Systems and methods for database management

Embodiments in the present disclosure may be directed to provide systems and methods for operating database and performing record set specific computing operations. The disclosed method may operate one or more client computing devices, an analytical engine, one or more wearable devices operatively coupled to one or more client computing devices via a communication network, and one or more databases. The disclosed method may include steps performed by a processor for tracking users to create or join groups with similar lifestyles attributes.

TECHNICAL FIELD

Generally, the present disclosure relates databases. More particularly, the present disclosure relates to file management and data structures.

BACKGROUND

A database stores a set of records. However, managing such records may be difficult due to record number, size, content, or relationships. For example, there may be a need to organize records into different sets and use such sets to perform various set-specific computing operations. Thus, a need exists for a technology to organize database records into different sets in order to execute set-specific computing operations.

SUMMARY

One embodiment comprises a method of managing databases, the method comprising: accessing, by an application server, over a wide area network, a plurality of records stored in a first database, wherein the first database is managed by a first database management system, wherein the first database and the first database management system are hosted on a first database server, wherein each of the records comprises a social network service profile, wherein each of the social network service profiles comprises a profile identifier, an electronic messaging address, and a health lifestyle identifier; identifying, by the application server, the health lifestyle identifiers in the records; reading, by the application server, the health lifestyle identifiers from the records; comparing, by the application server, the health lifestyle identifiers; in response to determining, by the application server, that the health lifestyle identifiers are related in content: creating, by the application server, a record in a second database, wherein the second database is managed by a second database management system, wherein the second database and the second database management system are hosted on a second database server, wherein the application server and the second database server are in a local area network, wherein the first database server is external to the local area network; configuring, by the application server, via the second database management system, the record in the second database to be searchable in the second database via the health lifestyle identifiers; copying, by the application server, from the first database, via the first database management system, the profile identifiers and the electronic messaging addresses; populating, by the application server, via the second database management system, the record with the profile identifiers and the electronic messaging addresses based on the copying; generating, by the application server, an electronic message; accessing, by the application server, the electronic messaging addresses in the record of the second database via the second database management system; sending, by the server, the electronic message to the electronic messaging addresses.

Numerous other aspects, features and benefits of the present disclosure may be made apparent from the following detailed description taken together with the drawing figures.

DEFINITIONS

As used here, the following terms may have the following definitions:

“Analytical engine” refers to a software module that handles data integration, breaks data streams into atomic parts, executes rules, and performs data matching by using fuzzy logic, among others.

DETAILED DESCRIPTION

In some embodiments, the impact of wearables is already being felt in education, communication, navigating, and entertainment. However, the greatest potential of wearable devices may lie in healthcare. Wearables are able to track activity, fitness, and lifestyle. Therefore, wearable devices may have the potential to disrupt the health in various ways. Additionally, with the advent of social media, customers can now provide and have access to a variety of information about their friend's lives. The availability of such information can have a positive impact on a person's life if it can be channeled to areas of interest, such as diet, fitness, healthy lifestyle, and the like. Furthermore, the addition of information gathered from wearables can provide a great amount of motivation to people connected through social media websites. Under the right conditions, the information collected from wearable as well as the potential motivation from social media may enable targeted electronic messaging.

A system and method for issuing an electronic document based on collected biometric data are disclosed. According to an embodiment, an analytical engine operates in a system that includes a user interface, external data sources, an internal database, client computing devices, a communication network, wearables, and a communication link. It should be understood that the system can include less components, more components, or different components depending on desired analysis goals.

In an illustrative operation, an analytical engine receives data from external data sources. The analytical engine then processes data received from the external data sources and creates address leads that are stored in an internal database. The analytical engine runs an electronic messaging campaign using the leads previously stored in the internal database. The analytical engine generates a user interface in which a user interacts with system via client computing devices and a communication network. The analytical engine manages accounts created by group of users. The analytical engine receives biometric data from group of users using wearables. The analytical engine processes biometric data of group of users and produces statistics. The analytical engine suggests reward messages for group of users that show healthy lifestyles during a standard period of time.

According to some embodiments, an exemplary illustration of a user interface for a mobile application (Fitness/LifeStyle App) for monitoring the Fitness/LifeStyle of a group of users is disclosed. In these embodiments, the mobile application includes the following exemplary functions: allowing the user to create and manage a user account in the system; allowing the user to see the available workouts; allowing the user to invite and look at friends list; allowing the user to look at friends activity; allowing the user to look at workout history of the user and friends; allowing the user to check rewards for achieving goals; allowing the user to perform searches of people to send friend requests; allowing the user to see the user name of the person connected to the system; allowing the user to receive emails from the an operator of the system; and allowing the user to see connection status.

