TW201405328A - Method and system of selecting benchmark via social network service for activity coaching - Google Patents

Method and system of selecting benchmark via social network service for activity coaching Download PDF

Info

Publication number
TW201405328A
TW201405328A TW101127574A TW101127574A TW201405328A TW 201405328 A TW201405328 A TW 201405328A TW 101127574 A TW101127574 A TW 101127574A TW 101127574 A TW101127574 A TW 101127574A TW 201405328 A TW201405328 A TW 201405328A
Authority
TW
Taiwan
Prior art keywords
user
stage
step
similarity
activity
Prior art date
Application number
TW101127574A
Other languages
Chinese (zh)
Inventor
Raymund Jun-Rui Lin
Gilbert Bo-Quan Liao
Kai-Quang Zhang
Sreeram Ramakrishnan
Original Assignee
Ibm
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ibm filed Critical Ibm
Priority to TW101127574A priority Critical patent/TW201405328A/en
Priority claimed from US13/955,067 external-priority patent/US20140038146A1/en
Publication of TW201405328A publication Critical patent/TW201405328A/en

Links

Abstract

A method and a system of selecting benchmark via social network for activity coaching are provided. The method includes the following steps: establishing a stage model according to at least a performing parameter of an activity, wherein the stage model comprises a plurality of stages, and a value corresponding to the performing parameter designated for a stage is smaller than the value for a higher stage; receiving activity data for N rounds from a first user of a social network service, determining the stage where the first user is staying according to the activity data for each round and the value corresponding to the parameter designated for the stage, and thus obtaining a N-round behavior change path of the first user in the stage model; receiving activity data for M rounds from a second user of the social network service, determining the stage where the second user is staying according to the activity data for each round and the value corresponding to the parameter designated for the stage, and thus obtaining a M-round behavior change path of the second user in the stage model; determining a path similarity between the M-round behavior change path of the second user and the first M-round behavior change path in the N-round behavior change path of the first user; if the path similarity meets a predetermined condition, communicating to the second user via the social network service the value corresponding to the parameter designated for the stage where the first user stayed for the (M+1)th round in the N-round behavior change path.

Description

Method and system for selecting a benchmark through social networking services for activity teaching

The present invention relates to a method and system for activity coaching through a social networking service selection benchmark.

In the case of medical care, doctors in addition to prescribing drugs, medical care instructions will also recommend patients to carry out designated health activities, such as adjustments to diet, exercise, or lifestyle. In some cases, such as obesity or diabetes (Diabetes), these home health activities tend to improve the condition more than taking medication.

In this regard, U.S. Patent No. 7,287,031 and U.S. Patent No. 7,821,404, both of which are incorporated herein, are incorporated herein by reference.

In addition, for the above-mentioned home health activities, the characteristics are that they need to be carried out in a step-by-step manner, and continue to be carried out, and finally become part of living habits, in order to improve the health or the condition. For example, even if the doctor thinks that the condition of the patient (such as obesity) needs to reach the amount of exercise running 3 kilometers per day to improve his condition, let a patient who has barely exercised immediately start running every day, which is actually It is unlikely, and I am afraid it will cause harm to the patient.

Simple and one-time action compared to pill taking Action), health care activities need to be repeatedly implemented and developed step by step into life style, so the compliance requirements are more difficult, and patients need to pay more psychologically and physiologically. The efforts will not be abandoned halfway. In view of this, one aspect of the present invention is to propose a method and system for activity teaching to assist in the smooth development of patient health care activities.

Existing private human-based activity coaches are time consuming, costly, and incapable of focusing on too many subjects. In contrast, one aspect of the present invention is directed to an automated method and system for activity teaching. On the other hand, the present invention proposes a method and system for teaching activities through social networking services, that is, through the power of the community, to achieve the function of activity teaching.

In addition to encouraging or urging patients to carry out activities, one of the activities of the activity is to recommend appropriate activities based on the measurement of individual patients. Take exercise as an example, you can recommend that the patient jog or swim. Another important point of activity teaching is to assist individual patients to set staged activities. For example, jogging 3 kilometers a day this week and jogging 5 kilometers a day next week can be used step by step. In view of this, another aspect of the present invention is to provide a user with a suitable phased goal in an automated manner.

In this article, "social network services" are built on "social network sites" where social network members can set up relationships and conduct other activities, such as posting opinions, games, or co-creation. Wait. For more details on the "social web services" in this article, you can refer to the Wikipedia web page at http://en.wikipedia.org/wiki/Social_networking_service for instructions.

In addition, the "social network service" herein preferably has a reward mechanism to provide rewards to the user according to the conditions, and the reward may be a virtual or honorary reward, such as providing a virtual badge, good rating (better) Rating), bonus point, or the "like" provided by Facebook's website service, without necessarily corresponding to a substantial reward, but the reward should be recorded on the social network serving. Examples of additional rewards can be found in Gash (Game Cash) by Gamania Digital Entertainment Co. or Gash+ by GASH PLUS (TAIWAN) Company Limited.

