Claims:We Claim:

1. A method 500 for generating a visualized information for at least one user subject, the method comprising:
capturing, via a processor, data associated with at least one of the user subject from one or more source provided by user device 103;
cleaning, via the processor, the data by removing unwanted characters from the captured data;
performing, via the processor, language processing on the data;
transforming, via the processor, the data into a structured data;
analyzing, via the processor, the structured data in order to identify pattern and create a match between timelines of other users, wherein the timeline of user comprise at least one of user’s education, courses, achievements, skillset, job and a combination thereof;
predicting, via the processor, one or more paths of the other users associated with at least one of the user subject based on the timelines of other users, wherein the prediction of one or more paths of the other users is based on at least one of the other user’s own decision, feedback of the multiple users, other user’s decision even after recommendations by system and a combination thereof;
generating, via the processor, the visualized information on the user device 103 for at least one of the user subject, based upon the one or more paths associated with the at least one of the user subject.

2. The method of claim 1, wherein the one or more source is at least one of candidate resume, career and company website, job portal, online assessment portal, professional networking platform, social media platform, user response collected on the computer based platform and a combination thereof.

3. A system 100 for generating a visualized information for at least one user subject, the system comprising:
a processor 201; and
a memory 203 coupled with the processor 201, wherein the processor 201 is capable of executing programmed instructions stored in the memory 203 for:
capturing data associated with at least one of the user subject from one or more source provided by the user device 103;
cleaning the data by removing unwanted characters from the captured data;
performing language processing on the data;
transforming the data into a structured data;
analyzing the structured data in order to identify pattern and create a match between timelines of other users, wherein the timeline of user comprise at least one of user’s education, courses, achievements, skillset, job and a combination thereof;
predicting one or more paths of the other users associated with at least one of the user subject based on the timelines of other users, wherein the prediction of one or more paths of the other users is based on at least one of the other user’s own decision, feedback of the multiple users, other user’s decision even after recommendations by system and a combination thereof;
generating the visualized information on the user device 103 for at least one of the user subject, based upon the one or more paths associated with the at least one of the user subject.

4. The system of claim 3 further comprises a pattern matching algorithm.

5. The system of claim 4, wherein the visualized information is in form of a tree structure.

6. The system of claim 5 further analyses missing data points in the information tree of the user.

7. The system of claim 3 further comprises a machine learning based algorithm.

8. The system of claim 6 further generates one or more survey questions pertaining to the missing data points in the information tree of the user.

9. The system of claim 3 further comprises a score calculating algorithm, wherein the score calculating algorithm calculates score of one or more user subject using at least one attribute.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
(As Amended by Patents Amendment Rules-2006)

COMPLETE SPECIFICATION

(See Section 10 and Rule 13)

Title of invention:
METHOD AND SYSTEM FOR GENERATING VISUALIZED INFORMATION

APPLICANT:
IPREDICTT DATA LABS PRIVATE LIMITED
An Indian Entity having address:
B 264, Kalpataru Towers,
Kandivali East, Mumbai - 400101,
Maharashtra, India

The following specification describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
The present application does not claim priority from any other patent application.

TECHNICAL FIELD
The present invention in general relates to a career guidance. More particularly, the invention relates to a method and system for generating career-related information to users, further enabling intelligent career decision-making at every stage in their career progression.

BACKGROUND
Career is a parameter that has been always been revolving in almost every individual’s mind. There are various stages in the individual’s life where the individual has to take decisions regarding his progression in the fields such as education, professional growth, financial growth, etc. Often, such decisions relate to recurring questions about “What to do next?” or “What should I choose now?”. In the existing art, available methods involve manual collection of such information through various multiple sources like career coach, job portals, newspapers, own personal network, etc. Although a career coach, mentor, manager etc. can sometimes provide useful guidance and support to the individual in making career decisions. Such guidance can be sometimes expensive, inefficient or unavailable. Some individuals also use books, movies, lectures etc. in order to obtain an advice for making career decisions. However, these techniques are often ineffective because they are unexciting, time-consuming, and/or not tailored to the individual. Based on the information available with the individual, finally he takes a decision regarding his/her career.

