TECHNICAL FIELD
[0001] The present disclosure relates generally to field of organizational models for resources. More particularly, the present disclosure provides a personality based human resource assessment system and method for employee and employer.
BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art. [0003] Advent of Internet and digitalization of organizational tasks and activities has created an environment where it has become imperative for employees to be digitally active. Major recruiting firms also analyze digital presence of employees being recruited. Besides this, their appraisals are also dependent on visibility on digital platforms. For example, in academia, metrics exist to assess faculty performance on the basis of their visibility on digital platforms like ResearchGate, Linkedln, etc. Therefore, there should be a way for the firms as well as employees to gauge their digital health, so that they can take necessary actions if it's not good. In addition, this can be used for prediction of job attitudes and personality, thereby, providing a basis for applicant screening at the time of selection.
[0004] Turnover rate of Information Technology (IT) companies are usually high and largely depends on human resource or employee-related efficiency and enhanced productivity. Therefore, rather than attributing the turnover rate to organizational factors, there is a need to uncover other employee-related causes of turnover. For example, is there a relationship between specific personality traits of employee and turnover. If turnover can be related to individual factors, such individuals can be screened at the timing of hiring itself to enhance turnover. [0005] Existing solutions can include identification organizational barriers which could cause attrition. However, it has been seen that despite favorable

organizational factors, employees tend to leave a firm. This becomes difficult for hiring managers, human resource department, and other administration people to decide and take decisions during screening of the employees at time of hiring. Appraisals, performance judgment, and other similar decisions for the employees requires a solution that can help the hiring managers and can predict turnover. [0006] There is a need to overcome above mentioned problems of prior art by bringing a solution that can facilitate prediction of turnover and helps hiring managers, human resource department and other similar department at time of screening the employees during hiring, appraisals, and promotions. The solution can be used as digital human resource management or assessment with help of facial analytics, job attitude and digital health of the employees. The solution can also help in predicting digital health of employee and enables in managing social media activity and job attitude along with tracking digital health for the employees.
OBJECTS OF THE PRESENT DISCLOSURE
[0007] Some of the objects of the present disclosure, which at least one
embodiment herein satisfies are as listed herein below.
[0008] It is an object of the present disclosure to provide a personality based
human resource assessment system and method that facilitates managing job
attitude, and tracking digital health of employees.
[0009] It is an object of the present disclosure to provide a personality based
human resource assessment system and method that helps ingenerate advance
warning for employees regarding leaving an organization and managers can take
initiative to warn them.
[0010] It is an object of the present disclosure to provide a personality based
human resource assessment system and method that enables in screening
applicants at time of hiring and helps mangers and hiring authority to take
decisions accordingly.
[0011] It is an object of the present disclosure to provide a personality based
human resource assessment system and method that helps hiring managers for

promotions and appraisals of employees with help of artificial neural network
based prediction model.
[0012] It is an object of the present disclosure to provide a personality based
human resource assessment system and method that enables in building an
employee database including demographics, facial features, personality score, and
work attitude score, and digital health score and creating a predictive model to
predict personality score, work attitude score and digital health score with help of
artificial neural network.
[0013] It is an object of the present disclosure to provide a personality based
human resource assessment system and method that helps in employee digital
health maintenance, and employee attitude maintenance.
SUMMARY
[0014] The present disclosure relates generally to field of organizational models for resources. More particularly, the present disclosure provides a personality based human resource assessment system and method for employee and employer
[0015] .An aspect of the present disclosure pertains to a personality based human resource management system. The system may include a device, where the device may include a set of sensors, a display, and a processor. The set of sensors may be configured to capture image of an entity and correspondingly generate a first set of signals. The display may be configured to display a questionnaire and receive a set of inputs based on displayed questionnaire and correspondingly generate a second set of signals. The processor may be operatively coupled to the display, where the processor may include a learning engine and a memory, where the memory storing instructions executable by the processor. The processor may be configured to extract facial attributes from the first set of signals and personality dimension, work attitude parameters and health parameters from the second set of signals. The processor may be configured to match the facial attributes with a dataset, where the dataset may include pre-stored image parameters. The processor may be configured to create and update a training and