In addition, the "activities" in this article are mostly described by taking exercise as an example, but those skilled in the art should understand that the present invention is not limited thereto, and can be applied to general educational purposes such as reading or other extracurricular activities, for example. As long as the "activity" can be designed to have at least one "execution parameter" that can be quantified, such as the number of times the "activity" is executed each week, or the time each time the activity is executed. It should also be noted that in this context, “execution parameters” are related to the execution status of the activity, not to the content of the activity itself. In other words, different activities, such as motion and reading, may have the same execution parameters (eg each Perform three times a week or 100 minutes a week).

Relative to "execution parameters", the "level" in this article is related to the content of the activity itself. For example, sports can be divided into lightweight walks and heavyweight rock climbing, or reading can be divided into lightweight readings. Newspaper and heavyweight literary classics.

In accordance with an embodiment of the present invention, a method and system for selecting a benchmark through a social networking service for activity teaching. The method comprises: ● setting a one-stage model according to at least one execution parameter of an activity, wherein the phase model includes at least a plurality of phases, wherein a phase corresponds to at least one execution parameter having a value less than a higher phase corresponding to the at least one The value of the execution parameter; ● receiving the activity data input by a first user N on a social network service, N is an integer greater than 1, and according to the activity input by the first user each time And the data and the value of the at least one execution parameter corresponding to the plurality of stages, determining a stage to which the first user belongs in the return, and thereby obtaining a track of the N-time behavior change of the first user in the stage model (behavior change path); ● receiving the activity data input by the second user on the social network service, M is an integer greater than 1, and M is less than N, and according to each second The activity data input by the user and the value of the at least one execution parameter corresponding to the plurality of stages determine the stage to which the second user belongs, and thereby obtain the model of the second user at the stage One of the M back behavior change trajectories; ● determining the trajectory similarity between the change track of the first M back behavior in the change track of the N user behavior and the change track of the M back behavior of the second user; and ● if If the trajectory similarity meets a predetermined condition, the value of the at least one execution parameter corresponding to the phase of the M+1 back in the trajectory of the N-time behavior change of the first user is prompted by the social network. Two users.

In accordance with another embodiment of the present invention, a computer program product stored on a computer usable medium is provided that includes a computer readable program for execution on a computer system to perform the method as described above.

According to another embodiment of the present invention, a computer system is provided, comprising: a memory and a processing unit, the memory storing a set of computer executable instructions, and the processing unit executing the set of computer executable instructions to perform the above The method.

The features, advantages, and similar expressions of the present invention are not to be construed as being limited by the scope of the invention. Rather, the specific features, advantages, or characteristics described in connection with the specific embodiments are included in at least one embodiment of the invention. Therefore, the description of features and advantages, and similar expressions in this specification are related to the same specific embodiments, but are not essential.

These features and advantages of the present invention will become more apparent from the description of the appended claims appended claims.

The reference to "a" or "an" or "an" or "an" or "an" Therefore, the appearances of the phrase "in a particular embodiment"

It will be apparent to those skilled in the art that the present invention can be implemented as a computer system, method, or computer readable medium as a computer program product. Therefore, the present invention can be implemented in various forms, such as a complete hardware embodiment, a complete software embodiment (including firmware, resident software, microcode, etc.), or can also be implemented as a software and hardware implementation. In the following, it will be referred to as "circuit", "module" or "system". In addition, the present invention can also be implemented as a computer program product in any tangible media form, with computer usable code stored thereon.

A combination of one or more computer usable or readable media can be utilized. For example, a computer usable or readable medium can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or communication medium. More specific computer readable media embodiments may include the following (non-limiting illustrations): electrical connections consisting of one or more connecting lines, portable computer magnetic disk, hard disk drive, random access Memory (RAM), read-only memory (ROM), erasable stylized read-only memory (EPROM or flash memory), optical fiber, portable optical disc (CD-ROM), optical storage device, transmission Media (such as the Internet or the internal connection of the intranet), or magnetic storage devices. It should be noted that the computer usable or readable medium may be paper or any suitable medium that can be used to print the program thereon so that the program can be re-electronicized again, for example by optically scanning the paper or other The media is then compiled, interpreted, or otherwise processed as necessary and then stored in computer memory. In this context, a computer usable or readable medium can be any medium for holding, storing, transmitting, transmitting, or transmitting a code for processing by an instruction execution system, apparatus, or device. Computer usable media can include storage There is a data transmission signal that can be used by the computer, whether it is a baseband or a partial carrier type. The computer can use the code to transmit any aptamable media, including but not limited to wireless, wireline, fiber optic cable, radio frequency (RF), and the like.

Computer code for performing the operations of the present invention can be written using a combination of one or more programming languages, including object oriented programming languages (eg, Java, Smalltalk, C++, or the like) and traditional programming languages (eg, C programming languages or Other similar programming languages).