The career decision taken by the individual involves risk, whether the individual has explored all possible career options available with him. Even for recruiters, there is limited data to assist in the recruitment process, while capturing the unique requirements of each organization. There is need of data analytics that can provide guidance in mapping out the future career path of the individual having a unique profile. The data analytics based system will help the individual to make better decisions about their career, irrespective of the career they are currently working in.
SUMMARY
Before the present apparatuses, methods and systems along with components related thereto are described, it is to be understood that this disclosure is not limited to the particular methods, apparatuses, systems and their arrangement as described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure but may still be practicable within the scope of the invention. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is not intended to identify essential features of the subject matter nor it is intended for use in detecting or limiting the scope of the subject matter.

In one embodiment, a method for generating a visualized information of at least one user subject is described. The method may comprise capturing, via a processor, data associated with at least one of the user subject from one or more source provided by user device. The method may comprise cleaning, via the processor, the data by removing unwanted characters from the captured data. The method may comprise performing, via the processor, language processing on the data. The method may comprise transforming, via the processor, the data into a structured data. The method may further comprise analyzing, via the processor, the structured data in order to identify pattern and create a match between timelines of other users. The timeline of user may comprise at least one of user’s education, courses, achievements, skillset, job and a combination thereof. The method may comprise predicting, via the processor, one or more paths of the other users associated with at least one of the user subject based on the timelines of other users, wherein the prediction of one or more paths of the other users is based on at least one of the other user’s own decision, feedback of the multiple users, other user’s decision even after recommendations by system and a combination thereof. The method may comprise generating, via the processor, the visualized information on the user device 103 for at least one of the user subject, based upon the one or more paths associated with the at least one of the user subject.

In another embodiment, a system for generating a visualized information of at least one user subject is described. The system may comprise a processor and a memory coupled with the processor, wherein the processor is capable of executing programmed instructions stored in the memory. The processor may capture data associated with at least one of the user subject from one or more source provided by the user device. The processor may clean the data by removing unwanted characters from the captured data. The processor may perform language processing on the data; transforming the data into a structured data. The processor may analyze the structured data in order to identify pattern and create a match between timelines of other users, wherein the timeline of user comprise at least one of user’s education, courses, achievements, skillset, job and a combination thereof. The processor may predict one or more paths of the other users associated with at least one of the user subject based on the timelines of other users. The prediction of one or more paths of the other users may be based on at least one of the other user’s own decision, feedback of the multiple users, other user’s decision even after recommendations by system and a combination thereof. The processor may generate the visualized information on the user device 103 for at least one of the user subject, based upon the one or more paths associated with the at least one of the user subject.
BRIEF DESCRIPTION OF THE DRAWINGS
The detailed description is described with reference to the accompanying Figures. In the Figures, the left-most digit(s) of a reference number identifies the Figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.

Figure 1 illustrates a network implementation 100 of the system 101 for generating visualized information, in accordance with an embodiment of the present disclosure.

Figure 2 illustrates the system 101, in accordance with an embodiment of the present disclosure.

Figure 3 illustrates a block diagram of the high-level architecture of data science platform 300, in accordance with an embodiment of the present disclosure.

Figure 4 illustrates the high-level architecture comprising of a recruiter interaction platform 401 as a connection between recruiter and the data pool 301, in accordance with an embodiment of the present disclosure.

Figure 5 illustrates a method 500 for generating visualized information, in accordance with an embodiment of the present disclosure

DETAILED DESCRIPTION
Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items.

It must also be noted that, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.

Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.

The present disclosure relates to method and system for extracting career-related information. Referring figure 1, a network implementation 100 of the system 101 for extracting career-related information is described. The system 101 may be self-serving interaction platform backed by a self-learning algorithm. The system 101 may be used for mapping a “career tree” of various career possibilities for each user by collecting data from one or more sources. The user may possess a user device 103 which is in communication with the system 101 via a network 102 as shown in Figure 1.

