WO2020165717A1

WO2020165717A1 - Systems and methods for career/profession recommendation

Info

Publication number: WO2020165717A1
Application number: PCT/IB2020/051001
Authority: WO
Inventors: Gaurav Dubey
Original assignee: Gaurav Dubey
Priority date: 2019-02-12
Filing date: 2020-02-10
Publication date: 2020-08-20

Abstract

The present disclosure relates to methods and systems for one or more profession recommendations of a user. The system includes a wearable device worn by the user to acquire/determine one or more biophysiological parameters associated with the user. The wearable device transmits the acquired biophysiological parameters to a server arrangement through a portable device. Further, the server arrangement processes the one or more patterns from the acquired biophysiological parameters through various stages such as segmentation, feature extraction, patterns generation and patterns match. Based on the matched pattern, a notification related to profession recommendation can be transmitted.

Description

SYSTEMS AND METHODS FOR CAREER/PROFESSION RECOMMENDATION

FIELD OF THE INVENTION

The present invention relates to systems and methods for career/profession recommendation; and more particularly to systems and methods for determining profession recommendation suitable career/profession of a user.

BACKGROUND

Generally, a profession is related to a career path or occupation which people opt to be part of a society. The profession is chosen through trainings and skills which are developed over the period. The profession is opted by the people for various reasons, one being earning compensations, among many.

People choose the profession according to their interest or due to the surrounding forces which compel one to get inclined towards a specific profession. As the profession significantly impacts the social life as well as professional life, appropriate choice of profession is highly required. For example, a person who can be a good sportsman, if not properly nurtured, can opt a medical profession. Similarly, a good lawyer can opt for a food retail chain business. Opting wrong or inappropriate profession can mislead severely in several ways in the professional’s life such as loss of effort, time, capital etc. Moreover, wrong choice is made because there is no proper guidance which can pave the way to choose profession per se. Rather opting appropriate profession at right time or in early stage helps the professional to sharp the development skills timely and plan entire journey in better way.

Currently, there is no solution which efficiently and objectively suggests the best suitable professional career path by using brain wave signals to the person. Thus, there remains a need for further contributions in this area of technology. More specifically, a need exists in the area of technology for suggestions of appropriate profession for the user.

SUMMARY

The present invention relates to systems and methods for career/profession recommendation; and more particularly to systems and methods for determining profession recommendation suitable career/profession of a user. The disclosure relates to a system for one or more profession recommendations of a user, the system comprising: a wearable device worn by the user, wherein the wearable device is configured to acquire one or more biophysiological parameters associated with the user; a portable device communicatively coupled to the wearable device, wherein the portable device is configured to receive the determined one or more biophysiological parameters, from the wearable device; a server arrangement communicatively coupled to the portable device, wherein the server arrangement comprises one or more memory units and one or more processors; wherein the one or more memory units includes a database, to store one or more patterns which are associated with one or more profession recommendations; wherein the one or more processors are operable to execute one or more routines, wherein the one or more routines include: a biophysiological parameters receive engine, wherein the biophysiological parameters data receive engine is operable to receive the acquired one or more biophysiological parameters associated with the user from the portable device; a biophysiological parameters segmentation engine, wherein biophysiological parameters segmentation engine is operable to segment the received one or more biophysiological parameters in one or more blocks; a feature extraction engine, wherein feature extraction engine is operable to extract one or more features from the blocks; a pattern generation engine, wherein the pattern generation engine is operable to generates one or more patterns based on the extracted features; a pattern match engine is operable to match the determined one or more patterns with the stored patterns from the database, and identify one or more profession recommendations based on the matched patterns; and a notification engine is operable to transmit the identified one or more profession recommendation information.

In an embodiment, the wearable device can include one or more sensors, said sensors being selected from the group consisting of an electroencephalography (EEG) sensor, an electrocardiography (ECG) sensor, a pulse oximeter, a body temperature sensor, a galvanic skin response (GSR) sensor, a electromyogram (EMG) sensor, a electrooculography (EOG) sensor, an accelerometer, a magnetometer, a gyroscope, a global positioning system (GPS) sensor, a glucose sensor, a blood pressure sensor, a sweating sensor an eye tracking sensor, a facial expression monitoring sensor, a body movement sensor and combination thereof.