The following description of the present invention will be described with reference to the flowchart and/or block diagram of the systems, devices, methods and computer program products according to the embodiments of the invention. Each block of the flowchart and/or block diagram, as well as any combination of blocks in the flowcharts and/or block diagrams, can be implemented using computer program instructions. These computer program instructions can be executed by a general purpose computer or a special computer processor or other programmable data processing device, and the instructions are processed by a computer or other programmable data processing device to implement a flowchart and/or The function or operation described in the block diagram.

The computer program instructions can also be stored on a computer readable medium to instruct a computer or other programmable data processing device to perform a particular function, and the instructions stored on the computer readable medium constitute a finished product. The instructions contained therein may implement the functions or operations illustrated in the flowcharts and/or block diagrams.

Computer program instructions may also be loaded onto a computer or other programmable data processing device for performing a system operation on a computer or other programmable device, and executing the command on the computer or other programmable device A computer implementation program is generated to achieve the functions or operations illustrated in the flowcharts and/or block diagrams.

2, FIG. 4 is a flow chart and block diagram showing the architecture, functions, and operations of the apparatus, method, and computer program product according to various embodiments of the present invention. Thus, each block of the flowchart or block diagram can represent a module, a segment, or a portion of a code that includes one or more executable instructions to implement the specified logical function. It is to be noted that in some other embodiments, the functions described in the blocks may not be performed in the order shown. For example, the blocks in which the two figures are connected may in fact be executed simultaneously, or in some cases, in the reverse order of the drawings. It should also be noted that each block diagram and/or block of the flowcharts, and combinations of blocks in the block diagrams and/or flowcharts may be implemented by a system based on a special purpose hardware, or by a special purpose. A combination of body and computer instructions to perform a specific function or operation.

1 is a schematic diagram of a system architecture 100 in accordance with a specific embodiment of the present invention, so that those skilled in the art can more clearly understand the present invention and are not intended to limit the scope of the present invention. In an exemplary system 100 embodiment, a server host 110, one or more client devices 120, 130, 140, and 150 (eg, a personal computer, a notebook computer, a mobile electronic device, or a smart television) are included. ). The server host 110 includes a processing unit PU and a database DB, which can provide social network services and perform the methods as shown in FIGS. 3 to 4. The customer The end devices 120, 130, 140 and 150 are respectively connected to the server host 110 via a network (not shown).

In the present invention, the client devices 120, 130, 140, and 150 may include an electronic device that can execute an application to provide instant messaging, email, newsletter, or other application capable of transmitting information, including a desktop computer and a notebook. Computers, terminal equipment, mobile phones, personal digital assistants, etc. In an exemplary embodiment, the client device is a general-purpose desktop computer having a processor to execute various applications; a storage device for storing various information and programs; a display device, communication, and output/ The device is used as an interface to communicate with the user; as well as peripheral components or other specific use components. In other embodiments, the invention may be embodied in other forms with more or fewer other devices or components.

Similarly, the server host 110 can use a general-purpose computer, a special application computer, a high-end workstation, a mainframe, etc., such as IBM's System X, Blade Center, or eServer server. The network can also be implemented as any type of connection, including a fixed-connection local area network (LAN) or wide area network (WAN) connection, or using an Internet service provider to temporarily dial into the Internet, or Limited to various connection methods such as wired and wireless. In addition, it should be understood, however, that other hardware and software components (such as additional computer systems, routers, firewalls, etc.) may be included in the network, although not shown.

Each application (such as an instant message, email, newsletter, etc.) executing the information on the client device has an information plug-in and can interact with the server host 110.

It must be noted here that many of the functional units described in this specification or the drawings are labeled as functional blocks or modules to more specifically emphasize their implementation independence. For example, a functional block or module can be implemented as a hardware circuit that includes a custom VLSI circuit or gate array, an off-the-shelf semiconductor such as a logic die, a transistor, or other discrete components. Modules may also be implemented in a programmable hardware device, such as a field programmable gate array, programmable array logic, programmable logic devices, or the like. Modules can also be implemented in software that is executed using various types of processors. For example, the identification module of the executable code includes one or more entities or logical blocks of computer instructions that, for example, can be organized into objects, programs, or functions. However, the executable modules of the recognition module are not necessarily physically located together, but may contain different instructions stored in different locations that, when combined together, will contain the modules and achieve the specified purpose of the modules.

The executable code module can be a single instruction or many instructions, and can be distributed on several different code segments, in different programs, and on several memory devices. Similarly, operational data may be identified and illustrated herein within a module and may be embodied and organized in any suitable form within any suitable type of data structure. The operational data may be collected as a single data set, or the operational data may be distributed at different locations (including distributed among different storage devices), and the operational data may exist at least locally as an electronic signal.

The present invention will be further described by way of a plurality of simplified embodiments, particularly in the context of health care activities, but as described above, it should be understood by those skilled in the art that the present invention is not limited thereto.

