Method for operating a server for monitoring and mutual evaluation according to motion matching

Abstract

The present application provides a server operation method for monitoring and mutual evaluation according to motion matching, a stage of obtaining initial information of a first student about a target sports project through a student terminal of the first student; a stage of creating at least one group for a coach of a plurality of students including the first student according to the initial information; a stage of receiving information of a student of a second group of students of the first student from a student of a first group of students matched with a first group of lecturers including the first student according to the information of the first group of students and the second group of students of the user, and training according to the information of the first group of students and the second group of students of the user. In the generation case, a stage of providing the first student motion history information and motion evaluation information as a lecturer terminal of a second lecturer matched with the second group can be included.

Description

Technical Field

[0001] This disclosure pertains to the operation of a server, and more specifically, to a server that provides matching services between trainees and mutual monitoring services for sports assessments, for coaching purposes in the same sport. Background Technology

[0002] I've recently become very interested in sports teams where a large number of people exercise together. These teams typically recruit members through social media platforms like SNS, making it difficult to base their activities on individual skill levels or goals. Therefore, due to the differences in ability among team members, it's hard to set specific goals.

[0003] Therefore, it is necessary to consider factors such as members' athletic ability, goals, and physical fitness levels to form similar groups, and promote the growth and development of the groups through the sharing of sports data among members.

[0004] At the same time, in order to enable each member to participate in sports activities, not only can they get feedback from the instructors, but they can also be provided with sports activities suitable for each member's ability. A systematic suggestion is needed to allow instructors to share members' sports data.

[0005] Existing technical documents

[0006] Patent documents

[0007] (Patent Document 1) Registered Patent Publication No. 10-2268607 Summary of the Invention

[0008] The problem that the invention aims to solve

[0009] Through this launch, we will provide instructions on how to operate the server, as well as matchmaking services for trainees who wish to mentor each other in the same sport and mutual monitoring services for sports assessments.

[0010] The purpose of this launch is not limited to the purposes described above. Other purposes and advantages of this launch not mentioned may be understood from the following description, or more clearly from examples of implementation of this launch. Furthermore, it will be readily apparent that the purposes and advantages of this launch may be achieved by means of the claims in the patent application and combinations thereof.

[0011] means for solving problems

[0012] According to an implementation example of this course, the server's operation method for monitoring and peer evaluation based on sports matching includes: a stage of obtaining initial information about the first student regarding the target sports through the first student's student terminal; a stage of creating at least one group for coaches of multiple students, including the first student, based on the initial information; a stage of training based on the first group of students' history (including the first student), the instructor information received by the first group of students, and the student information of the first group of students, and inputting the student information of the first group of students. If a second group is generated, it may include a stage of providing the first student's sports history information and sports evaluation information through the instructor terminal of a second instructor matched with the second group.

[0013] In addition, the operation method of the server may include the step of providing the first student's motion assessment information through the individual student terminals of the multiple students included in the first group.

[0014] Furthermore, the server's operation method includes the steps of obtaining relevant assessment disclosure condition information through user input received from the first student's student terminal, and providing the first student's motion assessment information; if the condition information is met, the first student's motion assessment information can be provided through the student terminals of the multiple students included in the first group.

[0015] Additionally, in the stage of acquiring the initial information of the first student, initial information including the first student's athletic tendency, physical fitness level, athletic ability, and at least one target is acquired. In the stage of generating at least one group of coaches for the multiple students, initial information for one group is generated, which includes multiple students whose similarity to the initial information of the multiple students exceeds a threshold. Initial information containing the multiple students is then generated. Based on the representativeness information, at least one instructor related to the target sport can be selected to match the generated group.

[0016] Furthermore, during the stage of generating the first student's sports resume information and sports evaluation information, if sports record data related to the target sports project performed by the first student is received from the first lecturer's lecturer team, the sports record data can be added to the first student's sports resume information, and the first student's ability evaluation information for each subject of the sports project can be generated based on the first lecturer's evaluation information of the first student.