According to some embodiments, a method for issuing an electronic document to users that volunteered to form groups that use wearables so as to monitor their fitness and lifestyle and report back to an operator of the system is disclosed. In these embodiments, the method includes a plurality of steps performed by a processor. The steps may include: extracting data from external sources; processing and storing data in the internal database; running a an electronic messaging campaign to attract users to join a messaging group; managing groups of registered users; issuing an electronic document to registered users; and sending messages to the messaging groups based on their achievements, as obtained via the wearables.

According to some embodiments, a method for operating a mobile application used for monitoring fitness/lifestyle of one or more users. In these embodiments, the method includes a plurality of steps performed by a processor. The steps may include: downloading and installing Fitness/LifeStyle App on client computing devices; creating accounts for users that volunteer to join a group; inviting friends to create groups of users with similar lifestyles; setting up wearables; monitoring and collecting biometrics of groups of users; and sending biometrics of groups to the server.

One computer-implemented embodiment may include a method that extracts user data associated with users from an external source, the user data including information indicative of lifestyles of the respective users. The user data may be processed to populate a database, and the user data may be stored in the database. The user data stored in the database may be queried for users having attributes indicative of similar lifestyles. A social network user identifier of each user of the set of users having attributes indicative of similar lifestyles may be obtained. An electronic messaging content may be communicated to the social network user identifier of each user in the set of users having attributes indicative of similar lifestyles. A change in risk level for the set of users having attributes indicative of similar lifestyles may be calculated.

One embodiment of a method may include creating accounts for users who are associated with a biometric monitoring program. Users may be enabled to create groups of users associated with the biometric monitoring program, and having similar lifestyles using a downloadable application installed on a computing device of a user. Biometric data being monitored by a wearable device being used by respective users in a group of users having similar lifestyles may be collected. The biometric data collected by respective wearable devices of the users in the group may be processed to determine whether the group qualifies to receive a reward message. A listing of the users in the group and associated biometric data from the users in the group may be transmitted.

One computer-implemented method may include receiving geolocation data associated with users throughout a time period. A determination to identify at least a subset of users with similar risk profiles may be made based on the geolocation data. The user(s) with similar risk profiles may be enabled to form or join groups of users with similar risk profiles via a social network. A record of the group of users with similar risk profiles may be established. Risk for group of users based upon the similar risk profiles at the end of the time period may be calculated. The calculated risk may be utilized for issuing an electronic document, which may comprise a plurality of formatted paragraphs.

A computer-implemented process may include collecting biometric data of at least one biometric parameter and geolocation data over at least one time period from a wearable device. A risk factor score for a user may be generated based on biometric data of the at least one biometric parameter and geolocation data over the at least one time period to be used for issuing an electronic document to the users. An input of answers to a questionnaire form about the users may be received, and a risk level of the users based on the answers and risk factor score may be calculated, where the risk level may be calculated independent of paramed data.

System Components and Architecture

FIG. 1is a block diagram illustrating a system for tracking, motivating, and groups of users to create or join social media groups that are concerned with lifestyle and fitness, and receive periodic reward messages based on the group's behavior.

According to one embodiment, system100includes analytical engine102, user interface104, external sources106, internal database108, client computing devices110, communication network112, wearables114, and link116. It should be understood that system100and analytical engine102can include less components, more components, or different components depending on desired analysis goals.

According to an embodiment, analytical engine102further includes one or more data extraction modules, one or more data processing modules, and one or more APIs. The modules and/or APIs contained/operating within analytical engine102are further described inFIG. 3. Each of the sub-components within analytical engine102may be a set of computer instructions executed by central processing units that run computer executable program instructions or related algorithms. Each central processing unit may be a component of computing devices such as a server, a single computer, or multiple computers in a distributed configuration. In an example, a central processing unit as described inFIG. 2below can be implemented within the aforementioned computing devices.

InFIG. 1, analytical engine102is operatively coupled to and in bi-directional communication with user interface104, external sources106, internal database108, and client computing devices110. User interface104is further operatively coupled to and in bi-directional communication with client computing devices111, and wearables114, and wearables114are wirelessly coupled to and in bi-directional communication with computing devices111via link116. Each of the different components of system100may be implemented in any type of suitable processor-controlled device that receives, processes, and/or transmits digital data, configured as further described below and inFIG. 2. Examples of devices incorporating one or more suitable processor-controlled devices include smartphones, desktop computers, laptop computers, servers, tablets, PDAs, specialized computing platforms biometric data processing, and the like. Examples of devices may include smartphones, desktop computers, laptop computers, tablets, and PDAs, among others. Examples of link116include any short-ranged wireless protocol, such as, for example Bluetooth, BTLE, Bluetooth Smart, Wi-Fi, Zigbee, and the like.