<stage model>

The “stage model” in this paper is designed for the purpose of gradual progress, in which different stages can be used as the stage targets of the patient or the instructor of the activity. It should be noted that the stage model in the present invention can be designed in different stages according to at least one "quantitative execution parameter" of the activity. In other words, each stage can correspond to a different value of the execution parameters, but one stage can simultaneously correspond to the values of two or more execution parameters. Preferably, for a particular execution parameter, the low to high phase corresponds to a low to high value for this particular execution parameter. In addition, the number of stages may also vary depending on actual needs, and the invention is not intended to be limited.

To illustrate the present invention, a one-stage model is presented as an example, wherein the stage model refers to two activity execution parameters, one for frequency S and the other for duration D, where frequency S is defined as the number of times the activity is performed each week, and The duration D is defined as the total number of weeks that are performed a certain number of times (for example, 2 times) per week. This stage model is illustrated in Table 1 below.

2 additionally shows that the frequency S is the X-axis and the duration D is the Y-axis, and the phase model of Table 1 is expressed as a two-dimensional coordinate. Thus, those skilled in the art will appreciate that different stage models can be designed even with the same two execution parameters (i.e., frequency S and duration D).

<First embodiment: in units of teams>

3 is a flow chart of an exemplary embodiment of the present invention, which is a computer implementation method in conjunction with the activity of the system 100 shown in FIG. 1, in particular, two or more users are grouped into a team for activities. Teaching, in which "activity" is described by taking fitness exercises as an example.

First, FIG. 1 only shows a server host 110 for providing social network services and activity teaching, but in reality, the number of server hosts 110 is not limited, and the service shared by each server host 110 is also Not necessarily the same. In other words, there may be some server hosts 110 that are primarily responsible for social networking services, while other server hosts 110 are primarily responsible for activity teaching, and each other can exchange the required data through any of the aforementioned types of networks.

• Step 300: This step is an initial preparation to gather basic information about the user, especially with respect to specific activities. In this step, the server host 110 invites the user to report or post through the client devices 120, 130, 140, and 150 for a specific period of time (by the server host) using the social network service maintained by the server host 110. 110's own life history, especially about the specific activities (ie activity history), and stored in the database DB.

In the case of sports, the user can report the name and weight of the exercise (for example, jogging 3 km or not) when it is (for example, on a certain day of the month or on a periodic day or weekday morning or afternoon or evening). Swimming for 1 hour), or the amount of calories burned. In addition, the information provided by the user does not have to be provided through a specific format or a natural language, but the server host 110 also has the ability to handle natural language.

Step 302: The server host 110 sets the stage model as shown in Table 1 or Figure 2 above and stores it in the database DB. It should be noted that the stage model shown in Table 1 or Figure 2 can also be applied to activities other than "sports".

Step 304: Since each user's situation is different, the purpose of this step is to determine the starting point of each user on the phase model (Table 1 or Figure 2) as the basis for the subsequent detachment. In this step, according to the information of the time and the amount of the movement history provided by each user received in step 300, the frequency S of the exercise and the duration D are determined, and thereby the user is assigned to step 302. The corresponding phase (stage 1-8) of the set phase model.

It should be understood by those skilled in the art that the above is a simplified example, but in other embodiments not shown, the activity history provided by the user may not directly correspond to the parameters of the phase model (for example, frequency S and duration D). The server host 110 may need to refer to a preset correspondence table or algorithm (also stored in the database DB) to assign the user to the stage mode. The corresponding stage on the type. In other words, through this step, users who exhibit similar frequencies S and duration D in the motion history (step 300) are assigned to the same stage.

In addition, for subsequent steps, consider adjusting the number of users at each stage. For example, if the user in the pre-adjustment stage 3 has only 1 bit and is not sufficient for the subsequent steps, it can be adjusted down to the stage 2, that is, the subsequent steps are performed together with the user of the stage 2.

• Step 306: The purpose of this step is to group two or more users assigned to the same stage (for example, stage 2) to conduct a group activity. There are many research discussions in the academic literature on the advantages offered by the team rather than the individual into the activity teaching, and will not be repeated here. In this step, the number of people in each team is preferably the same (for example, three people), but it may be different, and the present invention is not intended to be limited.

As for the way of detachment, all users in the same stage can be randomly squad, but preferably, they can be detached according to the personal information previously provided by the user to the social network service, such as age, gender, and education level. It is hoped that users of the same team will be similar in some aspects to facilitate communication and encourage each other.

In addition, the squad may be further divided according to the motion history provided by each user (step 300). It should be noted that, as described in step 304, users with similar motion history will be assigned to the same stage. In this step 306, users with more similar sports histories in the same stage will be assigned to the same team.

In another embodiment, in addition to personal information and sports history, the user may be assigned according to the rating given by the user in the same stage (for example, stage 2), or may be based on the user in the social network. The relationships (such as colleagues, friends, family members, etc.) on the service are squad, and these relationships can be preset by the server host 110, or can be specified by the user and named by themselves. For reference, the Google Plus Circle can be referred to.

