A system for determining the behavioral pattern based on browsing data and method thereof

Abstract

The present invention discloses a system (100) for determination of behavioral pattern based on browsing behavior, wherein the system (100) comprises a profiling unit (101) for profiling the browsing behavior of the first user in a user age group. The system (100) comprises a processing unit (102) for determining the correlation between the browsing behavior of the first user and other age-related behaviors. The system (100) further comprises a mapping unit (103) for creating of an age-related correlation map by correlating the browsing behavior and other age-related behaviors. Further, the system (100) comprises an analysis unit (104) for determining the similarities in the behavior of different age groups in a geographical location and a feedback unit (105) for reviewing the behavioral pattern determination for improving the performance of the system (100).

Description

TITLE OF THE INVENTIONA system for determining the behavioral pattern based on browsing data and method thereofDESCRIPTION OF THE INVENTIONTechnical field of the invention

[0001] The Present invention relates to building deterministic and probabilistic models for the different types of age group assessment (Physical, Mental, Cognitive, Social, Psychological and Health) of individuals or cohorts of individuals and develop correlation models with the location data, using the online and offline behavioral patterns.Background of the invention

[0002] The age group of the user may be determined based on their browsing pattern with the help of data mining methods. The user age group determined from the browsing behavior uses the click-through data from the webpage, through which the reinforced discriminative and predictive modelling techniques predicts the probable age of the user. Further, the browsing behavior of the user enables determination of various parameters through which the user profile can be created. The browsing pattern can provide insight on influence of location-based factors on the users of an age group.

[0003] The big data platforms are highly used in prediction of age group of the users based on their browsing behavior, but most big data platforms do not have a clear understanding on the behavior of different age groups in different locations, and their outcomes on thoughts and behavior of the users. Further, the existing systems have no understanding of the factors that influence different generations of users in each geographical location.

[0004] In order to overcome the drawbacks of the existing systems, several technologies have been developed over the decades to facilitate understanding of the browsing patterns of the users of various age groups.

[0005] The Patent Application No. US20130086261A1 entitled “Detecting Behavioral Patterns and Anomalies Using Activity Profiles'' d scloses a system for analyzing the activity data to detect behavioral patterns and anomalies. When a particular pattern or anomaly is detected, a system may send a notification or perform a particular task. This activity data may be collected in an information management system, which may be policy based. Notification may be by way e- mail, report, pop-up message, or system message. Some tasks to perform upon detection may include implementing a policy in the information management system, disallowing a user from connecting to the system, and restricting a user from being allowed to perform certain actions. To detect a pattern, activity data may be compared to a previously defined or generated activity profile.

[0006] The Patent Application No. US20100790979 entitled “Systems and methods for determining personal characteristics" discloses systems and methods for determining personal characteristics from images by generating a baseline gender model and an age estimation model using one or more convolutional neural networks (CNNs); capturing correspondences of faces by face tracking, and applying incremental learning to the CNNs and enforcing correspondence constraint such that CNN outputs are consistent and stable for one person.

[0007] Hence, there is a need for a system and a method to determine the behavioral pattern of the users through the browsing data.Summary of the invention

[0008] The present invention discloses a system and a method determining the behavioral pattern of a user by correlating a user age group and the influence of the geographical location on the user group wherein, the system facilitates understanding the difference in behavioral patterns of each user generation based on location-based influences. The system comprises a profiling unit for analyzingand profiling the browsing behavior of the first user in the user group. The system further comprises a processing unit to process the data received from the profiling unit to process the age-related correlation of the browsing behavior of the users with various age groups.

[0009] The system further comprises a mapping unit to facilitate creation of an age- related correlation map. Further, an analysis unit uses the age-related correlation map created by the mapping unit to determine the similarities in the behavior of the users of various age groups in a geographical location. The mapping unit creates an age and location correlation map depending on the similarities in the behavior of plurality of user age groups.

[0010] The present invention discloses a method for predicting the correlation between user age group and their geographical location, wherein the method comprises the steps of collecting the browsing data of the first user within a user group to determine the browsing behavior by the profiling unit, wherein the browsing behavior data is further processed by the processing unit. The processing unit processes the browsing behavior data to corelate the derived browsing behavior of the first user with other age-related behaviors. Further, the processing unit processes the age-related correlation data of the browsing behavior with a user age group.

[0011] A mapping unit uses the age correlation data to create an age-related correlation map. The analysis unit uses the age-related correlation map to determine the similarities in the behavior of various user age groups in the geographical location. The analysis unit further determines if the majority of a user age groups behaves similarly and directs the mapping unit to create an age-location correlation map, wherein the age-related behavioral data of various user age group in a geographical location are mapped. The mapped data helps in understanding the relationship between the age group of the users with their geographical location.