InFIG. 1, analytical engine102may be implemented as software that runs on a server including a processing unit for running related algorithms or computer executable program instructions. Processing unit may include a processor with computer-readable medium, such as a random access memory (RAM) coupled to the processor. Examples of processor may include a microprocessor, application specific integrated circuit (ASIC), and field programmable object array (FPOA), among others. In some embodiments, analytical engine102receives data from external sources106, and internal database108. In these embodiments, analytical engine102processes the received data and stores the processed data at internal database108. In further embodiments, analytical engine102generates user interface104in which a user interacts with system100via client computing devices110. Examples of data received from external sources106include address leads gathered from social networks of users that may exhibit a healthy lifestyle such as, for example, users that workout, users that jog/run every day, users that do yoga, and the like. Examples of data stored in internal database108include processed address leads that are included in a electronic messaging campaign. The electronic messaging campaign offers users that have similar lifestyles a way to create or join groups that can then be monitored using wearables114with biometric sensors, and receive an incentive electronic message. The biometric sensors may be of a wide variety to sense heart rate (e.g., heart rate monitor), sweat, oxygen levels, motion, steps (e.g., pedometer or smartphone with appropriate app), or otherwise. A sensor, such as a Fitbit® or comparable device, may be utilized.

In one or more embodiments, external sources106and internal database108are implemented as relational databases that provide functions of fetching, indexing, and storing data. For example, the relational database can manage records based on primary keys. External sources106and internal database108may be implemented through database management systems (DBMS), such as, MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Microsoft Access, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base, FileMaker Pro, and/or any other type of database that may organize collections of data.

In one or more embodiments, user interface104can be implemented as software that is configured to communicate with a user and that runs on any type of computing device. In these embodiments, user interface104communicates with client computing devices111so as to allow users of system100to manage personal data. Examples of users of system100include authorized personnel, and users that created an account within system100.

In further embodiments, user interface104receives an application from analytical engine102that is displayed on client computing devices110. User interface104grants access to users to perform one or more document generation tasks within system100. For example, the document can include formatted paragraphs. Examples of the tasks include determining risk scores of groups of users based on biometric data received from those groups. In still further embodiments, user interface104receives an application programming interface (API) from analytical engine102that is displayed on client computing devices111. User interface104grants access to users to manage their user accounts within system100. Examples of an application are further described inFIG. 4.

In one or more embodiments, wearables114are clothing and accessories incorporating computer and advanced electronic technologies. In these embodiments, wearables114read biometric data from one or more users, and send biometric data readings from one or more users to an application installed on client computing devices111. Examples of biometric data include number of steps per day, heartbeat rates, levels of sweat, O2 saturation, and the like. Client computing devices111compute and send biometric data via communication network112. Examples of wearables114include smartwatches, trackers, pedometers, activity trackers, and the like. In one embodiment, a client computing device (e.g., smartphone) with the ability to sense and generate biometric data (e.g., number of steps) may be utilized, as well, thereby reducing or eliminating the need for a wearable device.

In exemplary operation, analytical engine102receives data from external sources106. Analytical engine102then processes data received from external sources106and creates leads that it stores in internal database108. Analytical engine102runs an electronic messaging campaign using the address leads previously stored in internal database108. Analytical engine102generates user interface104in which a user and/or users interact with system100via client computing devices110and111via communication network112. Analytical engine102manages accounts created by groups of users. Analytical engine102receives biometric data from members of groups of users using wearables114. Analytical engine102processes biometric data received from the group of users and produces statistics that can be used by a user to generate an electronic document comprising formatted paragraphs to groups of users reporting biometric data to the server. Analytical engine102suggests reward messages for group of users that show healthy lifestyles during a standard period of time. In suggesting reward messages, the analytical engine102may determine how much of one or more biometric parameters (e.g., number of steps, number of workouts per week, etc.) a group of users as a whole (e.g., collective average number of workouts per week by each of the group members) performs.

FIG. 2is a diagram of example components of computing device200or server, according to an exemplary embodiment. According to some aspects of this embodiment, computing device200includes bus202, input device204, output device206, user interface208, main memory210, storage device212, read only memory (ROM214) and processor216. In another exemplary embodiment, server includes additional, fewer, different, or differently arranged components than are illustrated inFIG. 2.

In one embodiment, bus202coordinates the interaction and communication among the components of the server. Input device204includes a mechanism that permits a user to input information to computing device200, such as a keyboard, a mouse, a button, a pen, a touch screen, voice recognition and/or biometric mechanisms, etc. Output device206includes a mechanism that outputs information to the operator, including a display, a light emitting diode (LED), and a speaker, among others.

User interface208enables computer interactions with other devices and/or systems via a network connection. Network connections may refer to any suitable connection between computers such as intranets, local area networks (LAN), cloud networks, virtual private networks (VPN), wireless area networks (WAN), and the internet, among others.