From another perspective, the rating or relationship is also used to determine the similarity between the two users. The higher the sum of the ratings given to each other, the more similar the two users can be; the different relationships can also represent different similarities. Therefore, the rating or relationship can also be incorporated into the above considerations or algorithms that use personal information and athletic history to determine similarity between users.

Regarding the similarity algorithms for judging the similarity between two users and the clustering algorithms for grouping according to the similarity, the present invention is not intended to be limited, and this part should be familiar to the skilled person. It is known, so it will not be repeated here.

However, it should be noted that if an algorithm is used, the aforementioned personal information, sports history, ratings, and relationships need to be represented by numerical values in order to be imported into an automated algorithm.

In addition, since one of the purposes of the detachment is to make the team members easy to communicate and encourage each other, the number of players in each team should not be too large. It is better to set an upper limit (for example, 4 people), and this upper limit can also be introduced to the above. In the clustering algorithm.

Step 308: For the user of the same team formed in the same stage (for example, phase 2) in step 306, the server host 110 maintains the social network service (via via site information or email or other communication means). Send an invitation to participate in group games. In the invitation of this group game, the value of the execution parameter corresponding to the next stage (stage 3) is first prompted (ie, frequency S=3; duration D=1 (3 times a week; lasts for 1 week)) as a group game The team goal, in addition, can prompt the player list and the rules of the group game, for example, the team goal can be regarded as the achievement only if the frequency of all team members' execution results meets the team's target frequency S=3 and the duration D=1. It’s just a group game. In this way, players can encourage and supervise each other, thereby generating more motivation to achieve the goals of the event. In addition, during the invitation, a user can specify a game period, and the user must reply to the execution result during the game. It should be noted that the rules of the above group game are merely examples, and the present invention is not limited thereto.

Step 310: After the specified game period elapses, the server host 110 counts the execution results of the user replying through the client devices 120, 130, 140, and 150. As for the manner in which the user replies to the execution result, the user may return the exercise history in step 300, in other words, the steps 300 and 310 may be different at the user end, but the present invention Not wanting to be limited to this.

Similarly, the motion history reported by the user in step 310 may not directly correspond to the frequency S and the duration D, and the server host 110 may need to calculate the corresponding frequency S and continue by referring to the preset correspondence table or algorithm. Period D, and thereby determine whether the team goal is achieved, that is, whether all team members' execution results meet the required frequency of the team goal S=3 and the duration D=1.

• Step 312: If the decision of step 310 is yes, then all the team members of the team are upgraded from the original phase 2 on the phase model to phase 3, and provided according to a predetermined rewards scheme on the social network service. Rewarding the user in the same team, then the method can return to step 306 and repeat steps 306 to 312, so the user who is promoted from stage 2 to stage 3 can be detached with other users of stage 3, and Stage 4 advances.

On the other hand, if the decision of step 310 is no, all the team members of the team are retained in the original phase 2 on the phase model (step 311), and then the method also returns to step 306, with other users in phase 2 The detachment is performed, the team challenge phase 3 is again teamed, and steps 306 to 312 are repeated.

<Variable embodiment: activity level>

In the following, a variation based on the above-described first embodiment will be proposed in conjunction with FIG. 3, mainly considering the level of activity. The description of the first embodiment described above can be applied except for the parts described in the following steps.

Step 352: This step is performed after step 300, and is similar to step 302 above. The difference is that in step 352, the server host 110 further sets the level of activity and stores it in the repository DB. As mentioned before, the "level" here is related to the content of the activity itself (and independent of the execution parameters). For example, the motion can be divided into three levels according to the amount of heat consumed: lightweight, medium-level And the heavyweight; or according to the difficulty of the skill, the three levels are: basic level, advanced class, and professional level. The basis and number at this level may be adjusted as appropriate, and the invention is not intended to be limited.

Step 354: This step is performed after step 352, and is similar to step 304 above. In order to further consider the different conditions of each user, step 354 determines the level to which each user belongs in the same stage according to the information in the motion history (for example, the heat consumed) provided by each user received in step 300 ( For example, lightweight, medium, or heavyweight). In other words, through this step 354, users who have been assigned to the same stage and who have similarly consumed calories in the motion history are assigned to the same level. It should be understood by those skilled in the art that the above is an example of simplification, but in other embodiments not shown, the activity history provided by the user may not directly correspond to the level distinction (eg, heat consumption), and the server host 110 may need to refer to a preset correspondence table or algorithm to determine the level to which each user belongs at the same stage.

The same level of use is assigned to the same stage through step 354. The users who are assigned to the same stage only through step 304 are more similar to each other, and help each other to communicate and encourage each other.

Step 356: This step is performed after step 354, and is similar to step 306 above. The difference is that step 356 further groups users who are assigned to the same stage (e.g., stage 3) and the same level (e.g., middle level) to conduct an activity teaching.