[0012] The present invention is advantageous as it facilitates mapping of various age-related behaviors of various generations, facilitating understanding of the difference in behaviors, cognitive age, interests, lifestyle, etc. of the users of eachage group, based on the exposure attained in each geographical location. Further, the system facilitates detection of location-based factors that influence each user age group and the extent of the influence of each of these factors.

[0013] Furthermore, the system facilitates detection of the physical age, mental age, and the social age of the first user through their browsing patterns, and the location-based factors influencing the user age group in order to create a predictive model of age group related behaviors. The system correlates the economic influence of location-based factors on each user age group. Further, the system facilitates understanding the social influences of each geographical location on each generation of users throughout their timeline.Brief description of the drawings

[0014] The foregoing and other features of embodiments will become more apparent from the following detailed description of embodiments when read in conjunction with the accompanying drawings. In the drawings, like reference numerals refer to like elements.

[0015] Figure 1 illustrates a block diagram representation of the system for determination of behavioral pattern of the user age group based on their browsing pattern.

[0016] Figure 2 illustrates a flow diagram for the method for determination of behavioral pattern of the user age group based on their browsing pattern.Detailed description of the invention

[0017] Reference will now be made in detail to the description of the present subject matter, which is shown in the illustrations. Various changes and modifications obvious to one skilled in the art to which the invention pertains are deemed to be within the scope, and contemplation of the invention.

[0018] The present invention discloses a system and a method for determining the behavioral pattern of a user by correlating a user age group and the influence of thegeographical location on the user group wherein, the system facilitates understanding the difference in behavioral patterns of each user generation based on location-based influences.

[0019] Figure 1 illustrates a block diagram representation of the system for determination of behavioral pattern of the user age group, wherein the system (100) comprises a profiling unit (101) for analyzing and profiling the browsing behavior of the first user in a user age group. The profiling unit (101) collects and profiles the distinctive browsing data of the first user to create a user profile and to facilitate determination of the age-related behaviors. Examples of browsing behavior may include email, web information search, health information search, lifestyle related search, download music, food recipes, social networking sites, history related information search, job search, travel information and planning, bill payment websites, religious information and many more.

[0020] The system (100) further comprises a processing unit (102) to determine the correlation between the browsing behavior of the first user and other age-related behaviors, by processing the profiled browsing behavior data. According to an embodiment of the invention, the processing unit (102) uses deep learning algorithms for data processing. The system (100) further comprises a mapping unit (103) to facilitate creation of an age-related correlation map by correlating the browsing behavior of the first user and other age-related behaviors. The mapping unit (103) uses various location-based factors and parameters including area, zip code, town, city, county, state, country, etc., and their impact on the user age group, according to an embodiment of the invention.

[0021] Further, the system (100) comprises an analysis unit (104), wherein the analysis unit (104) uses the age-related correlation map created by the mapping unit (103) for determining the similarities in the behavior of different age groups in a geographical location. Further, the mapping unit (103) generates an age-location correlation map depending on the similarities in the behavior of different generations of users with various user age groups. The system (100) further comprises a feedback unit (105) that uses iterative methodologies to adjust, refine,and review the behavioral pattern determination process constantly in order to improve the performance of the system (100).

[0022] In one embodiment of the invention, the system (100) uses deep learning techniques, reinforced machine learning and predictive analytics of the user profile and the browsing behavior to identify the relationship dynamics between the user within the user group. Further, the system (100) uses statistical analysis, semantic correlation and sentimental analysis for analyzing the browsing behavior of the user and mapping the data. The system (100) collects the browsing pattern data from at least one non-intrusive data capturing applications and devices, according to one embodiment. The system (100) uses iterative methodologies using fast feedback systems for performance improvement.

[0023] Figure 2 illustrates a flow diagram of the method for determination of the behavioral pattern of the user age group based on their browsing pattern, wherein the method (200) comprises the steps of collecting the browsing data of the first user within the user group by the profiling unit (101) in order to determine the browsing behavior of the first user, in step (201). The profiling unit (101) creates the profile for the first user by using the browsing data. Further, in step (202), the processing unit (102) processes the data for correlating the browsing behavior of the first user with other age-related behaviors. If there is no correlation found, then the processing unit (102) performs correlation against all the user age groups in the geographical location, in step (203), and directs the profiling unit (101) to collect more browsing data of the users, in step (204). Further, if no correlation is found for any user age group, the collected browsing data is stored for future analysis, in step (206). Similarly, in case of partial correlation of the browsing behavior data, the processing unit (102) prompts the profiling unit (101) for further data collection, in step (205).

[0024] Subsequently, upon determining the correlation of browsing behavior of the first user with all the users in the user group, the processing unit (102) further processes the age-related correlation data of the browsing behavior of the first user with all the user age groups in the geographical location, in step (207). The mappingunit (103) uses the age-related data to create an age-related correlation map, in step (208).