testing dataset, based on the extracted personality dimension, work attitude parameters, health parameter and matched facial attributes. The processor may be configured to calculate a score for each of the personality dimension, work attitude and health based on the created training and testing dataset, where the processor may be configured to transmit each of the calculated score to the display, and where the displayed score can enables identifying entity's organizational behavior and facilitates human resource assessment. [0016] In an aspect, the set of sensors may include any or a combination of image sensor, camera, scanner, image scanner.
[0017] In an aspect, the facial attributes may include high check bone, eye color, distance between forehead and nose, distance between chin and nose, and facial symmetry.
[0018] In an aspect, the questionnaire may include a set of questions associated with the entity on age, gender, personality type, attributes including flexibility, self-esteem, j ob position, and intention to leave.
[0019] In an aspect, the personality dimension may include extraversion, agreeableness, openness, conscientiousness, and neuroticism, amiableness, where the health parameters may include chronic disease, injury, special case due to accident, and where the work attitude parameters may include any or a combination of total working hours, frequency of break, frequency of leave, project completion time, response time, frequency of meeting participation, attendance, frequency of job switch,
[0020] In an aspect, the learning engine may be configured with artificial neural network (ANN) based model, where the ANN based model can facilitate calculating score of the entity for personality dimension, work attitude, and health. [0021] In an aspect, the health parameters and personality dimensions may be identified through one or more social networks, where the processor may be communicatively coupled to the social network and facilitates indentifying the health parameters and the personality dimensions in response to the received first set of signals and the second set of signals.

[0022] In an aspect, the personality dimension and the work attitude parameters may be identified through the set of inputs entered by the entity according to the questionnaire.
[0023] Another aspect of the present disclosure pertains to a human behavior based turnover prediction device. The device may include a set of sensors, a display, and a processor. The set of sensors may be configured to capture image of an entity and correspondingly generate a first set of signals. The display may be configured to display a questionnaire and receive a set of inputs based on displayed set of questionnaire and correspondingly generate a second set of signals. The processor may be operatively coupled to the display, where the processor may include a learning engine and a memory, where the memory storing instructions executable by the processor. The processor may be configured to extract facial attributes from the first set of signals and personality dimension, work attitude. The processor may be configured to match the facial attributes with a dataset, where the dataset may include pre-stored image parameters. The processor may be configured to create and update a training and testing dataset, based on the extracted personality dimension, work attitude, health parameter and matched facial attributes. The processor may be configured to calculate a score for each of the personality dimension, work attitude and health based on the created training and testing dataset, where the processor may be configured to transmit each of the calculated score to the display. The displayed score may enable indentifying entity's organizational behavior and facilitates human resource assessment.
[0024] Another aspect of the present disclosure pertains to a personality based human resource assessment method. The method may include capturing, at set of sensors, an image of an entity, and correspondingly generate a first set of signals. The method may include displaying, at a display, a questionnaire and receive a set of inputs through the entity based on displayed questionnaire and correspondingly generate a second set of signals. The method may include extracting, at a processor, where the processor may be operatively coupled to the display, where the processor may include a learning engine and a memory, the memory storing

instructions executable by the processor, facial attributes from the first set of signals and personality dimension, work attitude parameters and health parameters from the second set of signals. The method may include matching, at the processor, the facial attributes with a dataset, where the dataset may include pre-stored image parameters. The method may include creating and updating, at the processor, a training and testing dataset, based on the extracted personality dimension, work attitude parameters, health parameter and matched facial attributes. The method may include calculating, at the processor, a score for each of the personality dimension, work attitude and health, based on the created training and testing dataset, where the processor may be configured to transmit each of the calculated score to the display, and where the displayed score may enable indentifying entity's organizational behavior and facilitates human resource assessment.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The accompanying drawings are included to provide a further
understanding of the present disclosure, and are incorporated in and constitute a
part of this specification. The drawings illustrate exemplary embodiments of the
present disclosure and, together with the description, serve to explain the
principles of the present disclosure.
[0026] The diagrams are for illustration only, which thus is not a limitation of
the present disclosure, and wherein:
[0027] FIG. 1 illustrates a block diagram of proposed personality based
human resource assessment system, in accordance with an embodiment of the
present disclosure.
[0028] FIG. 2 illustrates exemplary functional components of processing unit
of the proposed personality based human resource assessment system, in
accordance with an embodiment of the present disclosure.
[0029] FIG. 3 illustrates an exemplary view of flow diagram of the proposed
personality based human resource assessment system, in accordance with an
embodiment of the present disclosure.