In an embodiment, the wearable device can be configured to acquire the data, selected from the group of an electroencephalogram (EEG) parameter, an electrocardiogram (ECG), an electromyogram (EMG) parameter, a muscle tone, a galvanic skin response (GSR), a mechanomyogram (MMG), an electrooculogram (EOG), a magnetoencephalogram (MEG), a body temperature, a body movement, a pulse/heart rate, a blood oxygen saturation level, the tidal volume, an eye movement, a skin conductance, a blood lactate level, a blood pressure and combination of thereof.

In an embodiment, the profession recommendation can be selected from the group of a sportsman, an academician, a musician, a doctor, a defense man, a fashion personality, and a politician.

In an embodiment, the database can be operable to store and update the data associated with the patterns pertains to the profession recommendation.

In an embodiment, the disclosure relates to a method for profession recommendation for a user, the method comprising: receiving, at a server, one or more acquired biophysiological parameters from a portable device, wherein the portable device is operatively coupled with the wearable device, wherein the wearable device is worn by the user; determining one or more patterns based on the received biophysiological parameters; matching one or more determined patterns with the pre-stored patterns stored in a database, wherein the database is associated with the server; identifying one or more profession recommendations based on the matched patterns; and notifying the identified one or more profession recommendation.

OBJECT OF THE INVENTION

An object of the present disclosure is to overcome one or more disadvantages associated with conventional systems.

An object of the present disclosure is to identify and recommend a suitable profession to a user.

An object of the present disclosure is to assist a user to choose appropriate career path, profession recommendation.

DESCRIPTION OF EMBODIMENTS

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those skilled in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers. Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

FIG. 1 is an architecture for profession recommendation, in accordance with an embodiment of the present disclosure;

FIG. 2 is an exemplary system for profession recommendation, in accordance with an embodiment of the present disclosure;

FIG. 3 is an illustration of detailed architecture for profession recommendation, in accordance with an embodiment of the present disclosure;

FIG. 4 is an exemplary profession recommendation database, in accordance with embodiments for implementing the system

FIG. 5 is illustration of an exemplary flow diagram depicting steps involved in profession recommendation, in accordance with an embodiment of the present disclosure.

FIG. 6 is illustration of an exemplary profession recommendation, in accordance with an embodiment of the present disclosure.

FIG. 7 is illustration of an exemplary detailed profession recommendation, in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

DETAILED DESCRIPTION

The following is a detailed description of embodiments of the invention depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the invention. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.

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.

Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and/or by human operators.

V arious methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.

If the specification states a component or feature“may”,“can”,“could”, or“might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic. As used in the description herein and throughout the claims that follow, the meaning of “a,”“an,” and“the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of“in” includes“in” and“on” unless the context clearly dictates otherwise.

Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this invention will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. V arious modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). Also, the terminology and phraseology used is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed. For purpose of clarity, details relating to technical material that is known in the technical fields related to the invention have not been described in detail so as not to unnecessarily obscure the present invention.

Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named element.

Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.

All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g.,“such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention. V arious terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.

The disclosure relates to a system for one or more profession recommendations of a user, the system comprising: a wearable device worn by the user, wherein the wearable device is configured to acquire one or more biophysiological parameters associated with the user; a portable device communicatively coupled to the wearable device, wherein the portable device is configured to receive the determined one or more biophysiological parameters, from the wearable device; a server arrangement communicatively coupled to the portable device, wherein the server arrangement comprises one or more memory units and one or more processors; wherein the one or more memory units includes a database, to store one or more patterns which are associated with one or more profession recommendations; wherein the one or more processors are operable to execute one or more routines, wherein the one or more routines include: a biophysiological parameters receive engine, wherein the biophysiological parameters data receive engine is operable to receive the acquired one or more biophysiological parameters associated with the user from the portable device; a biophysiological parameters segmentation engine, wherein biophysiological parameters segmentation engine is operable to segment the received one or more biophysiological parameters in one or more blocks; a feature extraction engine, wherein feature extraction engine is operable to extract one or more features from the blocks; a pattern generation engine, wherein the pattern generation engine is operable to generates one or more patterns based on the extracted features; a pattern match engine is operable to match the determined one or more patterns with the stored patterns from the database, and identify one or more profession recommendations based on the matched patterns; and a notification engine is operable to transmit the identified one or more profession recommendation information.

