1. A machine learning (ML)-based method for psychological fitness level detection and management, comprising receiving sensor data from internet of things (IoT)based sensing node; pre-processing the sensor data to remove noise and artifacts; training an ML algorithm using historical sensor data and corresponding psychological fitness levels; using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level; and transmitting the determined psychological fitness level to a remote computing device.

2. The method of claim 1, wherein the IoT based sensing node comprising one or more sensors selected from galvanic skin response (GSR)sensor, electrocardiogram (ECG)sensors, and photoplethysmography (PPG)sensors.

3. The method of claim 3 wherein the ML algorithm is selected from a neural network, artificial neural networks (ANNs), multilayer feed-forward neural networks (MLFs), Convolutional neural networks (CNN), Bayesian neural network (BNN), Deep Feed-Forward NN (FFNN), Deep Recurrent NN (RNN)and combination thereof.

4. The method of claim 1 further comprising using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level.

5. The method of claim 5 further comprising using the ML algorithm to continuously learn and improve its accuracy in detecting psychological fitness levels.

6. The method of claim 1 further comprising generating personalized psychological fitness management plans based on the user's psychological fitness level.

7. The personalized psychological fitness management plans of claim 7 comprising breathing exercises, meditation techniques, and psychological fitness-reducing activities.

8. The method of claim 1 further comprising displaying psychological fitness level information and psychological fitness management suggestions to the user through a user interface.  SYSTEM TO CALCULATE A CANDIDATE'S PSYCHOLOGICAL FITNESS LEVEL Abstract Embodiments of the present disclosure may include a machine learning (ML)-based method for psychological fitness level detection and management, including receiving sensor data from internet of things (IoT) based sensing node. Embodiments may also include pre-processing the sensor data to remove noise and artifacts. Embodiments may also include training an ML algorithm using historical sensor data and corresponding psychological fitness level/psychological fitness levels. Embodiments may also include using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level detection level. Embodiments may also include transmitting the determined psychological fitness level detection level to a remote computing device. , Claims:Claims :

1. A machine learning (ML)-based method for psychological fitness level detection and management, comprising receiving sensor data from internet of things (IoT)based sensing node; pre-processing the sensor data to remove noise and artifacts; training an ML algorithm using historical sensor data and corresponding psychological fitness levels; using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level; and transmitting the determined psychological fitness level to a remote computing device.

2. The method of claim 1, wherein the IoT based sensing node comprising one or more sensors selected from galvanic skin response (GSR)sensor, electrocardiogram (ECG)sensors, and photoplethysmography (PPG)sensors.

3. The method of claim 3 wherein the ML algorithm is selected from a neural network, artificial neural networks (ANNs), multilayer feed-forward neural networks (MLFs), Convolutional neural networks (CNN), Bayesian neural network (BNN), Deep Feed-Forward NN (FFNN), Deep Recurrent NN (RNN)and combination thereof.

4. The method of claim 1 further comprising using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level.

5. The method of claim 5 further comprising using the ML algorithm to continuously learn and improve its accuracy in detecting psychological fitness levels.

6. The method of claim 1 further comprising generating personalized psychological fitness management plans based on the user's psychological fitness level.

7. The personalized psychological fitness management plans of claim 7 comprising breathing exercises, meditation techniques, and psychological fitness-reducing activities.

8. The method of claim 1 further comprising displaying psychological fitness level information and psychological fitness management suggestions to the user through a user interface.

Description:SYSTEM TO CALCULATE A CANDIDATE'S PSYCHOLOGICAL FITNESS LEVEL
Field of the Invention
[0001] The present invention relates generally to software tools to identify a psychological profile, more particularly to a system and method to evaluate a candidate's psychological fit for a particular job.