Step 358: This step is performed after step 356, and is similar to step 308 above. The difference is that step 358 is in the invitation of the team game, further adding the level of activity as part of the team goal of the team game. In other words, in order to achieve the team's goal, the level of activity that the player is going to perform must match the level set in the team's goal. Preferably, the level set in the team goal may be consistent with the level (e.g., middle level) that the team member of the team previously determined in step 354. However, it is also possible to adjust the level of the team's target (for example, to adjust to heat and to avoid heat stroke in response to heat waves). Step 358 may proceed to step 310 and step 312, then the method may return to step 356 and steps 356 through 312 are repeated.

<Second Embodiment: Behavior Change Trajectory>

4 is a flow diagram of another exemplary embodiment of the present invention, in conjunction with a computer implemented method of activity teaching by the system 100 shown in FIG. 1, particularly based on a behavioral change trajectory of a reference user on a stage model. Activity teaching. Preferably, but not limited to, the method illustrated in FIG. 4 is further implemented based on the method illustrated in FIG. 3, and in particular, the same stage model can be used as the method illustrated in FIG. 3 (as shown in Table 1).

"Behavior change track": Before explaining Figure 4, first use Figure 3 as an example to describe the "behavior change track". In FIG. 3, step 304, step 311, and step 312 are based on the activity data provided by the user (eg, the activity history of step 300 and the execution result of step 310) to determine a user in the stage model ( Referring to the stage on Table 1 or Figure 2), therefore, through step 304, step 311, and step 312, the same user changes the phase on the phase model to form a behavior change trajectory (as exemplified in Table 2 below), wherein Step 311 and step 312 can be repeated and the behavior change trajectory also correspondingly extends.

For purposes of illustration, each time the user provides activity data to the server host 110 (eg, one of the activity histories of step 300 or the results of step 310), the server host 110 determines the user's own The stage (for example, through one of step 304, step 311, or step 312), this process is referred to as a round. Each time a course is formed, a behavior change track is formed. For example, if the server host 110 performs a step 304, a second step 311, and a step 312 in sequence for a user to determine the stage to which the user belongs, the user's four behaviors are formed. Change track.

Step 400: In this step, the server host 110 obtains the behavior change track of the user, and establishes a behavior change track according to this, and stores it in the database DB as a follow-up reference. Since the time of participation is not the same as its progress, the length of each user's behavior change track (that is, the number of times) will also be different. Further preferably, but not limited to, Step 400 is implemented in real time, so that the latest behavioral change trajectory for each user can be obtained. An example of a single user's behavior change trajectory (6 times) is shown in Table 2 below.

Step 402: In this step, the server host 110 determines an object to be subjected to the activity teaching (hereinafter referred to as a teaching object), and extracts the latest behavior change track of the teaching object from the database described in step 400 (4 times, The fifth time has not yet been decided), and the following Table 3 is illustrated.

Step 404: In this step, the server host 110 calculates the trajectory similarity with other users of the database described in step 400 according to the latest behavior change trajectory of the teaching object (such as Table 3), in order to find out A reference user that can be used as a reference for teaching.

It should be noted that the purpose of the method embodiment is to assist the teaching object to set the next (ie, the fifth) target. If other users only have 4 (or shorter) behavior change trajectories, then 5 times of reference, so you can shave these users first to increase the efficiency of the calculation.

Further, in the calculation of the trajectory similarity, for the behavior change trajectory of other users (or longer) (refer to Table 2), only the first four parts of the behavior and the four-time behavior change of the teaching object are taken. The trajectory (as in Table 3) is used to calculate the trajectory similarity. See the discussion that follows for more details on trajectory similarity calculations.

Step 406: In the foregoing step 404, a trajectory similarity can be calculated between each other user and the teaching object, and in this step, the server host 110 needs to select a benchmark from all other users (benchmark The user is selected based on the trajectory similarity between the subject and the teaching subject as a reference user. For example, the trajectory similarity can be the highest of all users as a predetermined condition, and the server host 110 can then select the reference user having the closest behavior change trajectory to the teaching object. Further, a range section may be set as the predetermined condition for the trajectory similarity.

Step 408: After selecting the reference user, the server host 110 provides the value of the execution parameter corresponding to the next stage (ie, the fifth time) of the reference user to the teaching through the social network service maintained by the server host 110. The object is the target of the next (ie, the fifth) of the teaching object. For example, suppose the behavior change track of the reference user is as shown in Table 2, and the fifth time is in stage 5, and with reference to the stage model of Table 1, the value of the execution parameter corresponding to stage 5 (ie, frequency S) is known. =5; duration D = 1 (5 times a week; lasts 1 week)), and the server host 110 can provide the value of this execution parameter to the teaching object.

"Track Similarity": As can be seen from the description of FIG. 4, the behavior change track of the reference user is provided to the teaching target as a reference for setting the target, and the trajectory similarity is used to select the reference user. It can be seen that the method of FIG. 4 assumes that if the reference user and the teaching subject have a certain degree of similarity in the course of the past activity development, the reference of the behavioral change trajectory of the reference user as the teaching target as the setting target should be Reasonable and convincing to the subject.