In an embodiment, the disclosure relates to a method for profession recommendation for a user, the method comprising: receiving, at a server, one or more acquired biophysiological parameters from a portable device, wherein the portable device is operatively coupled with the wearable device, wherein the wearable device is worn by the user; determining one or more patterns based on the received biophysiological parameters; matching one or more determined patterns with the pre-stored patterns stored in a database, wherein the database is associated with the server; identifying one or more profession recommendations based on the matched patterns; and notifying the identified one or more profession recommendation. Referring to FIG. 1, there is shown an architecture 100 for profession recommendation, in accordance with an embodiment of the present disclosure. The system 100 comprises the wearable device 104, which is worn by the user 102. The wearable device 104 is a non-invasive device that can associate an attachment means for enabling convenient attachment thereof, to a body part (such as head) of the user 102. For example, the attachment means can be implemented as a strap, a Velcro® fasteners coupled to ends of the strap and/or one or more screws/rivets/clamping means/buttons/snap fittings.

In an embodiment, such wearable device 104 can include the one or more sensors 106a... n, one or more microprocessors/processors/microcontrollers, one or more memory units, a data transmission means 108, a power source, and other electronic components, wherein all the aforementioned elements/components can be operatively coupled with each other.

In an embodiment, upon wearing the wearable device 104 by the user 102 on the body part, the configured sensors 106a...n determine one or more biophysiological parameters (interchangeably referred as biophysiological parameters data) of the user 102. The determined biophysiological parameters can be including but not limited to, the electroencephalogram (EEG) parameter, the electrocardiogram (ECG), the electromyogram (EMG) parameter, the galvanic skin response (GSR), the mechanomyogram (MMG), the electrooculogram (EOG), the magnetoencephalogram (MEG), the body temperature, the body movement, the pulse/heart rate, the blood oxygen saturation level, the tidal volume, the eye movement, the skin conductance, the blood lactate level, the muscle tone, the blood pressure and so forth. Further, for the determination of each type of aforementioned parameters, one or more sensors 106a... n can be employed. For an instance, the electroencephalography (EEG) sensor 106a is used to determine EEG parameter, the electrocardiography (ECG) sensor 106b is used to determine ECG parameter, the pulse oximeter 106c is used to determine pulse oximeter parameter, the galvanic skin response sensor 106d is used to determine GSR parameter, the body movement sensor 106e is used to determine the body movement parameter. Additionally, more sensors can be configured in the wearable device 104 such as, the body temperature sensor, the electromyogram (EMG) sensor, the electrooculography (EOG) sensor, the magnetometer, the gyroscope, the global positioning system (GPS), the glucose sensor, the blood pressure sensor, the sweating sensor, the eye tracking sensor and the facial expression monitoring sensor.

The sensors 106a... n can be operatively coupled with the one or more processors/microprocessors. It should be understood that microprocessor may also comprise other forms of processors or processing devices, such as a microcontroller, or any other device that can be programmed to execute instructions to perform the functionality described herein.

In an embodiment, the data transmission means 108, configured in the wearable device 104, transmits the determined biophysiological parameters to the portable device 112 and/or other components of the system 100, through the communication means 110, wherein the communication means 110 can be any or a combination of: a USB connection, a Bluetooth® network, an infrared network, a wireless network, an internet, a wired network, a telecommunication network, WIFI® network, a LIFI® network, or a ZigBee® network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a wireless local area network (WLAN), a wireless wide area network (WWAN), a wireless metropolitan area network (WMAN), a WiFi® network, the internet, a visible light communication (VLC) network, a third-generation (3G) telecommunication network, a fourth-generation (4G) telecommunication network, a fifth-generation (5G) telecommunication network, or a worldwide interoperability for microwave Access (WiMAX®) network. Furthermore, the communication means 110 can be a short range communication and/or a long range communication. Accordingly, the portable device 112, communicatively coupled to the wearable device 104, receives the transmitted biophysiological parameters, via the communication means 110.

In one of the embodiment, the server arrangement 114 is configured to receive the biophysiological parameters. The server arrangement 114 comprises one or more memory units and one or more processors (not shown in the figure). The one or more memory units can comprise the database 116 which includes pre-stored one or more patterns wherein, any or combination of the pre-stored patterns are associated with the profession recommendation related information.

Additionally, upon receiving the biophysiological parameters from the portable device 112, the server arrangement 114 analyses the received biophysiological parameters to generate the one or more patterns. In an embodiment, the generated one or patterns are compared/matched against the pre-stored patterns which are pre-stored in the database 116. Based on the comparison, the profession recommendation can be suggested to the user 102.

In an embodiment, the identified one or more profession recommendations related information is transmitted to the portable device 112 and/or to the wearable device 104 and/or to an external network, through the aforementioned short range communication and/or long range communication. In an embodiment, the server arrangement 114 can store the profession recommendation information.