The user decides at various points or timelines of the career based on choices available. The system 101 may enable guidance to users through the career decisions, faced by the user at every turn of the career. The user may have to perform the tasks such as creating their professional timeline/profile which may include a visual representation of the user’s education, courses, achievements, skillset and job over the timeline. Each transition in the timeline may act as a decision point (say from graduation to first job, from the first job to post-graduation, second job details, etc.).

Further, the user on the user device 103 may see the career tree of the professional timeline of other users who may have a similar background as that of the user. The professional timeline of other users may be available in database of the system 101. The system 101 may further comprise machine learning algorithm wherein the machine may provide career path recommendations. The career path recommendations by the system 101 may be based on at least one of the other user’s own career decision, feedback of the multiple users, other user’s career decision even after recommendations by system and a combination thereof.

Based on the data collected by the system 101, a set of questions may be generated by the system 101. The set of questions may further enable the user in exploring potential career paths and may further facilitate in taking the career decision. The set of questions may be – Should I take a job in a multi-national company? Should I go for MBA in Marketing? Or should I start my own business?

The user on the user device 103 may trace and analyse the potential career paths in terms of customizable criteria such as percentage growth in salary, number of similar job opportunities, work-life balance, challenge level at work, company culture, etc. The information provided by the users may be collected, via set of survey questions, as feedback through the interaction with the computer based platform.

From recruiter’s view, the system 101 may enable the recruiter an access to data pool of users from different backgrounds in order to make more informed hiring decisions. The system 101 may facilitate the recruiters to assess employability through customized attributes that best fits to the company requirements thereby simplifying the recruiting process. Additionally, the collected data is available with the recruiters wherein the recruiters may check the users’ perception towards the company present in the market and how the users/candidates may consider the company as a part of their career journey. The system 101 may further facilitate the recruiter in creating a customized job requirement by combining different attributes of various users available by the collected data into a convenient employability score. Thus, the system 101 may facilitate a job market research for the recruiters in order to make hiring process more simple and efficient.

Although the present disclosure is explained considering that the system 101 is implemented on a server, it may be understood that the system 101 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. It will be understood that the system 101 may be accessed by multiple users through one or more user devices 103-1, 103-2…103-N, collectively referred to as user 103 hereinafter, or applications residing on the user devices 103. Examples of the user devices 103 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device and a workstation. The user devices 103 are communicatively coupled to the system 101 through a network 102.

In one embodiment, the network 102 may be a wireless network, a wired network or a combination thereof. The network 102 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 102 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 102 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

The one or more sources may be candidate resumes, career & company websites, job portals, online assessment portals, professional networking platforms, social media platforms. The system 101 may further impute missing career –related data points through personalized set of survey questions over the user device or computer-based platform.

The platform may collect career related data from a database of users and the one or more sources. The system 101 may convert the data into a uniform format. The system 101 may further employ a self-learning algorithm in order to collect missing data points and may use a pattern recognition algorithm to match available career paths. The available career paths may be collated thereby forming a data pool. The data pool may be further available to candidates in the form of creative visualization to the user on the user device 103. Similarly, the recruiter may access the data pool through an interface wherein the recruiter may calculate an employability score based on custom attributes specific to the job requirements.

In another embodiment, the method may form a data pool for career-related information collected from multiple sources. The method may comprise a data processing layer which analyses unstructured data and transforms it into a format readable by a computer. The data may be transformed by algorithms comprising text and language processing techniques. The method may comprise of a machine learning based algorithm wherein processed information is translated into a visualize-able career tree, based on similar attributes present in background of the users. Further, the algorithm may detect one or more missing data points and may trigger user-specific survey questions on the interaction platform in order to frame a professional picture of the user. The method may provide a comprehensive data platform with all possible deviations to a certain career path based on career path data collected from multiple users. The method may further facilitate a decision-making task for job seekers as well as recruiters.

