Claims:We Claim:

1. A computer system comprising:

one or more processors; and

a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, cause the one or more processors to perform a method for predicting behavior of a user (102) based on a genomic profile in real-time, the method comprising:

receiving, at a behavior prediction system (108), one or more set of data, wherein the one or more set of data is received from one or more sources, wherein the one or more set of data is received in one or more input forms;

analyzing, at the behavior prediction system (108), the one or more set of data received from the one or more sources, wherein the analyzing is performed to identify one or more personality factors of the user (102) using one or more hardware-run machine learning algorithms;

performing, at a clustering module (110) is associated with the behavior prediction system (108), clustering of each set of the one or more set of data to create a first set of one or more clusters, wherein the clustering is performed to segregate and categorize similar kind of data together, wherein the clustering is performed based on analyzing and identification of the one or more personality factors of the user (102), wherein the clustering is performed to enable creation of the genomic profile of the user (102);

creating, at a genome profiling engine (112) is associated with the behavior prediction system (108), the genomic profile of the user (102) based on the first set of one or more clusters, wherein the genomic profile of the user (102) is created in real-time;

performing, at the clustering module (110) is associated with the behavior prediction system (108), re-clustering of the one or more set of data and the first set of one or more clusters, wherein the re-clustering is performed to create an optimized genomic profile of the user (102) on the genome profiling engine (112), wherein the re-clustering is performed in real-time;

adding, at the behavior prediction system (108), the genomic profile of the user (102) in a bucket of one or more buckets, wherein the one or more buckets are created based on the one or more personality factors, wherein the genomic profile of the user (102) is added after evaluation of the genomic profile of the user (102); and

predicting, at the behavior prediction system (108), the behavior of the user (102) based on the evaluation of the genomic profile of the user (102), wherein the prediction of the behavior of the user (102) is performed to provide the one or more recommendations to the user (102).

2. The computer system as recited in claim 1, wherein the one or more recommendations comprise one of at least financial recommendation, policy based recommendation, and health recommendation.

3. The computer system as recited in claim 1, wherein the one or more set of data comprise a digital data, a historic data, and a user data, wherein the digital data is associated with data received from interaction of the user (102) on one or more social media platforms, wherein the historic data is associated with data received from interaction of the user (102) on one or more web based platforms, wherein the user data is associated with personal information of the user (102) received from the user (102) in real-time.

4. The computer system as recited in claim 3, wherein the digital data comprises data associated with clicks of the user (102), view of the user (102), feed of the user (102), interest of the user (102), applications used by the user (102), likes of the user (102), pages of the user (102), comments of the user (102), places visited by the user (102), and profile of the user (102), wherein the historic data comprises data associated with previous interactions, financial statements, bank records, investment portfolios, hospital records, medical records, prescriptions, tablets purchased, tests performed, tests reports, consultation schedule, consultation timing, interactions, meetings, and online interactions of the user (102), wherein the user data comprises data associated with first name, last name, age, gender, father name, mother name, education level, mobile number, address, nationality, income level, marital status, employment status, children, occupation, religion, image, address, company name, current salary, current location, mobile number, and mail id associated with the user (102).

5. The computer system as recited in claim 1, wherein the one or more sources comprise of at least one of a database warehouse, a database (116), one or more social media platforms, one or more web based platforms, and one or more third-party databases.

6. The computer system as recited in claim 1, wherein the one or more input forms comprises of text, audio, video, images, animation, and interactive content.

7. The computer system as recited in claim 1, wherein the one or more buckets are created based on behavior characteristics of users, wherein the one or more buckets facilitate in identification of character of users, wherein the one or more buckets are created based on factors such as reserved or warm, emotionally stable or reactive, deferential or dominant, serious or lively, trusting or vigilant, sensitive or unsentimental, self-assured or apprehensive, shy or bold, expedient or rule-conscious, private or forthright, and abstracted or practical.

8. The computer system as recited in claim 1, wherein the behavior prediction system (108) updates the received one or more set of data in real-time, wherein the updating is done to identify changes in the one or more set of data, wherein the updating is done to provide the accurate one or more recommendations to the user (102).

9. The computer system as recited in claim 1, wherein the behavior prediction system (108) performs re-clustering on the one or more set of data and the first set of one or more clusters up to one or more levels in the clustering module (110), wherein the one or more levels are selected in real-time.