Main memory210includes a random access memory (RAM) or another type of dynamic storage device that may store information and instructions for execution by processor216. ROM214includes a ROM device or another type of static storage device that may store static information and instructions for use by processor216. Processor216includes a microprocessor, an application specific integrated circuit (ASIC), and a field programmable object array (FPOA), among others, that may interpret and execute instructions.

According to some aspects of this embodiment, server, using one or more suitable software modules, enables data fetching, biometrics processing tasks, and predictive analytics. The server performs these operations in response to processor216executing software instructions contained in a computer-readable medium, such as main memory210.

The software instructions reads into main memory210from another computer-readable medium, such as storage device212, or from another device via user interface208. The software instructions contained in main memory210may cause processor216to perform processes that will be described later. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

FIG. 3is a block diagram illustrating a subsystem of the system pertaining to analytical engine102ofFIG. 1. InFIG. 3, subsystem300includes external sources106, internal database108, analytical engine102, data extraction module302, data processing module304, and API306. It should be understood that subsystem300can include less components, more components, or different components depending on the desired analysis goals. In an example, external sources106, internal database108, analytical engine102, data extraction module302, data processing module304, and API306are implemented as external sources, internal database, analytical engine, data extraction module, data processing module, and API described in system100ofFIG. 1.

Analytical engine102is operatively coupled to and in bi-directional communication with data extraction module302, data processing module304, and API306. Analytical engine102is further operatively coupled to and in bi-directional communication with external sources106and internal database108.

In one or more embodiments, data extraction module302within analytical engine102is implemented as one or more computer software modules that include programmatic rules or executing/running different algorithms that allow data fetching and data indexing of potential address leads gathered from social networks. In these embodiments, data extraction module302is configured to fetch and index from social networks data associated with users showing similar lifestyles. This data is indicative of a lifestyle of a user, and can be used to identify potential leads for grouping together users into social networking groups for beneficial results. Examples of potential leads include users that may exhibit a healthy lifestyle such as, for example, users that workout, eat healthy, jog/run every day, do sports, and the like.

In one or more embodiments, data processing module304within analytical engine102is implemented as one or more computer software modules that include programmatic rules or executing/running different algorithms that allow data fetching, data indexing, and data storing of potential leads gathered from data extraction module302. In these embodiments, data processing module304is configured to rank potential leads based on their lifestyle and produce a list of address leads that are stored within internal database108to later be included in future an electronic messaging campaigns. The an electronic messaging campaign offers users having similar lifestyles to create or join groups that can then be monitored using wearables with biometric sensors. These groups may then be eligible or qualify to receive incentive messages as a reward for healthy behavior. Examples of rewards include coupons for workout clothing, etc.

In one or more embodiments, API306within analytical engine102is any Representational State Transfer Application Programming Interface (REST API) that controls and manages one or more APIs. In these embodiments, API306provides web services to one or more mobile applications installed on the client computing devices. Examples of web services include showing data on a website, uploading large amounts of data that will later be consumed by a mobile app, downloading data to run custom analytics, exporting data, and the like.

FIG. 4is an exemplary illustration of a user interface of mobile an application for monitoring fitness/lifestyle of users. InFIG. 4, mobile application400includes profile402, workouts404, friends406, news feed408, statistics410, rewards412, status414, user name416, search418, and email420. It should be understood that mobile application400can include less fields, more fields, or different fields depending on the desired analysis goals.

InFIG. 4, mobile application400is implemented as software that can be downloaded and installed on client computing devices, carried by users, for interacting with a server system. Examples of mobile application400are GUI applications (Fitness/LifeStyle App) that may be available at, downloaded, and installed from a public software app stores or digital application distribution platforms, such as Apple iTunes®, Google Play® Store and Amazon.com®, among others. In these embodiments, mobile application400includes the following exemplary functions: allowing the user to create and manage a user account in the system profile402; allowing the user to see the available workouts404; allowing the user to invite and look at friends list406; allowing the user to look at friends activity news feed408; allowing the user to look at workout history of the user and/or friends statistics410; allowing the user to check rewards for achieving goals412; allowing the user to perform searches of people to send friend requests to418; allowing the user to see the user name of the person connected to the system416; allowing the user to receive emails from system420; and allowing the user to see connection status414.

Method for Tracking, Motivating, and Re-Undewriting a Group of Users

In some embodiments, a plurality of processes that are part of the electronic document issuance process are performed by one or more computing devices, such as computing device200. The methods are implemented with components of the exemplary operating environments ofFIGS. 1-4. The steps of this exemplary method are embodied in a computer readable medium containing computer readable code such that the steps are implemented when the computer readable code is executed by a computing device. While the blocks in the disclosed processes are shown in a particular order, the actual order may differ. In some embodiments, some steps may be performed in parallel.