In an embodiment, the pre-stored patterns can be in any form such as a graph, a chart, a picture, a textual (i.e. a numerical, an alphanumerical, a special character), a sound.

It will be appreciated that throughout the disclosure, the communication means 110 utilized anywhere in the system can be identical or different in nature. Such communication means 110 can transmit data at any speed i.e. high speed communication or a low speed communication. For example, the high speed communication may include 20gbps; low speed communication may include 500kbps. All the communication among the devices can be performed in a secure manner to avoid any malicious attack. Although the present disclosure describes the identification of one profession recommendation, however, in a similar manner, more than one profession recommendation can also be determined and all such embodiments are within the scope of the present disclosure.

In one embodiment, the wearable device 104 can be of any shape or size, such as a cap, a helmet, a hat, a headband, a patch, an arm band, a wrist band etc.

In an embodiment, the wearable device 104 is configured to determine the biophysiological parameters corresponding to the user 102 in a runtime basis or in a polling fashion i.e. in a predetermined time intervals. In operation of the system 100, the wearable device 104 may display the profession recommendation related information of the user 102.

In an embodiment, the portable device 112 can be any computing device including, but not limited to, a mobile phone, a smartphone, a personal digital assistant, a smart-watch, a tablet computer, a personal computer (PC), a palmtop, a notebook computer, a pocket computer, a desktop computer, a laptop computer, a personal digital assistant (PDA) and so forth. Moreover, in operation of the system 100, the portable device 112 functions as a communication link between the wearable device 104 and the server arrangement 114.

Referring to FIG. 2, there is shown an exemplary system 200 for profession recommendation, in accordance with an embodiment of the present disclosure. The system 200 can include the wearable device 104, which utilizes the sensors 106a-n to determine the biophysiological parameters of the user 102. The wearable device 104 is operatively coupled with the portable device 112. In an embodiment, the portable device 112 receives the biophysiological parameters, through the communication means 110, from the wearable device 104. The portable device 112 is operable to receive and/or stores and/or transmits the biophysiological parameters such as the EEG data 212-A, the ECG data 212-B, the pulse oximeter data 212-C, the temperature data 212-D, the GSR data 212-E, and the body movement data 212-F to the server arrangement 114.

The server arrangement 114 comprises one or more memory units which can comprise the database 116 and the computer/machine executable various engines such as: the biophysiological parameters receive engine 218-A, the biophysiological parameters segmentation engine 218-B, the feature extraction engine 218-C, the pattern generation engine 218-D, the pattern match engine 218-E and the notification engine

218-F. In an embodiment, the biophysiological parameters receive engine 218-A, which when executed by one or more processors, receives the one or more determined biophysiological parameters, transmitted from the portable device 112. The biophysiological parameters can be received either in real time or in a polling fashion i.e. in predetermined time intervals. The biophysiological parameters can be received by utilization of an electronic receiver/transceiver. The received biophysiological parameters can be in form of a number, a text, an image, electronic signals etc. The received biophysiological parameters are further processed by the biophysiological parameters segmentation engine 218-B.

The biophysiological parameters segmentation engine 218-B, which when executed by the one or more processors, segments or splits the received biophysiological parameters into multiple blocks. The segmentation can be performed through any mechanism. Exemplary mechanism can be any or a combination of: a time lapse window, a frequency deviation, a standard deviation, and the like. For example, in case of the time lapse window, the received ECG parameter data, recorded for 30 seconds, can be segmented in 10 blocks of 3 second time window without any overlapping. Alternatively, ECG parameter data can be segmented in 15 blocks of 3 second time window with the over lapping region. The segmentation process can vary for each of the received biophysiological parameters type. As another example, in case of the standard deviation, mechanism allows the segmentation of the received biophysiological parameters when there is a variation in signal. In case of the frequency deviation, mechanism allows the segmentation of the received biophysiological parameters, based on a variation in the frequency of the biophysiological parameters (i.e. for EEG parameter can be segmented in blocks of 0 -4 Hz, 4 - 8 Hz, 8 -12 Hz, 12- 40 Hz and 40 - 100 Hz). The segmented blocks are further evaluated by the feature extraction engine 218-C.