As for the manner in which the trajectory similarity is calculated, the present invention is not intended to be specific, and may be designed or adjusted depending on actual conditions. In one embodiment, the trajectory similarity calculates the similarity of the two data sequences by using the respective phase values of the other users and the teaching objects on the behavior change trajectory as two data sequences. Taking the four traces of behavioral changes in Tables 2 and 3 as an example, the two data sequences are (2, 2, 3, 4) and (1, 2, 3, 4), respectively, and the similarity between the two data sequences is calculated. Can refer to the existing similarity algorithm, not here Narration.

On the other hand, the respective phase values of the behavior change trajectory are actually based on the activity data provided by the user to the server host 110 (for example, the activity history of step 300 and the execution result of step 310), and thus in another embodiment In the process, the trajectory similarity is not directly calculated by using the respective phase values of the behavior change trajectory, but the trajectory similarity is directly calculated by the activity data provided to the server host 110 by each user. If the activity data contains more than two parameters (such as frequency S, duration D, calorie consumption, skill difficulty, etc.), the trajectory similarity between other users and the teaching object can be calculated by, for example, a multidimensional vector space model. In addition, as described in step 306, the ranking or similarity between other users and the teaching object may be imported into the calculation of the trajectory similarity after being digitized.

In another embodiment, the calculation of the trajectory similarity first calculates the similarity of the other users and the teaching objects, and then accumulates and adds up. Taking the four-time behavior change trajectory as an example, the first similarity, the second-order similarity, the third-order similarity, and the fourth-order similarity are respectively calculated, and the trajectory similarity is obtained after the summation. It is worth noting that each time the similarity is calculated by summing up the trajectory similarity, different weights can be given. For example, the similarity of subsequent returns (for example, the third similarity) can be given a similarity to the previous one. A higher weight (for example, the second degree of similarity).

As for the similarity of each time, the activity data provided to the server host 110 by each user can be used for calculation. Similarly, if the activity data If more than two parameters (such as frequency S, duration D, calorie consumption, skill difficulty, etc.) are included, the similarity between other users and the teaching object can be calculated by, for example, a multidimensional vector space model.

It should be noted that the present invention relates to the phased (step 304), the sub-level (step 354), the detachment (steps 306 and 356), and the reference user (step 406) in the above embodiments, all of which involve similarity. Calculation. Through the description of the above embodiments, it should be understood that the above similarity calculation is to find out the peers of the teaching objects on different faces, so the present invention is not intended to be limited as long as it can meet the purpose. A specific similarity algorithm.

The present invention may be embodied in other specific forms without departing from the spirit and scope of the invention. The aspects of the specific embodiments are to be considered as illustrative and not restrictive. Accordingly, the scope of the invention is indicated by the appended claims rather All changes that fall within the meaning and scope of the patent application are deemed to fall within the scope of the patent application.

100‧‧‧ computer system

110‧‧‧Server host

120‧‧‧Client device

130‧‧‧Client device

140‧‧‧Client device

150‧‧‧Client device

PU‧‧‧Processing unit

DB‧‧‧Database

In order to immediately understand the advantages of the present invention, the present invention briefly described above will be described in detail with reference to the specific embodiments illustrated in the accompanying drawings. The invention is described with additional clarity and detail with reference to the accompanying drawings, in which: FIG. A computer system according to a specific embodiment of the present invention; FIG. 2 is a stage model according to a specific embodiment of the present invention; and FIG. 3 is a flow chart of a specific embodiment of the present invention; 4 is a flow chart of another embodiment of the present invention.

Claims (9)

  1. A computer-implemented method for selecting a reference to activity teaching, the method comprising: (a) setting a one-stage model based on at least one execution parameter of an activity, wherein the phase model includes at least a plurality of phases, wherein one phase corresponds to at least one execution The value of the parameter is less than the value of the at least one execution parameter corresponding to a higher stage; (b) receiving the activity data input by a first user N round on a social network service, where N is greater than 1 An integer, and determining, according to the activity data input by the first user and the value of the at least one execution parameter corresponding to the plurality of stages, determining the stage to which the first user belongs, and thereby obtaining the first a user's behavior change path in one of the stages of the model; (c) receiving the activity data input by the second user on the social network service, M is An integer greater than 1, and M is less than N, and determining, according to the activity data input by the second user and the value of the at least one execution parameter corresponding to the plurality of stages, determining the second user to which the second user belongs. stage And obtaining a track of the M-behavior change of the second user in the stage model; (d) determining a change track of the first M-back behavior in the change track of the N-time behavior of the first user and the second user The trajectory similarity of the M-traversal change trajectory; and (e) if the trajectory similarity meets a predetermined condition, the stage of the M+1 return belongs to the N-time behavior change trajectory of the first user The value of the parameter should be executed at least one and prompted to the second user via the social networking service.
  2. The method of claim 1, wherein in step (d), the trajectory similarity is determined according to the activity data input by each of the first user and the second user in each of the M backs.
  3. The method of claim 2, wherein in step (d), the activity data input by each of the first user and the second user in each of the M returns is used to determine the similarity of each time, and then according to the total The similarity of the M back determines the trajectory similarity.
  4. The method of claim 3, wherein in step (d), when determining the similarity of the trajectory, the similarity for the Pth back is given a higher weight than the similarity at the P-1th back. Where P is an integer not greater than M.
  5. The method of claim 1, wherein in step (d), the trajectory similarity is determined according to a rating given by the first user and the second user.
  6. In the method of claim 1, the step (d) determines the trajectory similarity according to the predetermined relationship between the first user and the second user on the social network service.
  7. The method of claim 1, wherein the at least one execution parameter comprises a frequency or an accumulated duration.
  8. A computer program product stored on a computer usable medium, comprising a computer readable program for execution on a computer system to perform the method of any one of claims 1 to 7.
  9. A computer system comprising: a host, the host comprising: a bus system; a memory connected to the bus system, wherein the memory comprises a set of computer executable instructions; and a process connected to the bus system A unit, wherein the processing unit executes the set of computer executable instructions to implement the method of any one of claims 1 to 7.
TW101127574A 2012-07-31 2012-07-31 Method and system of selecting benchmark via social network service for activity coaching TW201405328A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
TW101127574A TW201405328A (en) 2012-07-31 2012-07-31 Method and system of selecting benchmark via social network service for activity coaching