[0017] Furthermore, the server's operation method includes a stage of obtaining personal information containing the first student's gender, health status, and at least one age from the first student's student terminal, and a stage of generating the first student's exercise history information and exercise evaluation information. The exercise history information can be used to identify the frequency of the first student's performance of the sports activity, and the first student can be evaluated based on the exercise frequency, health status, gender, and age to generate the first student's exercise information.

[0018] Furthermore, the server's operation method can also, based on the sports evaluation information, apply predetermined individual weights to the various ability values ​​of the first student according to the sports behaviors related to the target sports project, and sum up the individual ability values ​​of the individual weights applied to the sports behaviors according to the sports behaviors to generate sports plan information containing the stage of each sports behavior score and the sports behavior with the lowest calculated score, which serves as the terminal for generating the first student's course.

[0019] Furthermore, the server's operation method can generate a stage containing at least one candidate sports item selected by the first student, and a terminal containing planned exercise information of the candidate sports item, based on the similarity of the first student's initial information, so as to identify the stage with the highest ability value from the first student's exercise evaluation information, identify at least one candidate sports item for the first student's highest ability value, and the similarity of the first student's initial information for each candidate sports item.

[0020] Furthermore, the operation method of the server may include a stage of receiving post-instruction information from the first instructor's student terminal, and a stage of providing the post-instruction information from the first instructor's instructor terminal after the first instructor has finished guiding the first group.

[0021] Invention Effects

[0022] This initiative will allow participants with similar goals or athletic inclinations to join group activities, and enable members of the same group to view each other's evaluations, thus promoting growth and development within the group.

[0023] At the same time, through this launch, exercise plans can be provided based on trainees' evaluation information to help them grow continuously and achieve their goals. Attached Figure Description

[0024] Figure 1 This diagram illustrates the operation of a server communicating with various terminal devices, based on an embodiment of this city.

[0025] Figure 2This is a flowchart illustrating the operation of the server generating student information, based on an implementation example in this city.

[0026] Figure 3 This is a diagram illustrating the actions of a server communicating with multiple lecturer terminals, based on an embodiment of this city.

[0027] Figure 4 The diagram illustrates the actions of a server communicating with multiple student terminals, based on an example from this city.

[0028] Figure 5 This is a flowchart illustrating the actions of a server in generating exercise plan information, based on a real-time example from this city.

[0029] Figure 6 This is a block diagram of the server configuration based on the daily implementation method of this city.

[0030] Explanation of reference numerals in the attached figures

[0031] 100: Server;

[0032] 110: Memory;

[0033] 120: Processor;

[0034] 130: Communication interface;

[0035] 200: Student terminal;

[0036] 300: Instructor Terminal. Detailed Implementation

[0037] Before explaining this instruction in detail, the method for filling out this list and drawings will be explained.

[0038] First, considering the function of the terms used in this guide and the scope of claims in various implementation examples of this guide, some general terms have been selected. However, these terms may vary depending on the intent of a person skilled in the art, legal or technical interpretations, and the emergence of new technologies. Additionally, some terms may be arbitrarily chosen by the applicant. These terms may be interpreted as meanings defined in this list, or, where no specific terminology definition exists, may be interpreted based on the overall content of this list and common technical knowledge in the field.

[0039] Furthermore, the same reference numerals or symbols used in each of the accompanying drawings actually represent parts or components that perform the same function. For ease of explanation and understanding, the same reference numerals or symbols are also used in different embodiments. That is, even if all components with the same reference numerals are listed in multiple drawings, the multiple drawings do not necessarily represent a single implementation example.

[0040] Furthermore, in this description and scope of claims, terms containing ordinal numbers, such as "first," "second," etc., may be used to distinguish components. These ordinal numbers are used to distinguish identical or similar components and should not limit the meaning of the terms. For example, components associated with these ordinal numbers should not be restricted by the order in which their numbers are used or placed. The ordinal numbers may also be used interchangeably as needed.

[0041] In this listing, singular expressions include plural expressions unless the context clearly distinguishes the meanings. In this application, terms such as “comprising” or “constituting” should be understood to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof listed, without excluding the possibility of the presence or addition of one or more other features or numbers, steps, actions, components, parts, or combinations thereof.