The feature extraction engine 218-C, which when executed by one or more processors, extracts one or more features from the segmented/fragmented blocks. The feature extraction engine 218-C can include one or more mechanisms such as fuzzy logic, an artificial neural network (ANN), a genetic algorithm (GA), a support vector machines (SVM), an autoregressive (AR), a wavelet transform (WT), an eigenvector, a fast fourier transform (FFT), linear prediction (LP), a machine learning (ML), a principal component analysis (PCA) and an independent component analysis (ICA). The representative features which can be extracted from the fragmented biophysiological parameters are summarized in the below table. In an embodiment, artificial intelligence, machine learning and/or other aforementioned mechanisms can be utilized for the automatic profession recommendations. In an embodiment, the previous data can be stored throughout in the memory unit or just to save the memory, previously stored data can be erased from the memory in a periodic manner.

The feature extraction engine 218-C is communicatively coupled to the feature pattern generation engine 218-D for the further analysis.

In an embodiment, the server arrangement 114 can comprise a feature validation engine which when executed by the one or more processor, validate the extracted features. The validation is performed by assigning scores or weighs to the extracted features, wherein such scores are assigned by using one or more artificial intelligence mechanisms. The score can be assigned to the features based on similarity, dissimilarity, probability or other known methods in the prior arts. In an embodiment, the feature validation engine can assign a similarity score (SS), a dissimilarity score (DS), a fitness score (FS) and a probability score (PS). The assigned scores can be numeric, alphabet or alphanumeric in nature. The extracted features have one or more scores having the value which is outside of a predetermined threshold value would be discarded. The validations of extracted features are performed to improve the performance of the system 200 for the career profession recordation. Exemplary assigned scores for different features are summarized in below table.

In an embodiment, the feature validation engine is communicatively coupled to the feature pattern generation engine 218-D.

The pattern generation engine 218-D, which when executed by the one or more processor, generate one or more patterns by utilization of the extracted features. The pattern is multidimensional array of extracted features. The patterns can be in form of a picture, a numeric (with one or more special characters or without one or more special characters), an alphanumerical (with one or more special characters or without one or more special characters), an encrypted form. The pattern generation engine 218-D generates different permutation and/or combination of all extracted features. The pattern generation includes searching for an arrangement of features that can provide meaningful information towards the profession recommendation.

The pattern match engine 218-E, which when executed by the one or more processors, matches one or more generated patterns against the pre-stored patterns which are stored in the database 116 and based on the identified matched patterns, profession recommendation related information is determined. The pattern match engine 218-E can utilize any known algorithm for pattern marching, such algorithm can be selected from: a Naive string-search algorithm, a Rabin-Karp algorithm, a Knuth-Morris-Pratt algorithm, a Boyer-Moore string-search algorithm, a Bitap algorithm (shift-or, shift-and, Baeza-Yates- Gonnet), a Two-way string-matching algorithm, a Backward Non-Deterministic Dawg Matching (BNDM), a Backward Oracle Matching (BOM).

In an embodiment, the pattern match engine 218-E is operable to retrieve the profession recommendation related information. The pattern match engine 218-E can transmit the retrieved profession recommendation related information to the notification engine 218-F.

The notification engine 218-F is operable to transmit the alert/notification to one or more the portable devices 112 and/or other computing devices and/or other external network, though the communication means 110. In one embodiment the system 200 supports different types of the alert/notification methods such as a SMS, a MMS, an email, a voice mail, a buzzer, a phone call, a light glow, a siren, a tone, a bell or other known alert/notification methods. The portable devices 112 can be associated with the user 102 or a third party such as parents, school, mentor etc. In an embodiment, the system 200 performs acquiring/managing/updating huge amount of data related to brain and/or any phase of profession recommendation. The system 200 may also employ plurality of digital signal processing mechanisms. The digital signal processing mechanisms enable usage of digital processing, through one or more specialized digital signal processors, to execute multiple signal processing operations. The digital signal processing (DSP) is primarily utilized to analyse and modify the signals to optimize or improve efficiency or performance of the system 200. Various mathematical and computational algorithms for analog and digital signals can be employed to produce a signal that's of higher quality than the original signal, such digital signal processing forms part of the profession recommendation.

Referring to FIG. 3, there is shown detailed architecture for profession recommendation, in accordance with an embodiment of the present disclosure. The system 300 comprises the non-invasive device 104, which is worn by the user (not shown). In an embodiment, such wearable device 104 can include the one or more electroencephalography (EEG) sensors 106a to determine EEG parameters of the user. The determined parameters can be transmitted to the portable device 112 and/or other components of the system 300, through the communication means 110. In an embodiment, the portable device 112 can transmits the biophysiological parameters data to the one or more other components of the system 100 such as the server arrangement 114.