10. The computer system as recited in claim 1, wherein the behavior prediction system (108) performs the clustering and the re-clustering using one or more hardware-run clustering algorithms in the clustering module (110).
, Description:METHOD AND SYSTEM FOR PREDICTING BEHAVIOR OF USER BASED ON GENOMIC PROFILE IN REAL-TIME
TECHNICAL FIELD
[0001] The present disclosure relates to the field of market segmentation, and in particular, relates to a method and system for predicting behavior of user based on genomic profile in real-time.
BACKGROUND
[0002] Targeted marketing is considered as an important part of a business marketing effort. Target marketing entails focusing advertising on those people or group of people that are more likely to purchase a product. The group of people having similar behaviour characteristics tend to purchase similar kind of products. The identification of similar kind of people can be done using their social media platforms. Social media platforms act as a platform for the users to declare information about themselves. The information may include their professional qualification, education, skills, and interests. For example, the likes and dislikes of a user may be easily identified by analysing the Facebook profile of the user.
OBJECT OF THE DISCLOSURE
[0003] A primary object of the present disclosure is to predict behavior of a user based on a genomic profile in real-time.
[0004] Another object of the present disclosure is to create the genomic profile of the user in real-time.
[0005] Yet another object of the present disclosure is to segregate users based on behavior characteristics of the users in real-time.
[0006] Yet another object of the present disclosure is to provide one or more recommendations to the user in real-time.
SUMMARY
[0007] The present disclosure provides a computer system. The computer system includes one or more processors and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of instructions causes the one or more processors to perform a method to predict behavior of a user based on a genomic profile in real-time. The method includes a first step to receive one or more set of data at a behavior prediction system. The method includes next step to analyze the one or more set of data received from one or more sources at the behavior prediction system. The method includes yet another step to perform clustering of each set of the one or more set of data to create a first set of one or more clusters at a clustering module associated with the behavior prediction system. The method includes yet another step to create the genomic profile of the user based on the first set of one or more clusters at a genome profiling engine associated with the behavior prediction system. The method includes yet another step to perform re-clustering on the one or more set of data and the first set of one or more clusters at the clustering module associated with the behavior prediction system. The method includes yet another step to add the genomic profile of the user in a bucket of one or more buckets at the behavior prediction system. The method includes yet another step to predict the behavior of the user based on the evaluation of the genomic profile of the user at the behavior prediction system. The one or more set of data is received from the one or more sources. The one or more set of data is received in one or more input forms. The analyzing is performed to identify one or more personality factors of the user using one or more hardware-run machine learning algorithms. The clustering is performed to segregate and categorize similar kind of data together. The clustering is performed based on analyzing and identification of the one or more personality factors of the user. The clustering is performed to enable creation the genomic profile of the user. The genomic profile of the user is created in real-time. The re-clustering is performed to create the optimized genomic profile of the user. The re-clustering is performed in real-time. The one or more buckets are created based on the one or more personality factors. The genomic profile of the user is added after evaluation of the genomic profile of the user. The prediction of the behavior of the user is performed to provide the one or more recommendations to the user.
[0008] In an embodiment of the present disclosure, the one or more recommendations include one of at least financial recommendation, policy based recommendation, and health recommendation.
[0009] In an embodiment of the present disclosure, the one or more set of data include a digital data, a historic data, and a user data. The digital data is associated with data received from interaction of the user on one or more social media platforms. The historic data is associated with data received from interaction of the user on one or more web based platforms. The user data is associated with personal information of the user received from the user in real-time.
[0010] In an embodiment of the present disclosure, the digital data includes data associated with clicks of the user, view of the user, feed of the user, interest of the user, applications used by the user, likes of the user, pages of the user, comments of the user, places visited by the user, and profile of the user. The historic data includes data associated with previous interactions, financial statements, bank records, investment portfolios, hospital records, medical records, prescriptions, tablets purchased, tests performed, tests reports, consultation schedule, consultation timing, interactions, meetings, and online interactions of the user. The user data includes data associated with first name, last name, age, gender, father name, mother name, education level, mobile number, address, nationality, income level, marital status, employment status, children, occupation, religion, image, address, company name, current salary, current location, mobile number, and mail id associated with the user.