[0030] FIG. 4 illustrates an exemplary method for proposed personality based human resource assessment system, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION
[0031] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details. [0032] The present disclosure relates generally to field of organizational models for resources. More particularly, the present disclosure provides a personality based human resource assessment system and method for employee and employer
[0033] FIG. 1 illustrates a block diagram of proposed personality based human resource assessment system, in accordance with an embodiment of the present disclosure.
[0034] As illustrated in FIG. 1, the proposed personality based human resource assessment system (100) (also referred to as system (100), herein) can include a device (102), a set of sensors (104), a display (106), and a processor (108) (also referred to as processing unit (108), herein). In an illustrative embodiment, the system (100) can facilitate predicting turnover for a first entity through human behavior, where the first entity can be information technology employee, but not limited to the like. In an illustrative embodiment, the system (100) can help a second entity while screening the first entity during interview, job application, promotion and similar activity, where the second entity can include any or a combination of human resource person, manager, administration people, and the like.
[0035] In an illustrative embodiment, the system (100) can facilitate identifying organizational behavior through personality dimension, work attitude parameters, digital health parameters with help of facial recognition or facial analytics along with artificial neural network based prediction model. In another

illustrative embodiment, the system (100) can help in managing human resource and can assist human resource manager, administration group, and management group during employee screening, appraisal, promotion, and similar activity. [0036] In an illustrative embodiment, the system (100) can facilitate prediction of turnover based on personality dimension, work attitude, health parameters, and social media activity of the first entity with help of artificial neural network based model. In another illustrative embodiment, a score can be calculated for each of the personality dimension, work attitude, and health parameter, where the score can help the second entity for analyzing turnover of an organization and can help the first entity to manage work attitude and social media activity.
[0037] In an illustrative embodiment, the device (102) can be an interactive kiosk, where the interactive kiosk can be configured to receive the set of inputs from the first entity. In another illustrative embodiment, the interactive kiosk can be a computing device with a display and keyboard, where the keyboard can include set of keys to enter the set of inputs from the first entity, where the set of inputs can be entered based on generated questionnaire on the display (106) of the interactive kiosk. In yet another illustrative embodiment, the questionnaire can include name, age, gender, work experience, personality type, attributes including flexibility, self-esteem, j ob position, intention to leave, and the likes. [0038] In an embodiment, the display (106) can be configured to display the questionnaire and receive the set of inputs based on displayed questionnaire and correspondingly generate a second set of signals, where the second set of signals can be transmitted to the processing unit (108). In an illustrative embodiment, after the first entity enter the set of inputs based on the generated questionnaire, the display (106) can be configured to generate the second set of signals, where the second set of signals can be in electrical form.
[0039] In an embodiment, the interactive kiosk can include the set of sensors (104) configured at a pre-determined position of the interactive kiosk, where the set of sensors (104) can facilitate capturing one or more images of the first entity. In an illustrative embodiment, the set of sensors (104) can include image sensor,

camera, scanner, and the likes. The image sensor or the camera can capture image of the first entity as the first entity is in vicinity of the interactive kiosk and can correspondingly generate a first set of signals, where the first set of signals can be transmitted to the processing unit (108). In another illustrative embodiment, the first set of signals can be in electrical form or in binary or digital form and can be transmitted to the processing unit (108).
[0040] In an embodiment, the processing unit (108) can be configured to receive the first set of signals and the second set of signals in electrical form or binary or digital form. In another embodiment, the processing unit (108) can be operatively coupled to the display (106) and the set of sensors (104) of the interactive kiosk or the device (102). In an illustrative embodiment, the processing unit (108) can include a learning engine and a memory, where the memory can storing instructions executable by the processor and configured to analyze the personality dimension, work attitude, health parameter and social media activity through the questionnaire and captured one or more images of the first entity. [0041] In an illustrative embodiment, the personality dimension can include extraversion, agreeableness, openness, conscientiousness, and neuroticism, amiableness. In another illustrative embodiment, the health parameters can include any or a combination of chronic disease, injury, special case due to accident, and the likes. In yet another illustrative embodiment, the work attitude parameters can include any or a combination of total working hours, frequency of break, frequency of leave, project completion time, response time, frequency of meeting participation, attendance, frequency of job switch, and the likes. [0042] In an illustrative embodiment, the processing unit (108) can be configured with artificial neural network (ANN) based prediction model to facilitate calculating score for personality dimension, health parameter, work attitude, and social media activity. In another illustrative embodiment, the processing unit (108) can be communicatively coupled to a network module,), such as Wi-Fi, Bluetooth, Li-Fi,. Further, the networking module can be a wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local

Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, the networking module (102) can either be a dedicated network or a shared network. The shared network can represent an association of the different types of networks that can use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.
[0043] In an embodiment, the system (100) can be implemented using any or a combination of hardware components and software components such as a cloud, a server, a computing system, a computing device, a network device and the like. Further, the computing device (102) and the first entity can interact with the processing unit (108) through plurality of networking module, such as Wi-Fi, Bluetooth, Li-Fi, or an application, that can reside in the computing device (102). In an implementation, the system (100) can be accessed by the networking module or a server that can be configured with any operating system. [0044] In an illustrative embodiment, the networking module can facilitate accessing social media network or social media platform of the first entity based on the entered name and other credentials. In another illustrative embodiment, the social media network can include any or a combination of Facebook, Instagram, Linkedin, Research Gate, Twitter, and the likes. In yet another illustrative embodiment, the processing unit (108) with help of the networking module and the ANN based prediction model can enable identifying and analyzing social media activity and personality dimension of the first entity through the social media profiles.
[0045] In an illustrative embodiment, the system (100) can relate to human resource or human capital management system for job applicant screening. In another illustrative embodiment, the system (100) can help in predicting likelihood of job applicant turnover based on a neural network. In yet another illustrative embodiment, the system (100) can be used as a human resource management dashboard to facilitate determining employee's organizational behavior including personality dimension, digital health and work attitude to predict employee's association with organization or firm.

[0046] In an illustrative embodiment, proposed Neural Network Based Predictive Human Resource Kiosk for Information Technology (IT) employee can include an electronic interface to display the questionnaire, the processing unit (108) or database server to store responses from the first entity based on the set of inputs entered. In another illustrative embodiment, the processing unit (108) can be configured with neural network-based prediction model to predict the first entity or applicant's intention of leaving job or firm or organization. In yet another illustrative embodiment, the first entity like job applicant can answer the questionnaire provided like age, gender, personality type, and other attributes like flexibility, self-esteem, job positions, intention to leave, etc. and the database server can store the answers given by the job applicants. The neural network-based prediction model can analyze the responses stored in the database server and can predict the job applicants turnover.
[0047] FIG. 2 illustrates exemplary functional components of processing unit of the proposed personality based turnover prediction system, in accordance with an embodiment of the present disclosure.
[0048] As illustrated in an embodiment, the processing unit (108) can include one or more processor(s) (202). The one or more processor(s) (202) can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) (202) are configured to fetch and execute computer-readable instructions stored in a memory (204) of the processing unit (108). The memory (204) can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like. [0049] In an embodiment, the processing unit (108) can also include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices,

storage devices, and the like. The interface(s) (206) may facilitate communication of the processing unit (108) with various devices coupled to the processing unit (108). The interface(s) (206) may also provide a communication pathway for one or more components of processing unit (108). Examples of such components include, but are not limited to, learning engine(s) (208) and database (210). [0050] In an embodiment, the learning engine(s) (208) can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the learning engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the learning engine(s) (208) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the learning engine(s) (208) may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the learning engine(s) (208). In such examples, the processing unit (108) can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to processing unit (108) and the processing resource. In other examples, the learning engine(s) (208) may be implemented by electronic circuitry. A database (210) can include data that is either stored or generated as a result of functionalities implemented by any of the components of the learning engine(s) (208).
[0051] In an embodiment, the learning engine(s) (208) can include a facial analytics unit (212), an artificial neural network based prediction unit (214), and other unit(s) (216). The other unit(s) (216) can implement functionalities that supplement applications or functions performed by the system (100) or the learning engine(s) (208).