In one of the embodiment, the server arrangement 114 is configured to receive the biophysiological parameters 302, through the biophysiological parameters receive engine 218-A. In an embodiment, the received parameters 302 can be stored in the memory units. The biophysiological parameters segmentation engine 218-B processes the received data 302 and segments the received data in multiple frequencies such as an alpha band 304a, a beta band 304b, a gamma band 306c, a theta band 304d and a delta band 304e. The segmented data 304a, 304b, 304c, 304d and 304e is further analysed by the feature extraction engine 218-C. The feature extraction engine 218-C can extract the multiple features 306 based on the different band data. Exemplary extracted features can be an event-related synchronization (ERS) and an event-related desynchronization (ERD). In an embodiment ERS and/or ERD can be calculated based on fixed bands and fixed widths (FBFW) algorithm, individually defined bands and fixed widths (IBFW) algorithm, individually defined bands and widths method (IBIW) algorithm, and most significant region method (MSR) algorithm.

In an embodiment, the alpha and beta ERD/ERS was calculated by using the standard formula:

wherein the E denotes the alpha or beta band density during an event period and R denotes the alpha/beta band density during a baseline period.

In an embodiment, the pattern generation engine 218-D can receive the extract multiple features 306 and generates patterns 308, wherein the patterns can be 3D topographic maps 308a...308n based the received of the ERD/ERS data. In an embodiment, the pattern generation engine 218-D can utilize a spline interpolating function to generate 3D maps 308.

In an embodiment, the pattern database 116 can include one or more pre-stored 3D topographical maps, wherein the pre-stored 3D maps correspond to different types of professions such as sports, academics, politics, social service, entrepreneurs, security personals (including a police, army, an air force, a military, and an arm force), a banker, a pilot, a cook, a doctor, a surgeon, a lawyer, a public administrative officer, a telephone operator, a delivery boy, an actor, an film director, a singer, a musician, a magician, a farmer, a driver, a teacher, a guide, a professor, an engineer, a tour operator, a dentist, an advertising guru, an advocate, an agronomist, an air hostess, an air traffic controller, an anchor, an auditor, an automobile engineer, a chartered accountant, a chemist, a choreographer, a dietician, an astronomer, a fashion designer, a geologist, a geophysicist, a hacker, a jeweller, a mathematician, a nurse, a painter, a physicist, a radio jockey, a social worker, a web designer, a journalist, etc. In an embodiment, the database 116 can include additional information pertain to the career profession such as in case of engineer, which branch/stream is most optimum: electrical engineering, computer science engineering, mechanical engineering etc. The pattern match engine (not shown in the figure) can match generated 3D maps 308 against the pre-stored 3D maps 116a... 116n (collectively can refer as 116a) which are stored in the database 116. The pattern match engine can include known algorithms such as a stereo global matching algorithm, a scale invariant feature transform (SIFT) algorithm, shape context algorithm, harris corner algorithm, speeded up robust features (SURF) algorithm, a symmetric dynamic programming stereo (SDPS) matching algorithm, a stereo matching local algorithm, a gradient location-orientation histogram (GLOH) algorithm, a correlation matching algorithm, a pixel matching algorithm to match the generated 3D maps 308 with the pre-stored 3D maps. In embodiment, the pattern match engine can include a resolution converter to adjust a resolution of the generated 3D maps 308 with the pre-stored 3D maps 116a. In embodiment, the pattern match engine can include an image converter to convert 3D maps 308 to other 3D file format (STL, OBJ, FBX, COLLADA, 3DS, IGES, STEP, VRML/X3D) and/or to adjust colour/brightness/contrast of the 3D maps 308. In an embodiment, the pattern match engine can includes 2D to 3D converter and/or 3D to 2D converter to make the generated 3D maps 308 compatible with the pre stored maps 116a.

In an embodiment, based on the matched patterns, profession recommendation and its related information is determined. Identified profession recommendation and/or its related information is transmitted to the notification engine 218-F, wherein the notification engine 218-F is operable to transmit the alert/notification 310 (i.e. the SMS, the MMS, the email) to one or more the portable devices 112 and/or other computing devices and/or other external network, though the communication means 110. In embodiment the portable devices 112 can be associated with the user, a teacher, a professor, a tutor, a career consultant, a coach, a mentor, a manager, a human resource department, a career guide consultant etc.