[0011] In an embodiment of the present disclosure, the one or more sources include at least one of a database warehouse, a database, one or more social media platforms, one or more web based platforms, and one or more third-party databases.
[0012] In an embodiment of the present disclosure, the one or more input forms include text, audio, video, images, animation, and interactive content.
[0013] In an embodiment of the present disclosure, the one or more buckets are created based on behavior characteristics of users. The one or more buckets facilitate in identification of character of users. The one or more buckets are created based on factors such as reserved or warm, emotionally stable or reactive, deferential or dominant, serious or lively, trusting or vigilant, sensitive or unsentimental, self-assured or apprehensive, shy or bold, expedient or rule-conscious, private or forthright, and abstracted or practical.
[0014] In an embodiment of the present disclosure, the behavior prediction system updates the received one or more set of data in real-time. The updating is done to identify changes in the one or more set of data. The updating is done to provide the accurate one or more recommendations to the user.
[0015] In an embodiment of the present disclosure, the clustering module associated with the behavior prediction system performs re-clustering on the one or more set of data and the first set of one or more clusters up to one or more levels. The one or more levels are selected in real-time.
[0016] In an embodiment of the present disclosure, the clustering module associated with the behavior prediction system performs the clustering and the re-clustering using one or more hardware-run clustering algorithms.
BRIEF DESCRIPTION OF THE FIGURES
[0017] Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:
[0018] FIG. 1 illustrates a general overview of an interactive computing environment 100 for predicting behavior of a user based on a genomic profile in real-time, in accordance with various embodiments of the present disclosure; and
[0019] FIG. 2 illustrates a block diagram of a computing device, in accordance with various embodiments of the present disclosure.
DETAILED DESCRIPTION
[0020] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present technology. It will be apparent, however, to one skilled in the art that the present technology can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form only in order to avoid obscuring the present technology.
[0021] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present technology. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not other embodiments.
[0022] Moreover, although the following description contains many specifics for the purposes of illustration, anyone skilled in the art will appreciate that many variations and/or alterations to said details are within the scope of the present technology. Similarly, although many of the features of the present technology are described in terms of each other, or in conjunction with each other, one skilled in the art will appreciate that many of these features can be provided independently of other features. Accordingly, this description of the present technology is set forth without any loss of generality to, and without imposing limitations upon, the present technology.
[0023] FIG. 1 illustrates a general overview of an interactive computing environment 100 for predicting behavior of a user 102 based on a genomic profile in real-time, in accordance with various embodiments of the present disclosure. The interactive computing environment 100 includes the user 102, a computing device 104, a communication network 106, a behavior prediction system 108, a server 114, a database 116, and an administrator 118.
[0024] The interactive computing environment 100 includes the computing device 104. The computing device 104 is associated with the user 102. The user 102 is any person that wants to receive one or more recommendations based on the behavior of the user 102 from the behavior prediction system 108 in real-time. The one or more recommendations include one of at least financial recommendation, policy based recommendation, health recommendation, and the like. In an example, the user 102 is any person who wants a recommendation of best health policy from the behavior prediction system 108 based on the behavior of the user 102. In another example, the user 102 is any person who wants recommendation of best mutual fund from the behavior prediction system 108 based on salary and savings of the user 102. In an embodiment of the present disclosure, the user 102 is any person who wants financial recommendation from the behavior prediction system 108. In another embodiment of the present disclosure, the user 102 is any person who wants recommendation of any health related policy from behavior prediction system 108 based on behavior of user 102. In another embodiment of the present disclosure, the user 102 is any person who wants recommendation related to wealth management from the behavior prediction system 108 based on the behavior of the user 102.
[0025] In an embodiment of the present disclosure, the computing device 104 is a portable computing device. The portable computing device includes but may not be limited to laptop, smartphone, tablet, PDA and smart watch. In an example, the portable computing device may be an iOS-based smartphone, an Android-based smartphone, a Windows-based smartphone and the like. In another embodiment of the present disclosure, the computing device 104 is a fixed computing device. The fixed computing device includes but may not be limited to desktop, workstation, smart TV and mainframe computer.