FIG. 5illustrates a flowchart of a method for sending electronic documents with formatted paragraphs to a group of users that form groups that use wearables so as to monitor their fitness and lifestyle and report biometric information back to the server. In some embodiments, method500operates within an analytical engine configured to produce lists of address leads of users that share similar lifestyles and offer them the opportunity to join groups that report biometric data back to the server. In one embodiment, users within a group may be able to share information associated with biometric data collected by a wearable device.

In one or more embodiments, method500begins when the analytical engine, through the aid of the data extraction module, receives potential address leads of users (step502). In these embodiments, the data extraction module within the analytical engine is configured to fetch and index, from social networks, users showing similar lifestyles that may be potential leads. Examples of potential leads include users that may exhibit a healthy lifestyle such as, for example, users that workout, eat healthy, jog/run every day, do sports, and the like. In one embodiment, the analytical engine may be configured to access morbidity rates, as understood in the art, of users stored in the server and enable the users to create groups with other users having similar morbidity rates, where morbidity rates are typically expressed in deaths per thousand (e.g., 9.5 deaths per thousand, or 0.95%). A similar morbidity rate may be considered one that is computed to be within a certain percentage of morbidity rates with other users or potential users. As an example, if a certain work commute has a morbidity rate of 0.08%, a similar morbidity rate may be considered other users with a work commute between 0.06% and 0.10%. Other ranges and examples of morbidity rates may be utilized, as well. The morbidity rates of each user may be determined and stored in an account of the users. Method500then advances to step504.

The analytical engine can identify attributes of each user from a social networking site. Each attribute may be an occurrence of a word (e.g., running or biking), an association with a group (e.g., a running group), or an explicit reference to an event (e.g., “I just ran a marathon”). The analytical engine can determine that two or more users have a similar lifestyle based upon the identified attributes. For example, if two users each have a social networking site that mentions the word “run” more than 10 times in the last year, then these users may be considered to have a similar lifestyle. In another example, if one user recently posted about running a marathon, then that user may be considered to have a similar lifestyle to another user that is associated with a running group.

At step504, the analytical engine, through the aid of the data processing module, processes and stores leads in an internal database to be used in further operations. In these embodiments, the data processing module within the analytical engine is configured to rank potential leads based on their lifestyle and produce leads that are stored within the internal database to be included in future electronic messaging campaigns. Method500then advances to step506.

At step506, the analytical engine, through the aid of the API, generates an electronic messaging campaign for the address leads previously selected in step504. In these embodiments, the electronic messaging campaign offers the address leads previously selected and having similar lifestyles the opportunity to create or to join groups that can then be monitored using wearables having biometric sensors. Further to these embodiments, the an electronic messaging campaign includes personalized multimedia content files/data in social networks, where the personalized multimedia files/data may be automatically generated based on a variety of demographic and biometric factors of group or non-group members (e.g., friends of group members). The personalized multimedia files/data may be generated on a real-time (e.g., real-time request for a multimedia space in a social network or other user interface) or non-real-time (e.g., email multimedia files/data) basis for the users who use the social networks. Method500then advances to step508.

At step508, the analytical engine, through the aid of the API, manages the group of users that signs up for the electronic messaging campaign previously described in step506. In these embodiments, the analytical engine, through the aid of the API, performs one or more of the following functions: sends out friend suggestions, creates groups, sends out wearables to registered users, receives biometric data from registered users, and the like. Method500then advances to step510. In response to a group being created or a user joining a group, accounts of the users who are in the group are associated with the group using a data field or other parameter in a database or other data structure.

At step510, the analytical engine runs different algorithms to process biometric data of groups of users and produces statistics that can later be used by a user to issue an electronic document comprising formatted paragraphs to groups of users by reporting the received biometric data to the server. The algorithms may parse and classify the biometric data information according to previously defined parameters to determine the overall behavior of the group. In one embodiment, big data processing techniques, as understood in the art, may be utilized to parse and classify the biometric data information. Method500then advances to step512.

At step512, the analytical engine suggests or recommends reward messages for group of users that show healthy lifestyles during a standard period of time. That is, the analytical engine may be configured to determine achievements of a collective group, such as walking more than a threshold total or average number of miles within a period of time (e.g., day, week, month), and determine a reward message for the group. The analytical engine512, in recommending reward messages, may utilize rules that may be static (e.g., static threshold values) or dynamic (e.g., progressively increasing threshold values, relative threshold values with respect to one or more users within a group, values relative to other groups, etc.).

At step506, the data gathering module determines if the data collected from the internal database and external sources matches an existing profile of a high risk individual stored in a high risk identification database. If the data collected matches the profile of a high risk individual, then method500advances to step508. If the data collected does not match the profile of a high risk individual, then method500advances to step510. Matching may include having data be exactly the same, within a range, outside a range, above a level, or below a level that is indicative of a high risk individual,

FIG. 6is a flowchart describing a method for operating a mobile application used for monitoring fitness/lifestyle of one or more users within the system. In some embodiments, method600operates within an API, resident in an analytical engine.

