1. A system for carrying out psychological tests as part of professional suitability and qualification for work verification process, the system comprising: a training tube comprising a surface at which a participant to be supported, whereby the participant undergoes a mesopic vision test; a display screen is arranged to display images or textural information about various profession; a sensing unit to determine behavioural pattern of the participant; and a computer programmed control unit to provide the test as part of qualification for a work verification process, wherein the control unit is arranged to communicably coupled with a first interface to receive a programmed test programme instruction and a second interface to determine a appropriate sensing nodes of sensing unit to capture behavioural pattern sensor based on the received test programme instruction, wherein the control unit is arranged to developed a machine learning technique based behavioural models to determine professional fitness score to denote degree of inclination of participant to particular profession and activate the display screen to display the determined professional fitness score.

2. The system according to claim 1, wherein the tube is inclined in an opposite direction to the one of the first interface. 18

3. The system according to claim 1, wherein the behavioural pattern is selected from cognitive adaptation, gaze direction, facial expression, skin conductance, body temperature, heart rate and combination thereof.

4. The system according to claim 1, wherein the machine learning technique is selected from Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Principal Components Analysis Clustering (PCA) and combination thereof.

5. A method for carrying out psychological tests as part of professional suitability and qualification for work verification process, the method comprising step of: performing a mesopic vision test of participants through a training tube; displaying images or textural information about various profession onto a display screen; receiving a programmed test programme instruction, determining a appropriate sensing nodes of sensing unit to capture behavioural pattern sensor based on the received test programme instruction; developing a machine learning technique based behavioural models to determine professional fitness score to denote degree of inclination of participant to particular profession; and activating the display screen to display the determined professional fitness score.

6. The method according to claim 5, wherein the tube is inclined in an opposite direction to the one of the first interface. 19

7. The method according to claim 5, wherein the behavioural pattern is selected from cognitive adaptation, gaze direction, facial expression, skin conductance, body temperature, heart rate and combination thereof.

8. The method according to claim 7, wherein the machine learning technique is selected from Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Principal Components Analysis Clustering (PCA) and combination thereof