FIG. 4 illustrates an exemplary profession recommendation pattern database 400, in accordance with embodiments for implementing the system 100 of FIG. 1. The database 400 (same as 116) is utilized for the profession recommendation. The database 400 can store/maintain and/or update details of one or more patterns, a date of creation or updating of the profession recommendation pattern, a source, a comment, among other like information. As illustrated, the table comprises profession recommendation related information such as profession recommendation ID 402, the profession recommendation name 404, one or more patterns (interchangeably referred as pre-stored patterns) 406, the creation/data entry date or data update date information 408, the source of the pattern or other information associated with the profession recommendation 410 and the note/comment 412. In an embodiment, the database 400 can store multiple patterns 406a, 406b, 406c and 406d. In an embodiment, the database 400 can store additional profession recommendation related information such as sportsman - cricketer, more specifically a bowler or a batsman.

FIG. 5 illustrates an exemplary flow diagram 500 depicting steps involved in of profession recommendation, in accordance with an embodiment of the present disclosure. In an implementation, the proposed method can include, at step (502), receiving, at the server, one or more acquired biophysiological parameters from the portable device, wherein the portable device is operatively coupled with the wearable device, wherein the wearable device is worn by the user; and at step (504), determining one or more patterns based on the received biophysiological parameters. The method can further include the steps, at step (506), matching one or more determined patterns with the pre-stored patterns stored in the database, wherein the database is associated with the server, at step (508), identifying one or more profession recommendation based on the matched patterns; and at step (510), notifying the identified one or more profession recommendations.

In one embodiment, system 500 further can include a validation step, wherein the validation protocol step repeats aforementioned method steps, wherein the repetition of steps were performed when the user 102 was under external stimulations. In example, the user 102 received shooting as a career profession recommendation; the validation step includes the user 102 wears a virtual reality (VR) head set and playing a shooting game or watching a shooting video. During validation step, biophysiological parameters can be determined and corresponding patterns can be generated, based on the generated patterns career profession again can be determined. If, before and after using the VR headset, and the system 500 suggests same outcome, career which indicates that the user 102 have genius interest in the shooting. In an embodiment, during the validation step, the user 102 performs different activities (either in reality or virtually) related to previously suggested career profession, after completion of all the steps of system 500, specialization/sub-level/nested sub-level within the career profession can be determined. The specialization career profession provides specific career recommendation (i.e. a skeet over a double trap) to the user 102. Example user 102 got the suggestion as shooting, the user 102 can wear the VR headset and play one or more activities form 300 meter rifle three positions, a 300 meter rifle prone, a 300 meter standard rifle, 50 meter rifle three positions, a 50 meter rifle prone, a 10 meter air rifle, a 50 meter pistol, a 25 meter pistol, a 25 meter standard pistol, a 25 meter rapid fire pistol, a 25 meter center- fire pistol, a 10 meter air pistol, a 50 meter running target, a 50 meter running target mixed, a 10 meter running target, a 10 meter running target mixed, the trap, the double trap and a skeet. The system 500 can acquire the biophysiological parameters each time of different activities and generates patterns. The generated patterns are matched with the pre-stored patterns and career progression can be suggested.

FIG. 6 is illustration of an exemplary profession recommendation system 600, in accordance with an embodiment of the present disclosure. In an embodiment, the streams or sub-levels or sub-branches of a career/profession/career profession is depicted. The three sub-levels can be sports, academics and politics (collectively referred as 602) and further under the sub-level academics next nested level under academics can be engineer, doctor or teacher. One would appreciate that the depicted sub-levels and nested sub- levels are completely exemplary in nature and any number of sub-levels and/or nested levels are within the scope of the present disclosure. In an embodiment, the system can suggest profession upto any level/sub-level.

FIG. 7 is illustration of an exemplary detailed profession recommendation system 700, in accordance with an embodiment of the present disclosure. The figure depicts combination of professions sub-levels and nested sub-levels golf, hockey, chess (collectively referred as 702) are the three sports. Under hockey level, roller field ice (collectively referred as 704) are sub-levels; under field sub-level, player and official (collectively referred as 706) are nested sub-level. And under player nested sub-level, forward, enforcer and goalie (collectively referred as 708) are the next-level. In an embodiment, the system can suggest profession upto any level/sub-level.

In an embodiment, the wearable device 104 and/or the portable computing device 112 can include the power source to provide power to the various components. In an alternative embodiment, the power source is solar solution or rechargeable in nature.

ADVANTAGES OF THE INVENTION

The present disclosure overcomes one or more disadvantages associated with conventional systems.

The present disclosure identifies and recommend a suitable profession to a user.

The present disclosure assists a user to choose appropriate career path.

The present disclosure provides data transfer at higher speed.

The portable device enables compact and smaller design of the wearable device. The compact design greatly reduces awkwardness or discomfort to the user during the use.