[0042] In this introductory implementation example, the terms "module," "unit," "part," etc., refer to components that perform at least one function or operation. These components can be implemented in hardware or software, or a combination of both. Furthermore, while each "module," "unit," "part," etc., requires a separate, specific hardware implementation, they can be integrated into at least one module or chip and implemented by at least one processor.

[0043] Furthermore, in the implementation examples of this guide, if a part is connected to other parts, this includes not only direct connections but also indirect connections via other media. Moreover, the statement that a part contains a component means that, unless specifically stated otherwise, it may contain other components, rather than excluding them.

[0044] Figure 1 This diagram illustrates the operation of a server communicating with various terminal devices, based on an embodiment of this city.

[0045] like Figure 1 As shown, server 100 can communicate with student terminal 200 and lecturer terminal 300.

[0046] Server 100 may be the main entity of the operating platform, providing matching services between trainees and monitoring and evaluating their exercise.

[0047] As a temporary example, server 100 can be implemented as a server device or system, which contains at least one computer.

[0048] At this time, server 100 can communicate with multiple terminal devices through at least one webpage and / or application that makes up the platform, generate exercise history information or exercise evaluation information based on the input received by the user from the terminal device through at least one webpage and / or application, and provide the exercise evaluation information to student terminal 200 or instructor terminal 300.

[0049] In addition, server 100 can provide software (computer program) for at least one terminal device, which generates exercise history information or exercise evaluation information based on user input received from the terminal device running the software, and provides the exercise evaluation information to student terminal 200 or instructor terminal 300.

[0050] Figure 2 This is a flowchart illustrating the operation of the server generating student information, based on an implementation example in this city.

[0051] like Figure 2 As shown, server 100 can obtain the trainee's initial information about the target sport (S210).

[0052] As an example, server 100 can obtain the first student's initial information about the target sport through the first student's student terminal 200-1.

[0053] The target sport can be one of many sports such as diving, swimming, yoga, and mountain climbing.

[0054] Initial information may include at least one of the trainee's athletic inclinations, fitness level, athletic ability, and goals for the target sport. Here, the trainee's athletic inclinations may fall under the category identified through the testing service.

[0055] As an additional implementation example, server 100 can identify a student's exercise preferences by providing multiple queries through student terminal 200. Here, exercise preferences can consist of multiple categories (e.g., indoor, outdoor, high-intensity, low-intensity, individual exercise, team exercise, etc.).

[0056] Specifically, server 100 can provide multiple queries and multiple query options for the student's movement tendency recognition through student terminal 200.

[0057] At this point, a sub-score is set for each selected area. Alternatively, based on the user input received by the student terminal 200, the scores of multiple items can be totaled using the selected area as a basis, and the element that produces the highest score can be selected to identify the student's movement tendency.

[0058] Server 100 can create coaching groups for multiple trainees based on initial information (S220).

[0059] As a temporary example, server 100 can create at least one coaching group for multiple students, including the first student, based on initial information.

[0060] Specifically, server 100 can calculate the similarity between initial information among trainees who input initial information about a target sport.

[0061] For example, server 100 may contain at least one first artificial intelligence model trained to convert the text of items constituting the initial information into vectors and to calculate similarity based on the distance between the vectors. However, this artificial intelligence model may consist of an embedding model to convert the text into semantic-based vectors, but is not limited thereto.

[0062] For example, the distance between vectors can be calculated based on the differences in vector values ​​in each dimension. The first artificial intelligence model can be trained to determine that the closer the distance between vectors, the higher their similarity.

[0063] Therefore, server 100, as the first artificial intelligence model, can input the initial information of multiple trainees, obtain the similarity between the initial information based on the output data, and generate at least one group of trainees corresponding to the initial information, which corresponds to the initial information with similarity exceeding the threshold.

[0064] At this point, server 100 can generate representative information for the group based on the initial information of each of the multiple students contained in the group.

[0065] Specifically, server 100 can generate representative information by extracting keywords from the initial information of multiple students in the group.