[0025] Further, the analysis unit (104) uses the age-related correlation map to determine the similarities in the behavior of various user age groups in the geographical location of the first user, in step (209). The analysis unit (104) further determines if the user behavior is similar in majority of user age groups in the geographical location of the first user, in the step (210). Further, upon deriving no correlation in behavior, the analysis unit (104) studies the age-related correlation maps with geographical locations of similar characteristics, in the step (211). The analysis unit (104) further determines the presence of any correlation in the behavior, in the step (212) and in case of partial similarities in the behavior, the analysis unit (104) directs the profiling unit (101) to collect more data, in the step (214). In case of no correlations, the collected data is stored for future analysis (213).

[0026] In case of partial similarities in the behavior, the analysis unit (104) studies the age-related correlation maps with geographical locations of similar characteristics, in step (215). Further, upon identifying the correlation by the analysis unit (104) in steps (210) and (212), the mapping unit (103) creates an agelocation correlation map, wherein the behavioral pattern of various user age groups in a geographical location are mapped, in step (216). The mapped data helps in understanding the behavioral pattern of various user age groups at various geographical locations.

[0027] The present invention provides a system (100) and method (200) for understanding the difference in behavioral pattern, cognitive age, interests, lifestyle, etc. of the users of various user age groups, based on the exposure in each geographical location. The system (100) further facilitates determination of influence of the location-based factors on various user age groups and the extent of the influence of each of these factors.

[0028] Further, the system (100) facilitates detection of the physical age, mental age, and the social age of the first user through their browsing patterns, and thelocation-based factors influencing the user age group in order to create a predictive model of age group related behaviors. The analysis unit (104) of the system (100) correlates the economic influence of location-based factors on each user age group. Further, the system (100) facilitates understanding the social influences of each geographical location on each generation of users throughout their timeline.

[0029] For example, consider a first user in a geographical location “X” browsing through various lifestyle related webpages, wherein the browsing data of the first user is collected to interpret the user behavior and create the user profile by the profiling unit (101). The system (100) processes the user’s browsing behavior data to corelate with other age-related behaviors in the geographical location. Upon identifying the correlation in behavioral pattern, the data is further processed by the processing unit (102) to determine the age-related correlation of the browsing behavior with all the user age groups to create an age-related correlation map by the mapping unit (103). The analysis unit (104) determines if the map has similar behavior in different user age groups in the same geographical location. If majority of the user age groups in the geographical location behave in the similar manner, the analysis unit (104) directs the mapping unit (103) to create an age-location correlation map, that helps in determining the behavioral pattern of the first user.Reference numbers

Claims

ClaimsI Claim:

1. A system for determining the behavioral pattern from the browsing behavior, the system (100) comprising: a. a profiling unit (101) for profiling the browsing behavior of a first user in a user group; b. a processing unit (102) for determining the age-related correlation of the browsing behavior of the first user using the profiled browsing behavior data obtained from the profiling unit (101); c. a mapping unit (103) for facilitating creation of an age-related correlation map using at least one location-based parameter, wherein the mapping unit (103) uses the processed data obtained from the processing unit (102); d. an analysis unit (104) for predicting the behavioral pattern of the first user in a geographical location by determining the similarities in the browsing behavior using the age-related correlation map created by the mapping unit (103); e. a feedback unit (105) for providing fast feedback of the analyzed data obtained from the analysis unit (104).

2. The system (100) as claimed in claim 1 wherein, the profiling unit (101) facilitates creation of the user profile from the browsing data of the first user.

3. The method for determination of the relationship dynamics using the browsing behavior, the method (200) comprising the steps of: a. collecting the browsing data of the first user by the profiling unit (101) and facilitating creation of user profile for the first user by using the collected browsing data; b. processing the user profile by the processing unit (102) for correlating the browsing behavior of the first user with other age-related behaviors;c. correlating the browsing behavior of the first user against all the user age groups in the geographical location by the processing unit (102); d. processing the age-related correlation data of the browsing behavior of the first user with various user age groups by the processing unit (102); e. creating an age-related correlation map by the mapping unit (103), wherein the mapping unit (103) uses the age-related data of the first user; f. determining the similarities in the behavior of the various user age groups in the geographical location of the first user by the analysis unit (104) by using the age-related correlation map; g. determining if the user behavior is similar in majority of user age groups in the geographical location by the analysis unit (104); h. creating an age-location correlation map by the mapping unit (103) upon identifying the correlation by the analysis unit (104), wherein the agelocation correlation map facilitates understanding the behavioral pattern of various user age groups in correlation with their geographical location.

4. The method (200) as claimed in claim 3, the mapping unit (103) generates the age-location correlation map depending on the similarities in the behavior of various user age groups.

5. The method (200) as claimed in claim 3, wherein the analysis unit (104) facilitates understanding of the behavioral pattern of various user age groups at various geographical locations.

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