[0052] The database (210) can include data that is either stored or generated as a result of functionalities implemented by any of the components of the learning engine(s) (208).
[0053] It would be appreciated that units being described are only exemplary units and any other unit or sub-unit may be included as part of the system (100). These units too may be merged or divided into super- units or sub-units as may be configured.
[0054] As illustrated in FIG. 2,the processing unit (108) can be configured to receive a first set of signals from a set of sensors (104), and a second set of signals from a display (106), where the set of sensors (104), and the display (106) can be associated with a device (102), in electrical form or binary form. In an illustrative embodiment, the learning engine can be configured with an artificial neural network (ANN) based model, where the ANN based model can facilitate calculating score of a first entity for personality dimension, work attitude, and health. In another illustrative embodiment, the processing unit (108) can be configured with ANN based algorithms to predict organizational behavior of the first entity for an organization, firm, and the like and association of the first entity with the organization based on calculated personality dimension score, work attitude, and digital health. In another illustrative embodiment, the ANN based algorithm can enable in predicting turnover related to the first entity in the organization.
[0055] In an illustrative embodiment, the set of sensors (104) can capture one or more images of the first entity when the first entity is in vicinity of the device (102). In another illustrative embodiment, the display (104) can be configured to display a questionnaire, where the questionnaire can include a set of questions for the first entity. The first entity can enter a set of inputs based on the questionnaire displayed on the display (104), and correspondingly the display (106) can generate the second set of signals. In yet another illustrative embodiment, the set of questions of the questionnaire can include name, age, gender, personality type, attributes including flexibility, self-esteem, job position, intention to leave, work experience, and the like.

[0056] In an embodiment, the facial analytics unit (212) can include an extraction unit, where the extraction unit can be configured to extract facial attributes from the first set of signals and personality dimension, work attitude parameters and health parameters from the second set of signals. In an illustrative embodiment, the personality dimensions can include extraversion, agreeableness, openness, conscientiousness, and neuroticism, amiableness. In another illustrative embodiment, the health parameters can include any or a combination of chronic disease, injury, special case due to accident, and the like. In yet another illustrative embodiment, the work attitude parameters can include any or a combination of total working hours, frequency of break, frequency of leave, project completion time, response time, frequency of meeting participation, attendance, frequency of job switch, and the like.
[0057] In an illustrative embodiment, the facial attributes can include any or a combination of high check bone, eye color, distance between forehead and nose, distance between chin and nose, facial symmetry, but not limited to the like. In another illustrative embodiment, the authentication unit (212) can be configured to match the facial attributes with a dataset, where the dataset can include pre-stored image parameters. In yet another illustrative embodiment, the pre-stored image parameters can be stored in the database (210), where the authentication unit (21) can match the extracted facial attributes of the first entity.
[0058] In an illustrative embodiment, the facial analytics unit (212) and the extraction unit can facilitate accessing social media profile of the first entity from the second set of signals, where the second set of signals can enable extracting personality dimension, work attitude parameters, and health parameters of the first entity. In another illustrative embodiment, after the first entity enters name, age and other set of inputs based on the questionnaire, the extraction unit can be configured to extract social media activity information pertaining to the first entity. In yet another illustrative embodiment, the health parameters, work attitude parameters, and the personality dimensions can be analyzed and identified with help of the social media activity, social media profile, and the set of inputs entered

by the first entity, where the first entity can include any or a combination of employee, job applicant, information technology employee, and the like. [0059] In an illustrative embodiment, the extraction unit and the facial analytics n unit (212) can be configured to track social media profiles like Facebook, Linkedln, ResearchGate, Twitter, Instagram, and the like to gauge level of activity including number of new posts, frequency of posts, and the like. Further facial feature analysis software configured with the facial analytics unit (212) can facilitate relating facial features with extracted social media profiles and can be fed to the ANN based prediction unit (214) or model to predict personality dimension, work attitude parameters, and health parameters. [0060] In an illustrative embodiment, the processing unit (108) can be configured with a networking module, where the networking module can enable accessing the social media profile or social media network associated with the first entity. In another illustrative embodiment, after the facial analytics unit (212) and the extraction unit matches the facial attributes and extracts the personality dimension, work attitude parameters and the health parameters respectively, the ANN based prediction unit (214) can be configured to create and update a training and testing dataset, based on the extracted personality dimension, work attitude parameters, health parameter and matched facial attributes.
[0061] In an illustrative embodiment, the artificial neural network based prediction unit (214) can build a ANN based predictive model, where the ANN based predictive model can facilitate relating facial features with personality and work attitude. In another illustrative embodiment, the ANN based predictive model can facilitate predicting personality based on facial features and can enable in predicting work attitude based on personality. In yet another illustrative embodiment, the database (210) can be SQL server 2012 database where the dataset can be stored in SQL Server tables using SQL Server Data Analysis services, but not limited to the likes.
[0062] In an illustrative embodiment, algorithm on artificial neural network can run in background to process the dataset and build a predictive model by associating individual factors with employee attitude and intention to leave. In