[0066] Specifically, the text containing items in each initial information can be converted into vector form, and the frequency of word occurrence and the degree of correlation between words can be identified based on the vector of each word in the text, thereby extracting at least one keyword for each item.

[0067] For example, server 100 can take text as input to a module that processes natural language to extract one or more words. At this point, at least one second artificial intelligence model, trained to convert each word into a vector form, can be utilized; each word can be defined as a multi-dimensional vector that can be recognized by a machine. The second artificial intelligence model can be constructed based on at least one of the following algorithms: BOW (Bag of Words), Document Term Matrix (DTM), TF-IDF (Term Frequency-Inverse Document Frequency), LSA (Latent Semantic Analysis), and LDA (Latent Dirichlet Allocation).

[0068] Therefore, server 100 can select at least one lecturer from those related to the target sport to match the group, based on representativeness information.

[0069] For example, server 100 can use the professional sports programs of multiple instructors stored on server 100 and instructor information including sports preferences to match groups and instructors.

[0070] Specifically, server 100 can match the group's representative information with the lecturer information that has the highest similarity to the representative information of multiple lecturers, and the lecturer information that corresponds to the lecturer.

[0071] For example, server 100 can input the lecturer information of multiple lecturers and a set of representative information into the first artificial intelligence model, and obtain the similarity of the representative information of the lecturer information of multiple lecturers based on the output data.

[0072] In this way, server 100 can match the lecturer corresponding to the lecturer information with the highest similarity to the representative information with the group.

[0073] Server 100 can generate exercise history information and exercise evaluation information of trainees based on user input received from instructor terminal 300 (S130).

[0074] As an example, server 100 can generate the first student's exercise history information based on the user input received from the first instructor terminal 300-1, which is the first instructor terminal matched with the first student.

[0075] Specifically, after receiving the sports record data related to the target sports project performed by the first student from the first lecturer's lecturer terminal 300-1, the server 100 can add the sports record data to the first student's sports resume information.

[0076] Exercise data can include the date the trainee received instruction from the instructor on the target sport, and the results of the instruction (e.g., stride length, exercise time, diving depth, speed, etc.).

[0077] Furthermore, server 100 can generate sports evaluation information containing the first student's ability values ​​for various aspects of the target sport based on the first instructor's evaluation information received from the first instructor's instructor terminal 300-1. For example, the evaluation information may include scores for multiple items such as muscle strength, explosive power, mental strength, cardiorespiratory endurance, and muscular endurance.

[0078] Specifically, server 100 can calculate the first student's sub-ability value based on the sub-scores contained in the first lecturer's evaluation information.

[0079] As an example, server 100 can receive personal information from the first student's student terminal 200-1, including the first student's gender, health status (e.g., whether or not they have a disease), and at least one age, and use this personal information to correct the first instructor's assessment information to generate exercise assessment information.

[0080] Specifically, server 100 can identify the frequency of the first student's performance in the target sports activity based on the first student's sports history information, and correct the first instructor's evaluation information based on the sports frequency, gender, health status, and age to generate sports evaluation information.

[0081] For example, server 100 can obtain the weight of the first trainee based on the preset weight of exercise frequency, the preset weight of gender, the preset weight of health status, and the preset weight of age, and apply it to the sub-scores of the first lecturer, thereby correcting the evaluation information of the first lecturer and calculating the sub-ability values ​​contained in the exercise evaluation information.

[0082] At this point, server 100 can apply the set weights to each item of the first student's ability value according to the sports behavior related to the target sports item based on the sports evaluation information, and combine the weights of each item of the sports behavior according to the sports behavior to calculate the score of each sports behavior.

[0083] For example, in terms of sports-related behaviors, swimming-related behaviors may include diving, freestyle, breaststroke, butterfly, backstroke, etc. Even within the same sport, the weighting may vary depending on the specific sports behavior.

[0084] Here, server 100 can generate exercise plan information containing the lowest-scoring exercise behavior, which is provided to student terminal 200-1 of the first student.

