route. Including, An electronic device communicating with the user terminal, In response to receiving the user input from the user terminal, determining the target route, the time, and the difficulty based on time series data of body indicators acquired in advance from a plurality of users, and providing the target route, the expected time, and the difficulty to the user terminal. How it works.

13. In electronic devices, At least one processor comprising processing circuitry; and Memory that stores instructions Including, When the above instructions are individually or collectively executed by the at least one processor, the electronic device causes: Based on user input, select a target cluster targeted by the target user among multiple clusters based on body parameters, Based on time series data of body indices obtained in advance from multiple users, a target route is determined that guides the target user from a starting cluster that includes the body indices of the target user to the target cluster, Based on the above time series data, the time and difficulty that the target user's body indicator is expected to take to move from the starting cluster to the closest cluster on the target route are determined, To provide the target route, time and difficulty to the target user. Electronic devices.

14. In paragraph 13, When the above instructions are individually or collectively executed by the at least one processor, the electronic device causes: Determine a plurality of candidate paths based on the starting cluster and the target cluster among a plurality of predetermined paths based on an index indicating the location information of the above body indicator, Determine a target path among the plurality of candidate paths and determine the target route based on the target path. Electronic devices.

15. In paragraph 14, When the above instructions are individually or collectively executed by the at least one processor, the electronic device causes: A method of determining a plurality of candidate paths starting from an index representing the physical indicator of the target user and ending at any one index included in the target cluster. Electronic devices.

16. In paragraph 13, When the above instructions are individually or collectively executed by the at least one processor, the electronic device causes: An index representing statistical location information for the above body indicators is extracted from the time series data, and data that has changed when compared to the previous data is extracted, Based on the above extracted data, movement ratio information indicating the movement ratio from each index to each adjacent index is determined, To determine the difficulty level based on the above movement ratio information, Electronic devices.

17. In paragraph 16, When the above instructions are individually or collectively executed by the at least one processor, the electronic device causes: Based on the above movement ratio information, a histogram is created with the ratio and the number of data included in the above movement ratio information as axes, Determine the cumulative distribution function based on the above histogram, To determine the difficulty based on the above cumulative distribution function, Electronic devices.

18. In paragraph 13, When the above instructions are individually or collectively executed by the at least one processor, the electronic device causes: An index representing statistical location information for the above body indicators is extracted from the time series data, and data that has changed when compared to the previous data is extracted, Based on the above extracted data, time information representing the time consumed per unit distance spent moving from each index to each adjacent index is determined, To determine the expected time based on the above time information, including, Electronic devices.

19. In paragraph 18, When the above instructions are individually or collectively executed by the at least one processor, the electronic device causes: Determine the time information representing the time consumed per unit distance by dividing the time consumed in moving from each index to each of the adjacent indexes in the coordinate space using the above body index as a coordinate by the movement distance of the index. Electronic devices.

20. In paragraph 13, The above physical indicators are, Including BMI and PBF, Electronic devices.

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