Description: PSYCHOLOGICAL EVALUATION TO DETERMINE OPTIONS TO CHOOSE PROFESSIONS

Field of the Invention
[0001] The present invention relates generally to psychophysiological
tests, more particularly to a system and method to verify a professional
suitability of a person.
Background
[0002] The 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] Psychological evaluation is an effective tool for individuals who
are struggling to determine which career path to choose. Career decisions
can be difficult and overwhelming, and many people seek guidance in
making these important choices. A psychological evaluation to determine
career options can provide insights into an individual's strengths, interests,
and personality traits, which can be useful in identifying potential career
paths that are a good fit. The evaluation process typically involves a series
of tests, assessments, and interviews with a licensed psychologist or career
counsellor. These professionals use a variety of techniques to gather
3
information about the individual's abilities, interests, values, and goals.
Some of the assessments used in a career evaluation may include aptitude
tests, personality assessments, and interest inventories.
[0004] Aptitude tests are designed to measure an individual's natural
abilities in areas such as math, language, and spatial reasoning. Personality
assessments can help to identify an individual's strengths and weaknesses,
as well as their preferred work style and communication style. Interest
inventories can help to identify specific areas of interest that may align with
potential career paths.
[0005] Various technological solutions are disclosed in patent
literature to determine carrier suitability. Few of the exemplary documents
are discussed below.
[0006] RU2015115337A (By: Oleg Vladimirovich et al) discloses a
method of determining the students 'professional suitability, based on
testing, characterized in that to determine the students' professional
suitability, they are tested on the topics of the subjects studied, the topics
being grouped in the areas of training of higher education institutions, and
the highest rating in the corresponding field of training corresponds to the
student’s greatest inclination to chosen profession.
[0007] JP6741504B2 (By: Universal Entertainment Corp) relates to
interview apparatus configured to obtain response information through
questions and answers with an applicant, an interview controller configured
to control the interview apparatus and determine an interview evaluation
4
level which is quantification of aptitude of the applicant based on the
response information obtained by the interview apparatus, a storage device
configured to store an applicant information database in which the response
information and the interview evaluation level are stored in association with
the applicant, and a terminal device configured to be able to access the
applicant information database in the storage device are provided.
[0008] However, the technological solutions for professional carrier
compatibility evaluation suffers from various limitations such as, high timeconsumption, expensiveness, etc. Therefore, more advancement in this
field of technology is required. More specifically, to a system and method
to verify a professional suitability of a person.
Summary
[0009] The present invention relates generally to psychophysiological
tests, more particularly to a system and method to verify a professional
suitability of a person.
[00010] The following presents a simplified summary of various
aspects of this disclosure in order to provide a basic understanding of such
aspects. This summary is not an extensive overview of all contemplated
aspects, and is intended to neither identify key or critical elements nor
delineate the scope of such aspects. Its purpose is to present some
5
concepts of this disclosure in a simplified form as a prelude to the more
detailed description that is presented later.
[00011] The following paragraphs provide additional support for the
claims of the subject application.
[00012] Embodiments of the present disclosure may include a system
for carrying out psychological tests as part of professional suitability and
qualification for work verification process, the system including a training
tube including a surface at which a participant can be supported. In some
embodiments, the participant can perform a mesopic vision test.
[00013] Embodiments may also include a display screen may be
arranged to display images or textural information about various
profession, a sensing unit to determine behavioural pattern of the
participant, and a computer programmed control unit to provide the test as
part of qualification for a work verification process. In some embodiments,
the control unit may be arranged to communicably coupled with a first
interface to receive a programmed test programme instruction and a
second interface to determine a appropriate sensing nodes of sensing unit
to capture behavioural pattern sensor based on the received test
programme instruction. In some embodiments, the control unit may be
arranged to developed a machine learning technique based behavioural
models to determine professional fitness score to denote degree of
inclination of participant to particular profession and activate the display
screen to display the determined professional fitness score.
6
[00014] In some embodiments, the tube may be inclined in an opposite
direction to the one of the first interface. In some embodiments, the
behavioural pattern may be selected from cognitive adaptation, gaze
direction, facial expression, skin conductance, body temperature, heart rate
and combination thereof. In some embodiments, the machine learning
technique may be selected from Bayesian network, deep learning, random
forest, supervised vector machines, reinforcement learning, prediction
models, Statistical Algorithms, Classification, Logistic Regression, Support
Vector Machines, Principal Components Analysis Clustering (PCA) and
combination thereof.