[0085] For example, server 100 can generate exercise plan information that increases the training frequency of the corresponding movement behavior, because they believe that the lower the score, the more the learner needs to further train that movement behavior.

[0086] At the same time, server 100 can generate exercise plan information, that is, reduce the training frequency of the exercise behavior with the highest score and increase the training frequency of the exercise behavior with the lowest score.

[0087] In addition, server 100 can perform additional positioning based on the individual sports resume and sports evaluation information of the trainees receiving guidance in the target sports.

[0088] As a one-off example, server 100 can create at least one group consisting of trainees whose similarity between exercise history information and exercise evaluation information exceeds a threshold.

[0089] Specifically, server 100 inputs the sports history information of each student receiving guidance for the target sports project into the first artificial intelligence model, and obtains the similarity between the sports history information of each student based on the output data. It also inputs the sports evaluation information of each student receiving guidance for the target sports project into the first artificial intelligence model, and obtains the similarity between the sports evaluation information of each student based on the output data.

[0090] In addition, server 100 can update the initial information of the first student based on exercise history information and exercise evaluation information.

[0091] Specifically, server 100 can update at least one of the first trainee's physical fitness level and athletic ability based on sports history information and sports evaluation information.

[0092] For example, server 100 can identify fitness level based on the change in coaching results for coaching dates included in the fitness history information. Specifically, if the change in coaching results increases over time, server 100 can update the fitness level by adding the fitness level included in the initial information.

[0093] In addition, server 100 can also identify the athletic ability corresponding to the average ability value of each item in the athletic assessment information, so as to update the athletic ability contained in the initial information.

[0094] Figure 3 This is a diagram illustrating the actions of a server communicating with multiple lecturer terminals, based on an embodiment of this city.

[0095] like Figure 3As shown, server 100 can communicate with student terminal 200, lecturer terminal 300-1 of the first lecturer, and lecturer terminal 300-2 of the second lecturer.

[0096] As an example, if a second group containing the first student is created, server 100 can act as a lecturer terminal 300-2 matching the second lecturer of the second group to provide the first student's sports history and sports evaluation information.

[0097] In this way, the second instructor can provide guidance to the first student based on the first student's physical strength and athletic ability.

[0098] Figure 4 This diagram illustrates the actions of a server communicating with multiple student terminals, based on an embodiment of this city.

[0099] like Figure 4 As shown, server 100 can communicate with student terminal 200-1 of the first student and student terminals 200-2 and 200-3 of each of the multiple students in the first group.

[0100] As an example, server 100 can provide motion assessment information of the first student as the student terminals 200-2 and 200-3 of the multiple students in the first group.

[0101] In related aspects, server 100 can obtain information about the conditions for public evaluation by receiving user input from the student terminal 200-1 of the first student.

[0102] Conditional information may include the baseline values ​​of each item's ability in the sports evaluation information, the public release time of the sports evaluation information, and the non-public release time of the sports evaluation information.

[0103] In this case, after the conditions are met, the server 100 can provide the first student's motion assessment information through the student terminals 200-2 and 200-3 of the multiple students in the first group.

[0104] For example, if the condition information includes the standard values ​​of each item's ability value in the sports evaluation information, the server 100 can provide the first student's sports evaluation information as the student terminals 200-2 and 200-3 of multiple students respectively.

[0105] In addition, if the condition information includes the disclosure time of the sports evaluation information, the server 100 can provide the sports evaluation information of the first student as the student terminals 200-2 and 200-3 of multiple students after the disclosure time is reached.

[0106] In addition, if the condition information includes a non-public time point of motion evaluation information, after receiving the condition information from the first student's student terminal 200-1, the server 100 can provide the first student's motion evaluation information to the student terminals 200-2 and 200-3 of multiple students. However, if the non-public time point is reached, the first student's motion evaluation information cannot be provided to the student terminals 200-2 and 200-3 of multiple students.

[0107] Figure 5 This is a flowchart illustrating the actions of a server in generating exercise plan information, based on a real-time example from this city.

[0108] like Figure 5 As shown, server 100 can identify the item with the highest ability value of the trainee from the sports assessment information (S510).