Referring now to figure 2, the system 101 is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system 101 may include at least one processor 201, an input/output (I/O) interface 202 and a memory 203. The at least one processor 201 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 201 is configured to fetch and execute computer-readable instructions stored in the memory 203.

The I/O interface 202 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 202 may allow the system 101 to interact with a user directly or through the user devices 104. Further, the I/O interface 202 may enable the system 101 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 202 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 202 may include one or more ports for connecting number of devices to one another or to another server.

The memory 203 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 203 may include modules 204 and data 212.

The modules 204 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 204 may include a data collection module 205, a data processing module 206, a pattern recognition module 207, a machine learning module 208, a visualization module 209, employability score module 213, filter module 214 and other modules 215. The data processing module 206 may comprise a data cleaning and transformation module 210, a text analytics module 211, a natural language processing module 212. The other modules 215 may include programs or coded instructions that supplement applications and functions of the system 101.

The data 216, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 204. The data 214 may also include a data repository 217 and other data 218. The user profiles and the collected data may be stored in the data repository 217. The other data 218 may include data generated as a result of the execution of one or more modules in the other modules 215.

In one embodiment, at first, a user may use the user device 103 to access the system 101 via the I/O interface 202. The user may register themselves using the I/O interface 202 in order to use the system 101. Based on the registration, a user profile comprising the details of the user provided by user 103 may be received by the system 101. The user profile may comprise at least one of user’s name, age, sex, photo, education, work experience, industry and a combination thereof. By providing the details by the user device 103, the account for the user is generated and registered with the system 101. Furthermore, the user id and the password may be generated for the logging into the system 101. Based on the data provided by the user on the user device 103, various career paths may be displayed on the user device 103.

Figure 3 illustrates a block diagram of the high-level architecture of data science platform 300, in accordance with an embodiment of the present disclosure. The architecture may comprise of a candidate interaction platform 300 as a connection between user device 103 and the system 101, capable of generating a career tree and may further generate the survey questions based on missing data points. The platform 300 may feed the data into a data collection module 205, collecting the data from the one or more sources. The data may be further passed on to data processing layer, storing the data in a specific format thereby forming the data pool 301. The data pool 301 may also be stored in the data repository 217 (as shown in figure 2) of the system 101. The data pool may communicate with various modules such as the pattern recognition module 207, the machine learning module 208 and the visualization module 209. The visualization module 209 may facilitate interaction with user through the user device 103.

The interaction platform 300 may comprise of an interface, accessed via the user device 103. The platform 300 may further serve as the primary point of interaction for the system 101 in order to provide interface with the users.

The data collection module 205 may collect the data from one or more sources, wherein the one or more sources may be candidate resume, career and company website, job portal, online assessment portal, professional networking platform, social media platform, the user data captured through the interaction platform and a combination thereof.

The data cleaning and transformation module 210 may be configured for removing unwanted fields, values and outlines which are insignificant for further analysis. The analysis may be performed in later stages. Further, the collected data is transformed i.e. turning raw data into a format that is readable by the system 101. The text analytics module 211 may comprise one or more sophisticated algorithms, configured to converting unstructured forms of data into a structured format. A Natural Language Processing (NLP) 212 module may be configured to interpret implicit and subjective qualities from the users’ data.

The processed data may be further transferred to the data pool 301. The data pool 301 may be used by successive analytical modules. The pattern recognition module 207 may comprise of an algorithm to identify career patterns and create a match between timelines of different users. The custom tree selector may further, by the user, apply the filter using various criteria such as percentage growth in salary, number of similar job opportunities in the market, work-life balance, challenge level at work, company culture, etc. in order to see only specific career paths.
In the machine learning module 208, the career tree analyser may generate the missing data points in the career tree of the user based on analysis of the career paths of all the users in the data pool 301. The survey question generator may further enlist user-specific questions based on analysis of missing points on the career tree.

The visualization module 209 may generate the career tree plot for the user and displays the career tree plot on the user device 103. The set of survey questions may be displayed along with the career tree plot.