[00015] Embodiments of the present disclosure may also include a
method for carrying out psychological tests as part of professional
suitability and qualification for work verification process, the method
including step of performing a mesopic vision test of participants through
a training tube. Embodiments may also include displaying images or
textural information about various profession onto a display screen.
[00016] Embodiments may also include receiving a programmed test
programme instruction, determining a appropriate sensing nodes of
sensing unit to capture behavioural pattern sensor based on the received
test programme instruction. Embodiments may also include developing a
machine learning technique based behavioural models to determine
professional fitness score to denote degree of inclination of participant to
7
particular profession. Embodiments may also include activating the display
screen to display the determined professional fitness score.
[00017] In some embodiments, the tube may be inclined in an opposite
direction to the one of the first interface. In some embodiments, the
behavioural pattern may be selected from cognitive adaptation, gaze
direction, facial expression, skin conductance, body temperature, heart rate
and combination thereof. In some embodiments, the machine learning
technique may be selected from Bayesian network, deep learning, random
forest, supervised vector machines, reinforcement learning, prediction
models, Statistical Algorithms, Classification, Logistic Regression, Support
Vector Machines, Principal Components Analysis Clustering (PCA) and
combination thereof.
Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would
be more clearly understood from the following description taken in
conjunction with the accompanying drawings in which:
[00019] FIG. 1 is a flowchart illustrating psychological evaluation to
determine options to choose professions, according to some embodiments
of the present disclosure.
[00020] FIG. 2 is a flowchart illustrating a method for psychological
evaluation to determine options to choose professions, according to some
embodiments of the present disclosure.
8
Detailed Description
[00021] In the following detailed description of the invention, reference
is made to the accompanying drawings that form a part hereof, and in which
is shown, by way of illustration, specific embodiments in which the
invention may be practiced. In the drawings, like numerals describe
substantially similar components throughout the several views. These
embodiments are described in sufficient detail to claim those skilled in the
art to practice the invention. Other embodiments may be utilized and
structural, logical, and electrical changes may be made without departing
from the scope of the present invention. The following detailed description
is, therefore, not to be taken in a limiting sense, and the scope of the
present invention is defined only by the appended claims and equivalents
thereof.
[00022] The use of the terms a and an and the and at least one and
similar referents in the context of describing the invention (especially in the
context of the following claims) are to be construed to cover both the
singular and the plural, unless otherwise indicated herein or clearly
contradicted by context. The use of the term at least one followed by a list
of one or more items (for example, at least one of A and B) is to be
construed to mean one item selected from the listed items (A or B) or any
combination of two or more of the listed items (A and B), unless otherwise
indicated herein or clearly contradicted by context. The terms comprising,
having, including, and containing are to be construed as open-ended terms
9
(i.e., meaning including, but not limited to,) unless otherwise noted.
Recitation of ranges of values herein are merely intended to serve as a
shorthand method of referring individually to each separate value falling
within the range, unless otherwise indicated herein, and each separate
value is incorporated into the specification as if it were individually recited
herein. 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 herein, is intended merely to better illuminate the
invention and does not pose a limitation on the scope of the invention
unless otherwise claimed. No language in the specification should be
construed as indicating any non-claimed element as essential to the
practice of the invention.
[00023] The present invention relates generally to psychophysiological
tests, more particularly to a system and method to verify a professional
suitability of a person.
[00024] FIG. 1 is a flowchart illustrating psychological evaluation to
determine options to choose professions, according to some embodiments
of the present disclosure. In some embodiments, at 110, the work
verification process may include a sensing unit to determine behavioural
pattern of the participant. A training tube comprising a surface at which a
participant to be supported. The participant may undergo a mesopic vision
test. A display screen may be arranged to display images or textural
10
information about various profession. A computer programmed control unit
to provide the test as part of qualification for a work verification process.
[00025] In some embodiments, the control unit may be arranged to
communicably couple with a first interface to receive a programmed test
programme instruction and a second interface to determine a appropriate
sensing nodes of sensing unit to capture behavioural pattern sensor based
on the received test programme instruction. The control unit may be
arranged to develop a machine learning technique based behavioural
models to determine professional fitness score to denote degree of
inclination of participant to particular profession and activate the display
screen to display the determined professional fitness score.
[00026] In some embodiments, the tube may be inclined in an opposite
direction to the one of the first interface. In some embodiments, the
behavioural pattern may be selected from cognitive adaptation, gaze