another illustrative embodiment, any existing employee or new applicant whose turnover needs to be predicted can enter the set of inputs on basis of questionnaire displayed on the display (106) of the device (102) like kiosk. The ANN based predictive model can process the set of inputs in the background and can return value to the kiosk display 'whether individual is likely to stay with the firm or not'. If the display (106) shows 'individual not likely to quit the firm' the job applicant can be called for further interviews.
[0063] In an illustrative embodiment, the ANN based predictive model can facilitate computing digital health of the first entity with help of the social media profile and social media activity. In another illustrative embodiment, the ANN based predictive model can build an employee database by associating facial features with employee attitude and personality dimensions. In yet another illustrative embodiment, the processing unit (108) can be configured with XML software, but not limited to the like.
[0064] In an illustrative embodiment, the ANN based prediction unit (214) can include an analyzing and calculating unit configured to calculate a score for each of the personality dimension, work attitude and digital health based on the created training and testing dataset, where the calculating unit can be configured to transmit each of the calculated score to the display (106), and where the displayed score can enable indentifying entity's behavior related to estimation and prediction of turnover. In another illustrative embodiment, the ANN based predictive model can process the set of inputs in the background and can return score of personality dimension, score of work attitude, and score of digital health to a dashboard of the kiosk.
[0065] In an illustrative embodiment, the health parameters and personality dimensions can be identified through one or more social networks, where the processing unit (108) can be communicatively coupled to the social network and facilitates identifying the health parameters and the personality dimensions in response to the received first set of signals and the second set of signals. In another illustrative embodiment, the personality dimension and the work attitude parameters can be identified through the set of inputs entered by the entity based

on the questionnaire. In yet another illustrative embodiment, the score of the personality dimension, work attitude, and the health can help a second entity like human resource people, management authority and administration people in screening during selection and performance appraisals, and can also help the first entity to manage work attitude and track digital health.
[0066] FIG. 3 illustrates an exemplary view of flow diagram of the proposed personality based human resource assessment system, in accordance with an embodiment of the present disclosure.
[0067] In an embodiment, FIG. 3 illustrates a flow diagram of the proposed personality based human resource assessment system (100). The flow diagram (300) can include a device (102) like a kiosk with a display (106) to display a questionnaire including name, age, gender, marital status to determine work attitude and personality dimension as shown in block (302). In another embodiment, the system (100) can include a set of sensors (104) associated with the device (102). The set of sensors (104) on the kiosk can recognize facial attributes as shown in block (304). The system (100) can include a processing unit (108) configured with an artificial neural network based prediction model, where the processing unit (108) can facilitate in analyzing facial features through the facial analytics and recognition of the facial features to track social media activity as shown in block (306).
[0068] In an embodiment, the processing unit (108) can be configured to create an employee database through the facial features, work attitude and the personality dimension and social media activity as shown in block (308). In another embodiment, the processing unit (108) can be configured to create a training and testing dataset to predict digital health score, work attitude score, personality dimension score through artificial neural network (ANN) based prediction model as shown in block (310). In yet another embodiment, the digital health score, personality dimension score, and the work attitude score can be displayed on a dashboard of the kiosk as shown in block (312). [0069] In an illustrative embodiment, the kiosk can use the set of sensors (104) to scan facial features and display digital health score, predictive personality