[0109] Specifically, server 100 can identify the item with the highest ability value among all items in the first student's evaluation information.

[0110] Server 100 can identify matching candidate sports for the sports with the highest ability scores of trainees (S520).

[0111] Specifically, server 100 can identify at least one candidate sport that matches the sport with the highest ability value of the first trainee based on matching information containing at least one candidate sport. These sports match at least one of the multiple sports that constitute the sports evaluation information.

[0112] For example, muscular endurance can be matched with strength training, rowing, etc., and cardiorespiratory endurance can be matched with swimming, mountain climbing, etc., but it is not limited to these.

[0113] Server 100 can calculate the similarity between the student's sports history information and the sports characteristic information of the candidate sports (S530).

[0114] Sports characteristic information may include the difficulty of the sport, the environment in which the sport is performed (e.g., in water, in the mountains, indoors, etc.), and the sports that match the sport (e.g., muscle strength, mental strength, endurance, etc.).

[0115] Specifically, server 100 can calculate the similarity between the initial information of the first student and the sports characteristics information of the candidate sports projects that match the project with the highest ability value of the first student.

[0116] For example, server 100, as the first artificial intelligence model, can input the motion characteristic information of each candidate sports and the initial information of the first student, and obtain the similarity of the first student's initial information on the motion characteristic information of each candidate sports based on the output data.

[0117] Server 100 can select candidate sports based on similarity and generate exercise plan information (S540).

[0118] Specifically, server 100 can select the candidate sports that has the highest similarity to the first student's initial information from among the candidate sports that match the first student's highest ability value, and generate exercise plan information containing the selected candidate sports. The exercise plan information may include, but is not limited to, sports, exercise frequency, intensity, and goals.

[0119] In addition, server 100 can identify the first student's performance and add it to the sports evaluation information.

[0120] Specifically, server 100 can calculate the average of the ability values ​​for each item based on the date the first student received instruction from the first instructor. Here, server 100 can identify daily progress based on the difference between the average ability values ​​calculated by date.

[0121] In addition, the server 100 can identify the degree of increase or decrease in the average capability value based on the average capability value calculated by date, according to the time period (e.g., 7 days, 30 days, 100 days, etc.), and can also identify the results of each period that are proportional to the degree of increase or decrease in the average capability value.

[0122] Here, server 100 can generate exercise plan information by selecting at least one of the following modes: either a first mode for changing the sports activity or a second mode for changing the frequency of the target sports activity, based on the first student's performance and exercise preferences.

[0123] Specifically, if the first student's score is below the standard value, server 100 can decide whether to select the first mode based on the similarity between the first student's athletic tendency and the target sport.

[0124] For example, server 100 can input the first student's movement tendency and the movement characteristics information of the target sports project into the first artificial intelligence model, and obtain the similarity between the target sports project and the first student's movement tendency based on the output data. When the similarity is lower than the threshold, the first mode can be selected.

[0125] In this scenario, server 100 can input the motion characteristic information of multiple sports stored in server 100 memory 110 and the first student's motion tendency into the first artificial intelligence model, and generate motion plan information containing the sports with the highest similarity (recommended sports) based on the output data.

[0126] Here, the baseline value can be set based on the trainee's sports history information.

[0127] Specifically, when setting a benchmark value and comparing it with the first student's performance, the server 100 uses the first student's sports history information as a basis to calculate the student's execution time for the target sports event. The longer the calculation time, the smaller the benchmark value.

[0128] In this way, server 100 can set a baseline value to reflect the longer the time spent practicing a sport, the more limited the potential range of improvement that sport has reached above a certain level.

[0129] At this point, server 100 can set the exercise frequency based on the similarity between the first student's exercise preferences and the recommended exercise programs, and generate exercise plan information.

[0130] For example, server 100 can be set to have a higher frequency of movement as the similarity increases.

[0131] In this way, server 100 can provide exercise plan information so that the more suitable the recommended sports are for the first student's exercise preferences (the higher the similarity), the more frequently they can exercise, thus helping them with fitness.