In one or more embodiments, method600begins when a user downloads and installs Fitness/LifeStyle app on a client computing devices for interacting with the system (step602). In these embodiments, the Fitness/LifeStyle app, such as mobile application400ofFIG. 4, are GUI applications that may be available at, downloaded, and installed from a public software app store or digital application distribution platform, such as Apple iTunes®, Google Play® Store and Amazon.com®, among others. Method600then advances to step604.

At step604, the user interface of the Fitness/LifeStyle app allows a user to create an account within the system of the server. In these embodiments, the API running on the analytical engine receives an account request including all personal data of the user who wishes to open an account. The API processes the information and creates an account within the system. Examples of personal data include name, emails, phone numbers, lifestyle preferences, workout preferences, demographics, geographics, occupations, and the like. Method600then advances to step606.

At step606, the user interface of the Fitness/Lifestyle app allows the user to invite friends having similar lifestyles to create or join groups. In these embodiments, the API running in the analytical engine sends and receives friend requests to/from users that wish to form groups with similar lifestyles. In one embodiment, the analytical engine may be configured to predetermine whether friends actually have similar lifestyles. The determination as to whether the friends have similar lifestyles may be initially based on user profiles of the friends that may include lifestyle related information so that the analytical engine can filter the friends prior to or after the user selects his or her friends. In the case of filtering prior to selection, the analytical engine may simply not include friends without similar lifestyles in a selectable list for other users (or friends) to invite the users with similar lifestyles to join a group. In the case of filtering after the selection, the analytical engine may notify the user that the friend does not meet the criteria of having a similar lifestyle. The notification may optionally provide reason(s) as to why the friend does not include a similar lifestyle. In another embodiment, the analytical engine may generate and send a notice to the friend that indicates that his or her friend invited him or her to be in a group, but that certain lifestyle parameters are deficient and, if corrected, would allow the friend to join the group. The analytical engine may, in addition to being used in creating or joining friends with similar lifestyles into groups, operate to track the members of the group and, in the event that one of the members fails to meet the similar lifestyle, notify the member that he or she is falling outside the lifestyle parameter(s) of the group. Method600then advances to step608.

At step608, the analytical engine pulls from the internal database the list of users that have joined the biometric monitoring program and generates orders to send out and deliver wearables to the users. In these embodiments, the registered users receive the wearables and pair them with the client computing devices. Examples of wearables include smartwatches, trackers, pedometers, activity trackers, and the like. Method600then advances to step610.

At step610, the Fitness/Lifestyle app receives from wearables the biometric data read from the users. In these embodiments, the biometric data collected from the users, but is not limited to, number of steps per day, workouts, heartbeat rates, levels of sweat, O2 saturation, and the like. Method600then advances to step612.

At step612, the Fitness/Life app sends via the communication network the biometric data collected from users for further analysis. In these embodiments, the API running on the analytical engine receives the biometric data and aggregates the information into groups to be used for issuing an electronic document at a later time by users of the system. For example. The electronic document may comprise formatted paragraphs. Method600ends.

One computer-implemented embodiment may include a method that extracts user data associated with users from an external source, the user data including information indicative of lifestyles of the respective users. The user data may be processed to populate a database, and the user data may be stored in the database. The user data stored in the database may be queried for users having attributes indicative of similar lifestyles. A social network user identifier of each user of the set of users having attributes indicative of similar lifestyles may be obtained. An electronic messaging content may be communicated to the social network user identifier of each user in the set of users having attributes indicative of similar lifestyles. A change in risk level for the set of users having attributes indicative of similar lifestyles may be calculated.

Extraction of the user data may include extracting user data from a social network. The user data may include at least one attribute indicative of a lifestyle of a user. Collection of the biometric data of a biometric parameter may be performed by a remote server. The biometric parameter may be monitored by a wearable device being worn by the respective users in a group of users having attributes indicative of similar lifestyles. The biometric data monitored by the wearable device may be processed to determine statistics of the biometric data of the users in the group. The statistics may include averages, maximums, standard deviations, and/or other statistics that may be used for group document issuance purposes. The users may be issued reward messages based on achievement datums as determined from the statistics of the collected biometric data. In determining rewards, the analytical engine may use rules and/or thresholds that enable the analytical engine to determine and recommend reward(s).

In addition, the biometric data monitored by the wearable device of each of the users of the group may be processed, and a determination as to whether the respective users qualify for receiving a reward message. The biometric data may be collected by the wearable devices used by the users of the group may be the same biometric parameter (e.g., steps taken during a day by each of the users in the group). The biometric data from the users may be collected via a wireless communications network with which a mobile electronic communications device of the respective users utilize, and stored in data records associated with the respective users in a database. The collected biometric data of the users of the group may be processed, and, based on the processed biometric data, suggest reward messages for the group of users during a period of time.