score on big five dimensions, and predictive work attitude on the dashboard. In another illustrative embodiment, the ANN based prediction model can enable in building an employee database, where the employee database can include facial features, work attitude and demographics associated with a first entity like job applicant, employee, and the like. In yet another illustrative embodiment, the ANN network can build the ANN based predictive model to relate facial features with personality and work attitude, predict personality based on facial features, and can predict work attitude based on personality.
[0070] In an illustrative embodiment, kiosk can be configured with facial recognition tool, where the facial recognition tool can facilitate tracking social media profiles of the first entity like Facebook, Instagram, Linkedin, ResearchGate, Twitter, and the like to gauge level of activity like number of new post, frequency of post, and the like. Further, facial feature analysis software generated facial features as output which can be fed to the ANN model to predict big five personality score and score on work attitudes, and the score can be displayed on Kiosk dashboard. In another illustrative embodiment, a SQL server 2012 database can be used to store employee database in SQL Server tables using SQL Server Data Analysis services.
[0071] In an illustrative embodiment, the kiosk component can include notebook computer, facial feature sensor, mouse, keyboard, printer, internet connection, but not limited to the like. The kiosk sensor can scan the facial features and using facial recognition tool and crawlers can track social media profiles of the first entity, where the social media profiles can be fed to an algorithm which can compute social media presence of the first entity and can compute a digital health score. In another illustrative embodiment, the facial features can be fed as in input to an algorithm on artificial neural network which runs in background to process the dataset and build a predictive model (associating facial features with employee attitude and personality dimensions). [0072] In an illustrative embodiment, the ANN based predictive model can process the input in the background and can return score of personality big five dimensions, score of job attitudes, and score of digital health on the dashboard of

the kiosk. In another illustrative embodiment, the system (100) can help human
resource manager, department, hiring manger, administration people, and the like
in screening the first entity during interview selection, appraisal, and hiring. In yet
another illustrative embodiment, the system (100) can help the first entity to
manage work attitude and track digital health In yet another illustrative
embodiment, the system (100) can help in employee digital health maintenance,
and employee attitude assessment along with personality dimension assessment.
[0073] FIG. 4 illustrates an exemplary method for proposed personality based
human resource assessment system, in accordance with an embodiment of the
present disclosure.
[0074] In an embodiment, FIG. 4 illustrates a personality based human
resource assessment method (400). The method (400) can include a step (402) of
capturing, at set of sensors (104), an image of an entity, and correspondingly
generate a first set of signals.
[0075] In an embodiment, the method (400) can include a step (404) of
displaying, at a display (106), a questionnaire and receive a set of inputs through
the entity based on displayed e and correspondingly generate a second set of
signals.
[0076] In an embodiment, the method (400) can include a step (406) of
extracting, at a processor (106), where the processor (108) can be operatively
coupled to the display (106), and the set of sensors (104),where the processor
(108) can include a learning engine and a memory, and where the memory can
store instructions executable by the processor, facial attributes from the first set of
signals and personality dimension, work attitude parameters and health parameters
from the second set of signals.
[0077] In an embodiment, the method (400) can include a step (408) of
matching, at the processor (106), the facial attributes with a dataset, where the
dataset can include pre-stored image parameters.
[0078] In an embodiment, the method (400) can include a step (410) of
creating and updating, at the processor (108),a training and testing dataset, based

on the extracted personality dimension, work attitude parameters, health parameter and matched facial attributes.
[0079] In an embodiment, the method (400) can include a step (412) of calculating, at the processor (108),a score for each of the personality dimension, work attitude and health based on the created training and testing dataset, where the processor (108) can be configured to transmit each of the calculated score to the display (106), and where the displayed score can enable indentifying entity's organizational behavior and human resource assessment.
[0080] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
ADVANTAGES OF THE PRESENT DISCLOSURE
[0081] The present disclosure provides a personality based human resource assessment system and method that facilitates managing job attitude, and tracking digital health of employees.
[0082] The present disclosure provides a personality based human resource assessment system and method that helps ingenerate advance warning for employees regarding leaving an organization and managers can take initiative to warn them.
[0083] The present disclosure provides a personality based human resource assessment system and method that enables in screening applicants at time of hiring and helps mangers and hiring authority to take decisions accordingly. [0084] The present disclosure provides a personality based human resource assessment system and method that helps hiring managers for promotions and appraisals of employees with help of artificial neural network based prediction model.

[0085] The present disclosure provides a personality based human resource assessment system and method that enables in building an employee database including demographics, facial features, personality score, and work attitude score, and digital health score and creating a predictive model to predict personality score, work attitude score and digital health score with help of artificial neural network.
[0086] The present disclosure provides a personality based human resource assessment system and method that helps in employee digital health maintenance, and employee attitude maintenance.