[0026] In addition, the computing device 104 performs computing operations based on a suitable operating system installed inside the computing device 104. In general, the operating system is system software that manages computer hardware and software resources and provides common services for computer programs. In addition, the operating system acts as an interface for software installed inside the computing device 104 to interact with hardware components of the computing device 104. In an embodiment of the present disclosure, the computing device 104 performs computing operations based on any suitable operating system designed for the portable computing device. In an example, the operating system installed inside the computing device 104 is a mobile operating system. Further, the mobile operating system includes but may not be limited to Windows operating system from Microsoft, Android operating system from Google, iOS operating system from Apple, Symbian operating system from Nokia, Bada operating system from Samsung Electronics and BlackBerry operating system from BlackBerry. However, the operating system is not limited to above mentioned operating systems. In an embodiment of the present disclosure, the computing device 104 operates on any version of particular operating system corresponding to above mentioned operating systems.
[0027] In another embodiment of the present disclosure, the computing device 104 performs computing operations based on any suitable operating system designed for fixed computing device. In an example, the operating system installed inside the computing device 104 is Windows from Microsoft. In another example, the operating system installed inside the computing device 104 is Mac from Apple. In yet another example, the operating system installed inside the computing device 104 is Linux based operating system. In yet another example, the operating system installed inside the computing device 104 is Chrome OS from Google. In yet another example, the operating system installed inside the computing device 104 may be one of UNIX, Kali Linux, and the like. However, the operating system is not limited to above mentioned operating systems.
[0028] In an embodiment of the present disclosure, the computing device 104 operates on any version of Windows operating system. In another embodiment of the present disclosure, the computing device 104 operates on any version of Mac operating system. In yet another embodiment of the present disclosure, the computing device 104 operates on any version of Linux operating system. In yet another embodiment of the present disclosure, the computing device 104 operates on any version of Chrome OS. In yet another embodiment of the present disclosure, the computing device 104 operates on any version of particular operating system corresponding to above mentioned operating systems.
[0029] Further, the interactive computing environment 100 includes the communication network 106. In an embodiment of the present disclosure, the communication network 106 connects the computing device 104 with the behavior prediction system 108. The communication network 106 provides medium to the computing device 104 to connect to the behavior prediction system 108. Also, the communication network 106 provides network connectivity to the computing device 104. In an example, the communication network 106 uses a set of protocols to connect the computing device 104 to the behavior prediction system 108. The communication network 106 connects the computing device 104 to the behavior prediction system 108 using a plurality of methods. The plurality of methods used to provide network connectivity to the computing device 104 includes 2G, 3G, 4G, 5G, Wifi and the like.
[0030] In an embodiment of the present disclosure, the communication network 106 is any type of network that provides internet connectivity to the computing device 104. In an embodiment of the present disclosure, the communication network 106 is wireless mobile network. In another embodiment of the present disclosure, the communication network 106 is wired network with finite bandwidth. In yet another embodiment of the present disclosure, the communication network 106 is combination of the wireless and the wired network for optimum throughput of data transmission. In yet another embodiment of the present disclosure, the communication network 106 is an optical fiber high bandwidth network that enables high data rate with negligible connection drops.
[0031] The interactive computing environment 100 includes the behavior prediction system 108. The behavior prediction system 108 receives data from the user 102 as input. The behavior prediction system 108 performs processing and validation on received data and provides the one or more recommendations to the user 102. In addition, the behavior prediction system 108 performs profiling of the user 102 based on received data. Moreover, the behavior prediction system 108 performs clustering and re-clustering on received data. The behavior prediction system 108 is connected with the server 114. The server 114 is associated with the database 116.
[0032] In an embodiment of the present disclosure, the behavior prediction system engine 108 is associated with an administrator 118. The administrator 118 is any person that configures and operates the behavior prediction system 108 at back end. In an embodiment of the present disclosure, the administrator 118 is any person that maintains and operates the behavior prediction system 108. In yet another embodiment of the present disclosure, the administrator 118 is any person that troubleshoots the behavior prediction system 108.
[0033] The behavior prediction system 108 receives one or more set of data. The behavior prediction system 108 receives the one or more set of data from one or more sources. The behavior prediction system 108 receives the one or more set of data in one or more input forms. The one or more set of data includes a digital data, a historic data, and a user data. The digital data is associated with data received from interaction of the user 102 on one or more social media platforms. In addition, the historic data is associated with data received from interaction of the user 102 on one or more web based platforms. Also, the user data is associated with personal information of the user 102 received from the user 102 in real-time.