direction, facial expression, skin conductance, body temperature, heart rate
and combination thereof. In some embodiments, the machine learning
technique may be selected from Bayesian network, deep learning, random
forest, supervised vector machines, reinforcement learning, prediction
models, Statistical Algorithms, Classification, Logistic Regression, Support
Vector Machines, Principal Components Analysis Clustering (PCA)and
combination thereof.
[00027] FIG. 2 is a flowchart illustrating a method for psychological
evaluation to determine options to choose professions, according to some
11
embodiments of the present disclosure. In some embodiments, the method
may include: at 210, the step may include performing a mesopic vision test
of participants through a training tube. At 220, the step may include
displaying images or textural information about various profession onto a
display screen. At 230, the step may include receiving a programmed test
programme instruction, determining a appropriate sensing nodes of
sensing unit to capture behavioural pattern sensor based on the received
test programme instruction. At 240, the step may include developing a
machine learning technique based behavioural models to determine
professional fitness score to denote degree of inclination of participant to
particular profession. At 250, the step may include activating the display
screen to display the determined professional fitness score.
[00028] In some embodiments, the tube may be inclined in an opposite
direction to the one of the first interface. In some embodiments, the
behavioural pattern may be selected from cognitive adaptation, gaze
direction, facial expression, skin conductance, body temperature, heart rate
and combination thereof. In some embodiments, the machine learning
technique may be selected from Bayesian network, deep learning, random
forest, supervised vector machines, reinforcement learning, prediction
models, Statistical Algorithms, Classification, Logistic Regression, Support
Vector Machines, principal Components Analysis Clustering (PCA)and
combination thereof.
12
[00029] The current disclosure may be implemented in several forms,
one of which is a system for conducting psychological tests as part of a
professional appropriateness and qualification for work verification process,
the system containing a training tube with a surface at which a participant
can be supported. The individual may be subjected to a mesopic vision test
in certain implementations.
[00030] A display panel that may be set to show graphics or textual
information about different careers is also possible in certain embodiments.
It is possible for embodiments to include a sensor device for identifying
participant behaviour patterns. The exam might be administered by a
computer-controlled system in certain embodiments, serving as a
prerequisite for a process of work verification.
[00031] The control unit may be configured to communicably couple
with a first interface to receive a programmed test programme instruction
and a second interface to identify a suitable sensing nodes of sensing unit
to collect behavioural pattern sensor based on the received test programme
instruction. The control unit may be set up to activate the display screen to
show the participant's professional fitness score, which indicates the
participant's propensity towards a certain career, based on behavioural
models generated using machine learning.
[00032] The orientation of the tube, relative to the first interface, may
be different in various implementations. Cognitive adaptability, gaze
direction, facial expression, skin conductance, temperature, heart rate, and
13
combinations thereof are all candidate behavioural patterns in various
implementations. Bayesian networks, deep learning, random forests,
supervised vector machines, reinforcement learning, prediction models,
statistical algorithms, classification, logistic regression, support vector
machines, principal component analysis clustering (PCA), and combinations
thereof may all be used as the machine learning technique in various
implementations.
[00033] This disclosure may alternatively be interpreted as providing
a method for conducting psychological tests as part of a professional
appropriateness and qualification for work verification procedure, whereby
participants are subjected to a mesopic vision test using a training tube.
Displaying visuals or textural information about different occupations on a
screen is another possible embodiment.
[00034] Some embodiments may include receiving a programmed test
programme instruction and then, based on that instruction, deciding which
nodes of the sensing unit are most suited to capture the desired behavioural
pattern sensor. Creating a professional fitness score to represent a
participant's degree of predisposition towards a certain career via the use
of behavioural models informed by machine learning techniques is another
possible embodiment. In certain embodiments, the display screen is
activated to show the user their professional fitness score.
[00035] Example embodiments herein have been described above with
reference to block diagrams and flowchart illustrations of methods and
14
apparatuses. It will be understood that each block of the block diagrams
and flowchart illustrations, and combinations of blocks in the block
diagrams and flowchart illustrations, respectively, can be implemented by
various means including hardware, software, firmware, and a combination
thereof. For example, in one embodiment, each block of the block diagrams
and flowchart illustrations, and combinations of blocks in the block
diagrams and flowchart illustrations can be implemented by computer
program instructions. These computer program instructions may be loaded
onto a general purpose computer, special purpose computer, or other
programmable data processing apparatus to produce a machine, such that
the instructions which execute on the computer or other programmable
data processing apparatus create means for implementing the functions
specified in the flowchart block or blocks.