[0132] Conversely, if the similarity between the target sport and the first student's sporting tendency exceeds a threshold, the server 100 can select a second mode to generate exercise plan information that includes the frequency of the target sport.

[0133] Specifically, server 100 can identify the frequency of the target sport from the first student's sports history information and generate a sports frequency value containing sports plan information that is greater than the identified sports frequency value.

[0134] In this way, server 100 can provide exercise plan information to improve the physical strength and athletic ability of trainees who are growing slowly due to insufficient exercise sessions.

[0135] At this time, server 100 can provide exercise plan information to the first student's student terminal 200-1.

[0136] In addition, server 100 can receive post-lecture information about the first lecturer from the first student's student terminal 200-1.

[0137] In this scenario, after the first lecturer in the first group has finished their instruction, server 100 can act as the lecturer terminal 300-1 of the first group of lecturers to provide the follow-up information received from the student terminals of the first group of students.

[0138] Figure 6 This is a block diagram of the server configuration based on the daily implementation method of this city.

[0139] like Figure 6 As shown, server 100 may include memory 110, processor 120 and communication interface 130.

[0140] Memory 110 is a configuration used to store at least one instance or data related to the operating system (OS) and server 100 components to control the overall behavior of the server 100 components.

[0141] The memory 110 may include non-volatile memory such as ROM and flash memory, or volatile memory such as DRAM. In addition, the memory 110 may also include hard disks, solid-state drives (SSDs), etc.

[0142] Processor 120 is configured to provide full control over server 100.

[0143] As one embodiment, the processor 120 may include general-purpose processors such as CPUs (Central Processing Units), APs (Action Processors), and DSPs (Digital Signal Processors), graphics-specific processors such as GPUs (Graphics Processing Units) and VPUs (Vision Processing Units), or artificial intelligence-specific processors such as NPUs (Neural Processing Units). Artificial intelligence-specific processors can be designed with hardware structures specifically for training or using particular artificial intelligence models.

[0144] Communication interface 130 is configured for communicating with the outside world.

[0145] The communication interface 130 may include circuits, modules, chips, etc., for communication in various wired and wireless communication methods. The communication interface 130 can also connect to external devices and servers through various networks.

[0146] A network can be a Personal Area Network (PAN), a Local Area Network (LAN), a Wide Area Network (WAN), etc. Depending on the openness of the network, it can be an Intranet, an Extranet, or the Internet.

[0147] The communication interface 130 can communicate with and connect to external devices through various methods such as LTE (long-term evolution), LTE-A (LTE Advance), 5G (5th Generation) mobile communication, CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile communiversal mobile communiversal mobile system), WiBro (Wireless Broadband), GSM (Global System for Mobile Communications), DMA (Time Division Multiple Access), WiFi (Wi-Fi), Bluetooth, BLE (Low), NFC (Near Field Communication), and Zigb.

[0148] In addition, the communication interface 130 can also connect to external devices and servers via wired communication methods such as Ethernet, optical network, USB (Universal Serial Bus), and Thunderbolt.

[0149] Furthermore, the communication interface 130 may be configured to utilize various communication methods / technologies newly designed in the future.

[0150] Furthermore, the various implementation examples described above can be implemented within a recording medium readable by a computer or similar device by using software, hardware, or a combination thereof.

[0151] Depending on the hardware implementation, the implementation examples described in this guide can be implemented using at least one of ASICs (application-specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processors), PLDs (programmable logic devices), FPGAs (field gate arrays), processors, controllers, micro-controllers, and electrically programmable units that perform other functions.

[0152] In some cases, the implementation examples described in this listing may be implemented by the processor itself. Depending on the software implementation, the implementation examples (such as the steps and functions described in this listing) may be implemented by separate software modules. Each of these software modules may perform one or more functions and operations described in this specification.

[0153] Furthermore, according to various embodiments of this guide, computer instructions for performing processing operations such as those of electronic devices can be stored in non-transitory computer-readable media. When executed by a processor of a particular machine, the computer instructions stored in these non-transitory computer-readable media perform the processing actions of the various embodiments described above.