One aspect may include a user in a group being enabled to invite a user to join the group of users having attributes indicative of similar lifestyles. The process may enable an electronic messaging campaign may be conducted by communicating personalized multimedia content in one or more social networks based on lifestyles identified to be in association with users of the one or more social networks.

One embodiment of a method may include creating accounts for users who are associated with a biometric monitoring program. Users may be enabled to create groups of users associated with the biometric monitoring program, and having similar lifestyles using a downloadable application installed on a computing device of a user. Biometric data being monitored by a wearable device being used by respective users in a group of users having similar lifestyles may be collected. The biometric data collected by respective wearable devices of the users in the group may be processed to determine whether the group qualifies for receiving a reward message. A listing of the users in the group and associated biometric data from the users in the group may be transmitted.

In managing the users, a user may be enabled to search for friends with similar lifestyles via the downloadable application. Responsive to the user searching for other users with similar lifestyles, indicia (e.g., name photograph) of the friends with similar lifestyles may be caused to be selectably displayed for the user to invite to a group with similar lifestyles. The biometric data collected from each of the users in a group may be processed, and a determination of a collective achievement of the users in the group may be determined. In one embodiment, a reward message for the users in the group may be recommended in an automated manner based on the collective achievement of the group as determined from analyzing the biometric data of the users in the group.

Statistics from the biometric data of each user in the group may be generated, and each of the users in the group may be enabled to view at least a portion of the statistics. The group may be established by associating the accounts of the users in the group, and each of the users in the group may be enabled to share information associated with biometric data collected by the wearable devices being used by the users in the group. Statistical information derived from the biometric data may be enabled to be shared. For example, the statistical information may include a collective number of miles the group walked during a time period to motivate the users in the group, average heart rate during commute times, average heart rate during work hours, and so forth. It should be understood that the biometric data may include biometric data of one or more biometric parameter, such as heart rate, number of steps, movement, calories burned, or otherwise.

As a social network is created or expanded, users within the group may be enabled to search for friends with similar lifestyles who may or may not be within the group, thereby enabling the group to grow in a dynamic manner. A morbidity rate for the users may be computed, and the users may be enabled to create groups of users with similar lifestyles and morbidity rates. In one embodiment, the morbidity rates may not be presented to the users.

With regard toFIG. 7, a flowchart of a process700for use with groups of users is provided. The process700may start at step702, geolocation data of the respective users throughout a time period may be received. The time period may extend for days, weeks, or months to enable the a user to get a day-to-day understanding of the users lifestyle, risk profile, or other factor that may enable the user to better assess the users. A risk profile may be a value determined by assessing lifestyle, geolocations traveled by a user, time of day the user travels in particular geolocations, and so on. In one embodiment, a map may be defined with risk values in specific geopolitical regions (e.g., zip codes), on specific roadways (e.g., highways versus side streets, intersections, etc.), specific modes of transportation (e.g., automobile, train, subway, bus, airplane), optionally as a function of particular times (e.g., rush hours, after dusk and before dawn, after midnight and before 6 AM), and so on. A downloadable mobile app for users to download to respective mobile devices may be provided and available via a communications network (e.g., at an online app store). In an embodiment, the downloadable mobile app may collect the geolocation data generated respective mobile devices of the users, or a network device, such as a server may track geolocation of the mobile devices of respective users. At step704, a determination based on the geolocation data may be made of users with similar risk profiles. Similar risk profiles may be determined based on, at least in part, the geolocation data of the respective users throughout the time period. At step706, users with similar risk profiles may be enabled to form or join groups of users with similar risk profiles via a social network. At step708, a group of users with similar risk profiles may be established, and risk for the group of users based on the similar risk profiles may be calculated at the end of the time period at step710.

In determining users with similar risk profiles based on geolocation data, the determination may include determining that the users travel within a predetermined distance threshold to and from work, for example. The distance range may also be correlated to a morbidity rate below a threshold level. For example, the predetermined distance threshold may include a distance of 5 miles, 10 miles, 15 miles, 20 miles, and so forth, where the distance may correlate to a risk factor score. Additionally, specific geolocation information, such as type of roadways, particular roadways, particular intersections, particular commute times, modes of transportations, and so forth may be used in determining morbidity rate and/or risk factor. A risk factor score may be a value that is determined based on one or more risk factors (e.g., total, average, or other statistical value produced by the risk factors). Moreover, the determination may include determining average speed, number of stops, and so on, that may contribute to the risk factor score and/or morbidity rate. Still yet, the specific geolocation data may be associated with crime, historical accident rates, historical traffic death rates, or any other historic information at geolocations of users that may play a part is affecting morbidity rate and/or risk factor score.