Figure 4 illustrates the high-level architecture comprising of a recruiter interaction platform 401 as a connection between recruiter and the data pool 301. The platform 401 may feed the data from the data pool 301 into an employability score module 213 and a filter module 214. The platform 400 may further select candidate profiles based on score criteria and display on the user device.

The employability score module 213 may use the data fed from the data pool 301 to display the set of candidate attributes. The attribute selector may input the selected attributes from the recruiter’s side and accordingly the score calculator may generate an employability score based on predefined logic. Based on the generated score, the filter module 214 may select the candidate, matching the selected criteria and may further display on the user device 103.

The recruiter interaction platform 401 may be compatible within organization talent management system 402 through Application Programming Interface (API), enabling employability score calculation for existing employees.

Referring now to figure 5, a method 500 generating a visualized information of at least one user subject is illustrated, in accordance with an embodiment of the present disclosure. The visualized information may be suitable for various applications. The method 500 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 500 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.

The order in which the method 500 is described is not intended to be construed as a limitation, and any number of the described methods can be combined in any order to implement the method 500 or alternate methods. Additionally, individuals may be deleted from the method 500 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 500 may be considered to be implemented in the above described system 101.

In one embodiment, the visualized information may be visualized career-related information. At step 501, the user may provide data to create a profile on the system 101 via user device 103. The data may comprise at least one of a user’s name, age, sex, photo, education, work experience, industry and a combination thereof. Further, the data may be captured, associated with at least one of the user subject from one or more source.

At step 502, the captured data may be cleaned by removing unwanted characters from the captured data followed by performing language processing over the cleaned data.

At step 503, the cleaned data may be transformed into a structured data and may be further analyzed in order to identify pattern and create a match between timelines of other users. The timeline of user may comprise at least one of user’s education, courses, achievements, skillset, job and a combination thereof.

At step 504, one or more career paths of other users may be predicted, associated with at least one of the user subject based on the timelines of other users. The one or more paths of the other users is based on at least one of the other user’s own career decision, feedback of the multiple users, other user’s career decision even after recommendations by system and a combination thereof.

At step 505, the visualized career-related information may be generated on the user device 103 for at least one of the user subject, based upon the one or more paths associated with the at least one of the user subject.

Example: At first, a candidate/user provides personal data in order to create a profile on the system 101 via the user device 103. The data comprises at least one of a user’s name, age, sex, photo, education, work experience, industry and a combination thereof. Further, the data is captured which is associated with at least one of the user subject from one or more source. The captured data is further cleaned by removing unwanted characters from the captured data followed by performing language processing over the cleaned data. The cleaned data is transformed into a structured data and may be further analyzed in order to identify pattern and create a match between timelines of other users. Further, one or more career paths of other users are predicted, associated with at least one of the user subject based on the timelines of other users. The visualized career-related information is generated and can be seen on the user device 103 in form of recommendations for the user. The visualized information show at least one of the jobs, courses, education in a timeline view along with parameters such as skills acquired, financial growth, job position, company, etc. on the user device 103. The one or more career paths are predicted, that the user can follow in the future, similar to the user timeline (with similar background in jobs and education) and may further be visualized highlighting financial growth, high opportunities, work life balance or popular choices selected by other users.

From recruiter’s view, the system further analyzes and organizes resumes of the users and machine learning may further bring insights from the data in order to create employability score. The unique candidate score will help find the best match for job descriptions and predict the probability of the candidate to join the suitable job. The system may have the advantages such as reducing process time, finding the best candidate, predict joining probability of the candidate, etc. The above factors improve productivity of the recruiter and the organization may get benefit from hiring the right talent comparatively faster.

In accordance with embodiments of the present disclosure, the method and system for generating visualized information described above may be utilized in multiple applications including but not limited to:
• Candidates, for deciding career paths and explore the possible career options.
• Recruiters, to calculate the score of candidates.
• Organizations, to calculate score of existing employees etc.

The embodiments, examples and alternatives of the preceding paragraphs or the description and drawings, including any of their various aspects or respective individual features, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.