[0034] The behavior prediction system 108 receives the digital data from the one or more social media platforms. In an example, the one or more social media platforms include but may not be limited to Facebook, Twitter, Instagram, LinkedIn, Pinterest, Whatsapp, WeChat, YouTube, Tumblr, Google+, Snapchat, and Telegram. The behavior prediction system 108 receives the historic data from the one or more web-based platforms. In an example, the one or more web-based platforms include but may not be limited to banking websites, hospitality services related websites, medical websites, and financial websites. The behavior prediction system 108 receives the user data from the user 102 in real-time. In an embodiment of the present disclosure, the behavior prediction system 108 may receive the user data from the user 102 in a plurality of formats. In an example, the plurality of formats includes form, table, and the like.
[0035] The digital data includes data associated with clicks of the user 102, views of the user 102, feed of the user 102, interest of the user 102, and applications used by the user 102. In addition, the digital data includes data associated with likes of the user 102, pages of the user 102, comments of the user 102, places visited by the user 102, profile of the user 102, and the like. Further, the historic data includes data associated with previous interactions, financial statements, bank records, investment portfolios, hospital records, and medical records of the user 102. Also, the historic data includes data associated with prescriptions, tablets purchased, tests performed, tests reports, consultation schedule, consultation timing, interactions, meetings, online interactions of the user 102 and the like. Furthermore, the user data includes data associated with first name, last name, age, gender, father name, mother name, education level, mobile number, address, and nationality of the user 102. Moreover, the user data includes data associated with income level, marital status, employment status, children, and occupation of the user 102. Also, the user data includes data associated with religion, image, address, company name, current salary, current location, mobile number, mail id associated with the user 102, and the like.
[0036] In addition, the one or more sources includes at least one of a database warehouse, the database 116, the one or more social media platforms, the one or more web based platforms, one or more third-party databases, and the like. The one or more input forms includes but may not be limited to text, audio, video, images, animation, and interactive content. The one or more set of data provides complete information of the user 102. The behavior prediction system 108 receives the one or more set of data to provide the one or more recommendations to the user 102.
[0037] The behavior prediction system 108 analyzes the one or more set of data received from the one or more sources. The behavior prediction system 108 performs the analyzing to identify one or more personality factors of the user 102 using one or more hardware-run machine learning algorithms. In an embodiment of the present disclosure, the behavior prediction system 108 analyzes the one or more set of data in real-time.
[0038] The behavior prediction system 108 includes a clustering module 110. The clustering module 110 performs clustering of each set of the one or more set of data to create a first set of one or more clusters. The clustering module 110 performs the clustering to segregate and categorize similar kind of data together. The clustering is performed based on analyzing and identification of the one or more personality factors of the user 102. The clustering module 110 performs the clustering to enable creation the genomic profile of the user 102.
[0039] The behavior prediction system 108 includes a genome profiling engine 112. The genome profiling engine 112 performs creating of the genomic profile of the user 102 based on the first set of one or more clusters. In an embodiment of the present disclosure, the genomic profile of the user 102 includes the genetic information. The genome profiling engine 112 creates the genomic profile of the 1user 102 based on clustering in the clustering module 110 in real-time. The genomic profile is created based on analyzing and identification of the one or more personality factors of the user 102 and the first set of one or more clusters. The genome profiling engine 112 allows the administrator 118 to modify and optimize the genomic profile of the user 102.
[0040] Further, the clustering module 110 performs re-clustering on the one or more set of data and the first set of one or more clusters. The clustering module 110 performs the re-clustering to create a optimized genomic profile of the user 102. The clustering module 110 performs the re-clustering in real-time. The clustering module 110 performs re-clustering on the one or more set of data and the first set of the one or more clusters up to one or more levels. The behavior prediction system 108 selects the one or more levels in real-time. The clustering module 110 performs the clustering and the re-clustering using one or more hardware-run clustering algorithms. In an example, the one or more hardware-run clustering algorithms include but may not be limited to k-means clustering, mean-shift clustering, DBSCAN, and hierarchical clustering. The behavior prediction system 108 creates the precise genomic profile of the user 102 based on the clustering and the re-clustering on the one or more set of data and the first set of one or more clusters.