[0154] Non-transitory computer-readable media refers to media that store data for a short period of time, such as registers, caches, and memory, rather than storing data semi-permanently, and that can be read by a device. Specific examples of non-transitory computer-readable media may include CDs, DVDs, hard drives, Blu-ray discs, USB drives, memory cards, and ROMs.

[0155] The preferred embodiments of this launch have been described and illustrated above, but this launch is not limited to the specific embodiments described. Various variations may be implemented by those skilled in the art within the scope of this launch without departing from the spirit of this launch requested, and these variations should not be individually understood from the technical ideas and vision of this launch.

Claims

1. A method for operating a server, used for monitoring and mutual evaluation based on motion matching, wherein, The operation method of the server includes the following steps: The initial information about the target sport of the first student is obtained through the student terminal of the first student; Based on the initial information, at least one group shall be created for the coaches of multiple students, including the first student. Based on user input received by the instructor team matching the first group of instructors including the first student, generate the first student's exercise history information and exercise assessment information; and If a second group containing the first student is created, the instructor terminal, acting as the second instructor matched with the second group, provides the first student's sports history information and sports evaluation information.

2. The server operation method according to claim 1, wherein, The server operates by providing motion assessment information of the first student to the individual student terminals of the multiple students included in the first group.

3. The server operation method according to claim 2, wherein, The server operates by obtaining information about the conditions for the public assessment through user input received from the first student's student terminal. In the step of providing the first student's exercise assessment information, If the conditions are met, the exercise assessment information of the first student is provided through the individual student terminals of the multiple students included in the first group.

4. The server operation method according to claim 1, wherein, In the step of obtaining the initial information of the first student, initial information about the target sport is acquired, which includes at least one of the first student's athletic tendency, physical fitness level, athletic ability, and target. In the step of creating at least one group for the coaches of the multiple students, Among the trainees who input the initial information for the target sport, at least one group should be created, comprising multiple trainees whose initial information has a similarity exceeding a threshold. Based on the initial information of each of the multiple students included in the generated group, representative information of the generated group is generated. Based on the representative information, at least one lecturer related to the target sport is selected and matched with the generated group.

5. The server operation method according to claim 1, wherein, In the steps of generating the first student's sports history information and sports evaluation information After receiving the exercise record data related to the target sport performed by the first student from the first lecturer's team of lecturers, the exercise record data is added to the first student's exercise resume information. Based on the evaluation information of the first instructor to the first student, sports evaluation information containing the first student's ability values ​​for each item of the target sports project is generated.

6. The server operation method according to claim 5, wherein, The server operates by including a phase of obtaining personal information from the first student's client, which includes at least one of the first student's gender, health status, and age. In the steps of generating the first student's sports history information and sports evaluation information Based on the sports history information, the frequency of the first student's performance of the target sport is identified. The evaluation information of the first lecturer is corrected based on the exercise frequency, gender, health status and age to generate the exercise evaluation information.

7. The server operation method according to claim 5, wherein, The server operates by including the following steps: Based on the sports evaluation information, individual weights related to the sports event are applied to the various ability values ​​of the first trainee. The individual ability values ​​applied to these weights are then calculated based on the sports events to obtain a sports event score. The exercise plan information containing the exercise behavior with the lowest calculated score is generated and provided as the student terminal of the first student.

8. The method of operating the server according to claim 5, wherein, The server operates by including the following steps: Identify the sport in which the first trainee has the highest ability score from the sports evaluation information; For the sport in which the first student has the highest ability score, identify at least one matching candidate sport. Based on the initial information similarity of the first student's information on the respective sports characteristics of the candidate sports, at least one of the candidate sports is selected; and Generate exercise plan information containing the selected candidate sports and provide it to the student terminal of the first student.

9. The server operation method according to claim 1, wherein, The server operates by including the following steps: Receive subsequent information about the first lecturer from the first student's student terminal; and After the first group of instructors finishes their guidance, the post-instruction information is provided to the instructor terminals of the first group of instructors.

Images