We Claim:
1. A personality based human resource assessment system (100) comprising:
a device (102) including:
a set of sensors (104) configured to capture image of an entity and correspondingly generate a first set of signals;
a display (106) configured to display a questionnaire and receive a set of inputs based on displayed questionnaire and correspondingly generate a second set of signals, and
a processor (108) operatively coupled to the set of sensors (104), and the display (106),wherein the processor (108) includes a learning engine and a memory, wherein the memory storing instructions executable by the processor to:
extract facial attributes from the first set of signals and personality dimension, work attitude parameters and health parameters from the second set of signals;
match the facial attributes with a dataset, wherein the dataset includes pre-stored image parameters;
create and update a training and testing dataset, based on the extracted personality dimension, work attitude parameters, health parameter and matched facial attributes, and
calculate a score for each of the personality dimension, work attitude and digital health based on the created training and testing dataset, wherein the processor is configured to transmit each of the calculated score to the display, and wherein the displayed score enables indentifying entity's organizational behavior and facilitates human resource assessment.
2. The personality based human resource assessment system (100) as claimed in claim 1, wherein the set of sensors (104) include any or a combination of image sensor, camera, scanner, image scanner.
3. The personality based human resource assessment system (100) as claimed in claim 1, wherein the facial attributes include high check bone, eye color,

distance between forehead and nose, distance between chin and nose, and facial symmetry.
4. The personality based human resource assessment system (100) as claimed in claim 1, wherein the questionnaire includes set of questions associated with the entity on name, age, gender, personality type, attributes including flexibility, self-esteem, j ob position, and intention to leave.
5. The personality based human resource assessment system (100) as claimed in claim 1, wherein personality dimension includes extraversion, agreeableness, openness, conscientiousness, and neuroticism, amiableness, wherein the health parameters include any or a combination of chronic disease, injury, special case due to accident, and wherein the work attitude parameters include any or a combination of total working hours, frequency of break, frequency of leave, project completion time, response time, frequency of meeting participation, attendance, and frequency of job switch.
6. The personality based human resource assessment system (100) as claimed in claim 1, wherein the learning engine is configured with artificial neural network (ANN) based model, wherein the ANN based model facilitates calculating score of the entity for personality dimension, work attitude, and health.
7. The personality based human resource assessment system (100) as claimed in claim 1, wherein the health parameters and personality dimensions are identified through one or more social networks, wherein the processor (108) is communicatively coupled to the social network and facilitates identifying the health parameters and the personality dimensions in response to the received first set of signals and the second set of signals.
8. The personality based human resource system (100) as claimed in claim 1, wherein the personality dimension and the work attitude parameters are identified through the set of inputs entered by the entity according to the questionnaire.
9. A personality based human resource assessment device (102) comprising:

a set of sensors (104) configured to capture image of an entity and correspondingly generate a first set of signals;
a display (106) configured to display a questionnaire and receive a set of inputs based on displayed questionnaire and correspondingly generate a second set of signals, and
a processor (108) operatively coupled to the set of sensors (104), and the display (106),wherein the processor (108) includes a learning engine and a memory, wherein the memory storing instructions executable by the processor (108)to:
extract facial attributes from the first set of signals and personality dimension, work attitude
match the facial attributes with a dataset, wherein the dataset includes pre-stored image parameters
create and update a training and testing dataset, based on the extracted personality dimension, work attitude, health parameter and matched facial attributes
calculate a score for each of the personality dimension, work attitude and health based on the created training and testing dataset, wherein the processor is configured to transmit each of the calculated score to the display, and wherein the displayed score enables indentifying entity's organizational behavior and facilitates human resource assessment. 10. A personality based human resource assessment method (400) comprising:
capturing, at set of sensors (104), an image of an entity, and correspondingly generate a first set of signals
displaying, at a display (106),a questionnaire and receive a set of inputs through the entity based on displayed questionnaire and correspondingly generate a second set of signals;
extracting, at a processor (108), wherein the processor(108) is operatively coupled to the display (106),wherein the processor (108) includes a learning engine and a memory, and wherein the memory storing instructions executable by the processor, facial attributes from the first set of signals and

personality dimension, work attitude parameters and health parameters from the second set of signals;
matching, at the processor (108),the facial attributes with a dataset, wherein the dataset includes pre-stored image parameters;
creating and updating, at the processor (108),a training and testing dataset, based on the extracted personality dimension, work attitude parameters, health parameters and matched facial attributes, and
calculating , at the processor (108), a score for each of the personality dimension, work attitude and digital health based on the created training and testing dataset, wherein the processor (108) is configured to transmit each of the calculated score to the display (106), and wherein the displayed score enables indentifying entity's organizational behavior and facilitates human resource assessment.