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
TW101127574A TW201405328A (en) 2012-07-31 2012-07-31 Method and system of selecting benchmark via social network service for activity coaching
US13/955,067 US20140038146A1 (en) 2012-07-31 2013-07-31 Method and system of teaming up via social network service for activity coaching

Publications (1)

Publication Number Publication Date
TW201405328A true TW201405328A (en) 2014-02-01

Family

ID=50550018

Family Applications (1)

Application Number Title Priority Date Filing Date
TW101127574A TW201405328A (en) 2012-07-31 2012-07-31 Method and system of selecting benchmark via social network service for activity coaching

Country Status (1)

Country Link
TW (1) TW201405328A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9535579B2 (en) 2012-10-09 2017-01-03 International Business Machines Corporation Keyword-based user interface in electronic device
TWI656504B (en) * 2016-01-14 2019-04-11 香港商阿里巴巴集團服務有限公司 Business processing method and device

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9535579B2 (en) 2012-10-09 2017-01-03 International Business Machines Corporation Keyword-based user interface in electronic device
US9582159B2 (en) 2012-10-09 2017-02-28 International Business Machines Corporation Keyword-based user interface in electronic device
US10365806B2 (en) 2012-10-09 2019-07-30 International Business Machines Corporation Keyword-based user interface in electronic device
TWI656504B (en) * 2016-01-14 2019-04-11 香港商阿里巴巴集團服務有限公司 Business processing method and device

Similar Documents

Publication Publication Date Title
Yang et al. Implementation of behavior change techniques in mobile applications for physical activity
Asimakopoulos et al. Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables
US20180114602A1 (en) Interactive graphical user interfaces for implementing personalized health and wellness programs
Pereira et al. A review of gamification for health-related contexts
US9183262B2 (en) Methodology for building and tagging relevant content
Chen et al. HealthyTogether: exploring social incentives for mobile fitness applications
US20160342756A1 (en) Enhancing diagnosis of disorder through artificial intelligence and mobile health technologies without compromising accuracy
Godino et al. Using social and mobile tools for weight loss in overweight and obese young adults (Project SMART): a 2 year, parallel-group, randomised, controlled trial
Kirwan et al. Using smartphone technology to monitor physical activity in the 10,000 Steps program: a matched case–control trial
Cotter et al. Internet interventions to support lifestyle modification for diabetes management: a systematic review of the evidence
Thompson Worldwide survey of fitness trends for 2019
Allam et al. The effect of social support features and gamification on a Web-based intervention for rheumatoid arthritis patients: randomized controlled trial
Millington Smartphone apps and the mobile privatization of health and fitness
JP6430391B2 (en) Automatic health data collection, processing and communication system
Verhagen et al. A knowledge transfer scheme to bridge the gap between science and practice: an integration of existing research frameworks into a tool for practice
Lister et al. Just a fad? Gamification in health and fitness apps
US9474934B1 (en) Biometric assessment in fitness improvement
Eisenberg et al. Help seeking for mental health on college campuses: Review of evidence and next steps for research and practice
Proffitt et al. Considerations in the efficacy and effectiveness of virtual reality interventions for stroke rehabilitation: moving the field forward
US10265624B2 (en) Access control for electronic entertainment systems
US9460632B2 (en) System and method for rewarding physical activity
Steinberg et al. Daily text messaging for weight control among racial and ethnic minority women: randomized controlled pilot study
Rabin et al. Desired features of smartphone applications promoting physical activity
Brannon et al. A systematic review: is there an app for that? Translational science of pediatric behavior change for physical activity and dietary interventions
US7967731B2 (en) System and method for motivating users to improve their wellness