Claims

CLAIMS We Claim:

1. A system for one or more profession recommendations of a user, the system comprising:

- a wearable device worn by the user, wherein the wearable device is configured to acquire one or more biophysiological parameters associated with the user;

- a portable device communicatively coupled to the wearable device, wherein the portable device is configured to receive the determined one or more biophysiological parameters, from the wearable device;

- a server arrangement communicatively coupled to the portable device, wherein the server arrangement comprises one or more memory units and one or more processors;

wherein the one or more memory units includes a database, to store one or more patterns which are associated with one or more profession recommendations;

wherein the one or more processors are operable to execute one or more routines, wherein the one or more routines include:

- a biophysiological parameters receive engine, wherein the biophysiological parameters data receive engine is operable to receive the acquired one or more biophysiological parameters associated with the user from the portable device;

- a biophysiological parameters segmentation engine, wherein biophysiological parameters segmentation engine is operable to segment the received one or more biophysiological parameters in one or more blocks;

- a feature extraction engine, wherein feature extraction engine is operable to extract one or more features from the blocks;

- a pattern generation engine, wherein the pattern generation engine is operable to generates one or more patterns based on the extracted features;

- a pattern match engine is operable to match the determined one or more patterns with the stored patterns from the database, and identify one or more profession recommendations based on the matched patterns; and - a notification engine is operable to transmit the identified one or more profession recommendation information.

2. The system of claim 1, wherein the wearable device comprises one or more sensors, said sensors being selected from the group consisting of an electroencephalography (EEG) sensor, an electrocardiography (ECG) sensor, a pulse oximeter, a body temperature sensor, a galvanic skin response (GSR) sensor, a electromyogram (EMG) sensor, a electrooculography (EOG) sensor, an accelerometer, a magnetometer, a gyroscope, a global positioning system (GPS) sensor, a glucose sensor, a blood pressure sensor, a sweating sensor an eye tracking sensor, a facial expression monitoring sensor, a body movement sensor and combination thereof.

3. The system of the claim 1, wherein the wearable device is configured to acquire the data, selected from the group of an electroencephalogram (EEG) parameter, an electrocardiogram (ECG), an electromyogram (EMG) parameter, a muscle tone, a galvanic skin response (GSR), a mechanomyogram (MMG), an electrooculogram (EOG), a magnetoencephalogram (MEG), a body temperature, a body movement, a pulse/heart rate, a blood oxygen saturation level, the tidal volume, an eye movement, a skin conductance, a blood lactate level, a blood pressure and combination of thereof.

4. The system of the claim 1, wherein the profession recommendation is selected from the group of a sportsman, an academician, a musician, a doctor, a defense man, a fashion personality, and a politician.

5. The system of claim 1, wherein the database is operable to store and update the data associated with the patterns pertains to the profession recommendation.

6. A method for profession recommendation for a user, the method comprising:

receiving, at a server, one or more acquired biophysiological parameters from a portable device, wherein the portable device is operatively coupled with the wearable device, wherein the wearable device is worn by the user;

determining one or more patterns based on the received biophysiological parameters;

matching one or more determined patterns with the pre-stored patterns stored in a database, wherein the database is associated with the server;

identifying one or more profession recommendations based on the matched patterns; and notifying the identified one or more profession recommendation.

7. The method of claim 6, wherein the wearable device comprises one or more sensor, said sensor being selected from the group consisting of an electroencephalography (EEG) sensor, an electrocardiography (ECG) sensor, a pulse oximeter, a body temperature sensor, a galvanic skin response (GSR) sensor, a electromyogram (EMG) sensor, a electrooculography (EOG) sensor, an accelerometer, a magnetometer, a gyroscope, a GPS, a body movement sensor and combination of thereof.

8. The method of the claim 6, wherein the parameter the wearable device is configured to acquire the data is selected from the group the electrical activities of a brain (Electroencephalography), the electrical activities of a heart (Electrocardiography), a heart rate, the electrical activities produced by the skeletal muscles (Electromyography), a respiratory rate, a tidal volume, a body temperature, a blood pressure, a galvanic skin resistance (GSR), a skin temperature, an eye movement, a skin conductance, a sleep sensor, and combination of thereof.

9. The method of the claim 6, wherein the profession recommendation is selected from the group of a sportsman, an academician, a musician, a doctor, a defense man, a fashion personality, and a politician.

10. The method of claim 6, wherein a database is configured to store and update the data associated with the pattern pertains to the profession recommendation.