[00036] Throughout the present disclosure, the term ‘processing
means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not
limited to, a general purpose processor (such as, for example, a complex
instruction set computing (CISC) microprocessor, a reduced instruction set
computing (RISC) microprocessor, a very long instruction word (VLIW)
microprocessor, a microprocessor implementing other types of instruction
sets, or a microprocessor implementing a combination of types of
instruction sets) or a specialized processor (such as, for example, an
application specific integrated circuit (ASIC), a field programmable gate
array (FPGA), a digital signal processor (DSP), or a network processor).
15
[00037] The term non-transitory storage device or storage or memory,
as used herein relates to a random access memory, read only memory and
variants thereof, in which a computer can store data or software for any
duration.
[00038] Operations in accordance with a variety of aspects of the
disclosure is described above would not have to be performed in the precise
order described. Rather, various steps can be handled in reverse order or
simultaneously or not at all.
[00039] While several implementations have been described and
illustrated herein, a variety of other means and/or structures for performing
the function and/or obtaining the results and/or one or more of the
advantages described herein may be utilized, and each of such variations
and/or modifications is deemed to be within the scope of the
implementations described herein. More generally, all parameters,
dimensions, materials, and configurations described herein are meant to be
exemplary and that the actual parameters, dimensions, materials, and/or
configurations will depend upon the specific application or applications for
which the teachings is/are used. Those skilled in the art will recognize, or
be able to ascertain using no more than routine experimentation, many
equivalents to the specific implementations described herein. It is,
therefore, to be understood that the foregoing implementations are
presented by way of example only and that, within the scope of the
appended claims and equivalents thereto, implementations may be
16
practiced otherwise than as specifically described and claimed.
Implementations of the present disclosure are directed to each individual
feature, system, article, material, kit, and/or method described herein. In
addition, any combination of two or more such features, systems, articles,
materials, kits, and/or methods, if such features, systems, articles,
materials, kits, and/or methods are not mutually inconsistent, is included
within the scope of the present disclosure.
17
Claims
I/We Claim:
1. A system for carrying out psychological tests as part of professional
suitability and qualification for work verification process, the system
comprising: a training tube comprising a surface at which a participant to
be supported, whereby the participant undergoes a mesopic vision test; a
display screen is arranged to display images or textural information about
various profession; a sensing unit to determine behavioural pattern of the
participant; and a computer programmed control unit to provide the test
as part of qualification for a work verification process, wherein the control
unit is arranged to communicably coupled with a first interface to receive a
programmed test programme instruction and a second interface to
determine a appropriate sensing nodes of sensing unit to capture
behavioural pattern sensor based on the received test programme
instruction, wherein the control unit is arranged to developed a machine
learning technique based behavioural models to determine professional
fitness score to denote degree of inclination of participant to particular
profession and activate the display screen to display the determined
professional fitness score.
2. The system according to claim 1, wherein the tube is inclined in an
opposite direction to the one of the first interface.
18
3. The system according to claim 1, wherein the behavioural pattern is
selected from cognitive adaptation, gaze direction, facial expression, skin
conductance, body temperature, heart rate and combination thereof.
4. The system according to claim 1, wherein the machine learning technique
is selected from Bayesian network, deep learning, random forest,
supervised vector machines, reinforcement learning, prediction models,
Statistical Algorithms, Classification, Logistic Regression, Support Vector
Machines, Principal Components Analysis Clustering (PCA) and combination
thereof.
5. A method for carrying out psychological tests as part of professional
suitability and qualification for work verification process, the method
comprising step of: performing a mesopic vision test of participants through
a training tube; displaying images or textural information about various
profession onto a display screen; receiving a programmed test programme
instruction, determining a appropriate sensing nodes of sensing unit to
capture behavioural pattern sensor based on the received test programme
instruction; developing a machine learning technique based behavioural
models to determine professional fitness score to denote degree of
inclination of participant to particular profession; and activating the display
screen to display the determined professional fitness score.
6. The method according to claim 5, wherein the tube is inclined in an
opposite direction to the one of the first interface.
19
7. The method according to claim 5, wherein the behavioural pattern is
selected from cognitive adaptation, gaze direction, facial expression, skin
conductance, body temperature, heart rate and combination thereof.
8. The method according to claim 7, wherein the machine learning
technique is selected from Bayesian network, deep learning, random forest,
supervised vector machines, reinforcement learning, prediction models,
Statistical Algorithms, Classification, Logistic Regression, Support Vector
Machines, Principal Components Analysis Clustering (PCA) and combination
thereof