[0041] The behavior prediction system 108 adds the genomic profile of the user 102 in a bucket of one or more buckets. The behavior prediction system 108 creates the one or more buckets. The one or more buckets are created based on the one or more personality factors. The genomic profile of the user 102 is added after evaluation of the genomic profile of the user 102. In an embodiment of the present disclosure, the one or more buckets are pre-defined by the behavior prediction system 108. In another embodiment of the present disclosure, the one or more buckets are created by the behavior prediction system 108 in real-time. In yet another embodiment of the present disclosure, the one or more buckets are updated by the behavior prediction system 108 in dynamic and adaptive manner.
[0042] The behavior prediction system 108 creates the one or more buckets based on behavior characteristics of users. The one or more buckets facilitate in identification of character of users. The one or more buckets are created based on factors such as reserved or warm, emotionally stable or reactive, deferential or dominant, serious or lively, and trusting or vigilant. Moreover, the one or more buckets are created based on factors such as sensitive or unsentimental, self-assured or apprehensive, shy or bold, expedient or rule-conscious, private or forthright, abstracted or practical, and the like.
[0043] In an embodiment of the present disclosure, the one or more buckets are created based on the one or more personality factors. In an embodiment of the present disclosure, the bucket of the one or more buckets corresponds to single personality factor or combination of the one or more personality factors. The number of the one or more buckets is sixteen. However, the number of the one or more buckets is not restricted to sixteen.
[0044] The behavior prediction system 108 predicts the behavior of the user 102 based on the evaluation of the genomic profile of the user 102. The behavior prediction system 108 performs the prediction of the behavior of the user 102 to provide the one or more recommendations to the user 102. The behavior prediction system 108 provides the one or more recommendations to the user 102 in real-time. The behavior prediction system 108 provides the one or more recommendations to the user 102 on the computing device 104 of the user 102. In an embodiment of the present disclosure, the behavior prediction system 108 provides the one or more recommendations to the user 102 on devices connected with the computing device 104 of the user 102. In an embodiment of the present disclosure, the behavior prediction system 108 provides the one or more recommendations to the user 102 on the computing device 104 in form of push notifications, OTA updates, SMS, and the like.
[0045] The interactive computing environment 100 includes the server 114. In general, the server 114 is computer program or device that provides functionality or service to all connected programs or devices. In an example, the server 114 is one of at least web server, application server, proxy server, cloud server, mail server, file server, and the like. In an embodiment of the present disclosure, the server 114 is cloud server. In general, the cloud server possesses and exhibit similar capabilities and functionality to the server 114 but is accessed remotely from cloud service provider. In an example, the server 114 is similar to a physical server but provides virtual space for handling all operations.
[0046] The server 114 includes the database 116. The database 116 provides storage location for the one or more set of data, the genomic profile of the user 102, and any other information used by the interactive computing environment 100. In an embodiment of the present disclosure, the database 116 is associated with the server 114. The server 114 stores the one or more set of data in the database 116. In an embodiment of the present disclosure, the server 114 stores the genomic profile of the user 102 in the database 116. The server 114 interacts with the database 116 to retrieve the stored data.
[0047] In an embodiment of the present disclosure, the behavior prediction system 108 utilizes a plurality of techniques to enhance security of the interactive computing environment 100. The plurality of techniques includes but may not be limited to fingerprint identification, voice recognition, retinal scans, iris scans, and facial recognition. In an example, the behavior prediction system 108 utilizes fingerprint identification to verify authenticity of the user 102 before providing the one or more recommendations to the user 102. In another example, the behavior prediction system 108 utilizes facial recognition to verify authenticity of the user 102 before providing the one or more recommendations to the user 102.
[0048] In an embodiment of the present disclosure, the behavior prediction system 108 receives the one or more set of data in unstructured form. The behavior prediction system 108 converts the one or more set of data from unstructured form into structured form. The behavior prediction system 108 performs the conversion using hardware-run text processing algorithms. The hardware-run text processing algorithms includes but may not be limited to natural language processing algorithms and data matching algorithms.
[0049] The behavior prediction system 108 updates the received one or more set of data in real-time. The behavior prediction system 108 does the updating to identify changes in the one or more set of data. The behavior prediction system 108 does the updating to provide the accurate one or more recommendations to the user 102. In an example, the behavior prediction system 108 continuously checks Facebook profile of a user A to check for any new update in profile of the user A. In another example, the behavior prediction system 108 continuously checks Instagram and Twitter accounts of a user B to identify change in respective profiles of the user B.