In one embodiment, a determination of users with similar travel types may be made. The travel types may be determined based on the geolocation data, and the similar travel types may include public transportation (e.g., trains, buses, ferries, airplanes) or personal transportation (e.g., automobile, motor cycle). In determining similar risk profiles, risk profiles of users who travel on similar types of transportation for similar amounts of time may be determined. Similar amounts of time may be durations of time that are, for example, within 5, 10, 20, or 30 minutes of one another, and may be varied based on type of roadways or mode of transportation taken by the users being compared and/or grouped. Still yet, a determination based on the geolocation data of the types of roadways on which the users drive may be made, and a determination of users with similar risk profiles who drive on similar types of roadways for similar amounts of time may be made.

In another embodiment, a biometric sensor to collect biometric data may be provided to users. Biometric data may be collected from the biometric sensor being used by the users, and the biometric data may be correlated with the geolocation data. A determination of a biometric response of the respective users in association with the geolocation data may be made. The biometric response, such as change in heart rate, may enable a determination as to how a user reacts to certain situations, such as rush hour traffic, work activities, stressful situations, non-stress situations, and so forth, which may be used to determine risk factors and profiles. A determination of users with similar biometric responses to similar geolocation data may be made, and users with similar biometric responses may be grouped.

With regard toFIG. 8, a flowchart of a process800of a user using non-invasive techniques is shown. Invasive techniques may include blood samples, but for the purposes of this description, may include urine samples, stool samples, or other samples of bodily fluids or tissue that are used for determining health and risk factors of a user. In other words, the non-invasive techniques are independent of collecting paramed data. The process800may start at step802by collecting biometric data of the at least one biometric parameter and geolocation data over at least one time period from a wearable device. The wearable device may be configured to collect at least one biometric parameter from a user. The biometric parameters may include number of steps, heart rate, blood sugar level, energy usage, movement, or any other dynamic biometric measurement or combination thereof, as understood in the art. The time period(s) may include work commuting times, sleep times, mornings, afternoons, work periods, workout times, and so forth. At step806, a risk factor score for the user based on the biometric parameter(s) and geolocation data over the time period(s) may be generated. The risk factor score may be based on the combined biometric data of the biometric parameter(s) and geolocation data.

At step808, an input of answers to a questionnaire about the user may be received. The questionnaire may be conventional or may include additional information, such as typical drive times, work geolocation, or any other information that can be used in correlating biometric parameter(s) and geolocation data to produce a risk factor score. At step810, a risk level of the user based on the answers and risk factor score may be calculated.

In one embodiment, the time period(s) may include commute times, and biometric parameter(s) include heart rate. The time period(s) may additionally or alternatively include work times, and the biometric parameter(s) may include heart rate. A determination may be made of an activity (e.g., sleep, work, exercise) of the user during time period(s). One aspect may include providing a mobile app to be downloaded to a mobile device of the user, where the mobile device may be selectively in wireless communication with the wearable device. The mobile app may be configured to collect the biometric data being captured by the wearable device. A group of users having similar risk factor scores may be generated. In being enabled to participate, the users may be provided with an interface (e.g., graphical user interface, such as a website or app user interface) via which the users may register to join the group.

The risk factor scores of the each of the users in the group may be generated by processing additional biometric data and geolocation data. Responsive to the risk factor scores in the group improving in the aggregate, a reward message to which the users in the group are set to receive may be recommended. In one embodiment, the recommendation may be automatically made based on rules that relate to a correlation of biometric data and geolocation data. Moreover, the biometric data of the biometric parameter(s) and geolocation data over the at least one time period may be correlated. By correlating the biometric and geolocation data, a determination as to how a user reacts to stresses in various situations may be made. A determination of similar lifestyles and medical history based on the answers and the geolocation data may be made. A determination of lifestyle of the user based on the geolocation data and biometric data may also be made. That is, lifestyle may be determined based on the geolocations. The lifestyle may include type of activities, amount of commute time, work stress, amount of leisure time (e.g., golfing, fishing, etc.), specific retail establishments visited, specific entertainment establishments visited, and so on. In one embodiment, a risk factor score may be influenced by mobile device operation during driving times (e.g., texting or talking while driving), and a mobile app may track mobile device usage and geolocations for determining usage while commuting. Similar medical histories may be determined based on general and/or specific medical conditions of the users. For example, general medical histories may include particular diseases, such as heart disease, while specific medical histories may be particular stage of disease, family history of disease, or general condition of health.

When implemented in hardware, the functionality may be implemented within circuitry of a wireless signal processing circuit that may be suitable for use in a wireless receiver or mobile device. Such a wireless signal processing circuit may include circuits for accomplishing the signal measuring and calculating steps described in the various embodiments.

Any reference to claim elements in the singular, for example, using the articles “a,” “an” or “the,” is not to be construed as limiting the element to the singular.