[0050] In an embodiment of the present disclosure, the behavior prediction system 108 creates the genomic profile of the user 102 based on the clustering and the re-clustering of the one or more set of data. The genomic profile of the user 102 is created based on analyzing and identification of the one or more personality factors of the user 102. The genomic profile of the user 102 is created in real time. In an example, the behavior prediction system 108 receives the one or more data from the one or more sources. In an embodiment of the present disclosure, the behavior prediction system 108 analyzes the one or more set of data on the server 114. Further, the behavior prediction system 108 performs clustering of each set of the one or more set of data. Furthermore, the behavior prediction system 108 performs the re-clustering on the one or more set of data until the precise genomic profile of the user 102 is created. The behavior prediction system 108 creates the precise genomic profile of the user 102. The genomic profile of the user 102 facilitates in prediction of behavior of the user 102. The behavior prediction system 108 provides the one or more recommendations to the user 102 based on the predicted behavior of the user 102.
[0051] In an embodiment of the present disclosure, the one or more buckets have one or more weights associated with the one or more buckets. The one or more weights update in real-time based on personality characteristics of users. The one or more weights help during addition of the genomic profile of the user 102 in the bucket of the one or more buckets.
[0052] In an example, the behavior prediction system 108 receives the one or more set of data of a user X from the one or more sources. The behavior prediction system 108 performs the clustering to form a cluster P1 for the historic data, a cluster D1 for the digital data, and a cluster C1 for the user data. The behavior prediction system 108 performs the clustering based on the one or more personality factors of the user X. The behavior prediction system 108 performs re-clustering on the first set of clusters P1, D1, and C1 to create a new cluster C2. Further, the behavior prediction system 108 performs re-clustering on the new cluster C2 and the cluster C1 and creates a cluster C3. The behavior prediction system 108 creates the cluster C3 to create the precise genomic profile of the user X. The behavior prediction system 108 performs the re-clustering n number of times until the precise genomic profile of the user X is created by the behavior prediction system 108.
[0053] Further, the behavior prediction system 108 adds the user X and the genomic profile of the user X in a bucket A. In an example, bucket A represents list of users that own health insurance policy. In another example, bucket B represents list of users that own health insurance policy from past five years. In another example, bucket C represents list of users that own health insurance for lifetime.
[0054] Furthermore, the behavior prediction system 108 provides the one or more recommendations to the user X. In an example, the behavior prediction system 108 provides push notification for health checkup every month to the user X if the user X is suffering from heart problem. In another example, the behavior prediction system 108 provides push notification to recommend medicines to a user Y if the user Y is suffering from cold or flu. In another example, the behavior prediction system 108 provides push notifications to recommend mutual funds to a user Z if the user Z is interested in building investment portfolio.
[0055] It is shown in the FIG. 1 that the user 102 utilizes the computing device 104 to connect to the behavior prediction system 108. However, those skilled in the art would appreciate that there may be more number of users utilizing more number of computing devices to connect to the behavior prediction system 108.
[0056] FIG. 2 illustrates a block diagram of a computing device 200, in accordance with various embodiments of the present disclosure. The computing device 200 includes a bus 202 that directly or indirectly couples the following devices: memory 204, one or more processors 206, one or more presentation components 208, one or more input/output (I/O) ports 210, one or more input/output components 212, and an illustrative power supply 214. The bus 202 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 2 are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. The inventors recognize that such is the nature of the art, and reiterate that the diagram of FIG. 2 is merely illustrative of an exemplary computing device 200 that can be used in connection with one or more embodiments of the present invention. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of FIG. 2 and reference to “computing device 200.”
[0057] The computing device 200 typically includes a variety of computer-readable media. The computer-readable media can be any available media that can be accessed by the computing device 200 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer storage media and communication media. The computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device 200. The communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.
[0058] Memory 204 includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory 204 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical-disc drives, etc. The computing device 200 includes one or more processors that read data from various entities such as memory 204 or I/O components 212. The one or more presentation components 208 present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc. The one or more I/O ports 210 allow the computing device 200 to be logically coupled to other devices including the one or more I/O components 212, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.
[0059] The foregoing descriptions of specific embodiments of the present technology have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present technology to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, to thereby enable others skilled in the art to best utilize the present technology and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present technology.