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] A few well-known software applications help the hiring process by determining whether a candidate and a position are a suitable fit in terms of abilities and experience. Yet, these software techniques do not assess a candidate's psychological suitability for a position. Employers should also seek for a match between a candidate's psychological profile and the psychological features they will use in a role in order to support happy, fulfilled, and enabled staff and to enjoy the associated rewards.
[0004] Several well-known software applications look at a candidate's personality and temperament assessment and make predictions about the candidate's future in a role using psychological instruments or methodology. To estimate an applicant's general performance and job happiness, these software techniques, however, do not use any psychological evaluation of the function.
[0005] Various technological solutions (e.g., personality test apparatus and method thereof, Method and apparatus for analysis of psychiatric and physical conditions, etc.) are disclosed in patent literature. Few of the exemplary documents are discussed below.
[0006] KR20160136055A (By: TWINWORD) discloses personality test apparatus and a method thereof are disclosed. The personality test apparatus according to an embodiment of the present invention includes an input/output part for providing test items including stimulus words and acquiring reaction words received from a testee with respect to the stimulus words, and a control part for extracting the stimulus words from word graphs classified by a personality type, constructing test items, analyzing the reaction words using the word graphs, and determining the personality type of the testee. So, the personality of the testee can be easily determined.
[0007] EP2007277A2 (By: MIROW SUSAN) provides method, apparatus and software for diagnosing the state or condition of a human which always generates physiological modulated signals having temporal-spatial organization, the organization having dynamic patterns whose structure is fractal, involving the monitoring of at least on physiological modulated signal and obtaining a set of temporal- spatial values of each of said physiological modulated signals, and processing the respective temporal-spatial values using linear and nonlinear tools to determine the linear and nonlinear characteristics established for known criteria to determine the state or condition of the person.
[0008] US10623431B2 (By: FORCEPOINT) relates to method, system and computer-usable medium for performing a psychological profile operation. The psychological profile operation includes: monitoring user interactions between a user and an information handling system; converting the user interactions into electronic information representing the user interactions; determining when the user interactions are associated with generation of an electronic communication; associating the user interactions with the electronic communication; and, generating a psychological profile of the user based upon the user interactions and the electronic communication, the psychological profile comprising information regarding a psychological state of the user.
[0009] However, the technological solutions for psychological fitness level detection suffers from various limitations such as, complexity, etc. Therefore, more advancement in this field of technology is required. More specifically, to a system and method for evaluating a candidate's psychological fit for a particular job.
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary
[00011] The present invention relates generally to software tools to identify a psychological profile, more particularly to a system and method to evaluate a candidate's psychological fit for a particular job.
[00012] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure.
[00013] The following paragraphs provide additional support for the claims of the subject application.
[00014] Embodiments of the present disclosure may include a machine learning (ML)-based method for psychological fitness level detection and management, including receiving sensor data from internet of things (IoT)based sensing node. Embodiments may also include pre-processing the sensor data to remove noise and artifacts. Embodiments may also include training an ML algorithm using historical sensor data and corresponding psychological fitness levels. Embodiments may also include using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level. Embodiments may also include transmitting the determined psychological fitness level to a remote computing device.
[00015] In some embodiments, the IoT based sensing node including one or more sensors selected from galvanic skin response (GSR)sensor, electrocardiogram (ECG)sensors, and photoplethysmography (PPG)sensors. In some embodiments, the method may include using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level. In some embodiments, the method may include generating personalized psychological fitness management plans based on the user's psychological fitness level. In some embodiments, the method may include displaying psychological fitness level information and psychological fitness management suggestions to the user through a user interface.
Brief Description of the Drawings
[00016] Embodiments will now be described in more detail in relation to the enclosed drawings, in which:
[00017] FIG. 1 is a flowchart illustrating a machine learning (ML)-based method, according to some embodiments of the present disclosure.

Detailed Description
[00018] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00019] Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. However, the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.
[00020] The present invention relates generally to software tools to identify a psychological profile, more particularly to a system and method to evaluate a candidate's psychological fit for a particular job.
[00021] FIG. 1 is a flowchart that describes a machine learning (ML)-based method, according to some embodiments of the present disclosure. In some embodiments, at 140, the machine learning (ML)-based method may include receiving sensor data from internet of things (IoT)based sensing node. At 110, the receiving may include training an ML algorithm using historical sensor data and corresponding psychological fitness levels. At 120, the receiving may include using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level. At 130, the receiving may include transmitting the determined psychological fitness level to a remote computing device. Pre-processing the sensor data to remove noise and artifacts.
[00022] In some embodiments, the IoT based sensing node comprising one or more sensors selected from galvanic skin response (GSR) sensor, electrocardiogram (ECG) sensors, and photoplethysmography (PPG)sensors. In some embodiments, the method may include using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level. In some embodiments, the method may include generating personalized psychological fitness management plans based on the user's psychological fitness level. In some embodiments, the method may include displaying psychological fitness level information and psychological fitness management suggestions to the user through a user interface.
[00023] A machine learning (ML)-based method may be included as one embodiment of the current disclosure. This method may be used for the detection and management of psychological fitness levels, and it may include receiving sensor data from an internet of things (IoT)-based sensing node. In certain embodiments, there is additionally a pre-processing step that eliminates noise and artefacts from the sensor data. In certain embodiments, there is also the possibility of training a machine learning algorithm with the help of past sensor data and the accompanying psychological fitness levels. Using the ML algorithm to categorise the sensor data as matching to a certain psychological fitness level is another possibility that may be included in embodiments. certain embodiments, the process of determining the psychological fitness level additionally includes sending that information to a remote computer device.
[00024] In certain implementations, the Internet of Things-based sensing node includes one or more sensors chosen from the group consisting of electrocardiogram (ECG) sensors, galvanic skin response (GSR) sensors, and photoplethysmography (PPG) sensors. Using the ML algorithm to categorise the sensor data as relating to a certain psychological fitness level is an optional step that may be included in some implementations of the approach. The approach may, in certain implementations, incorporate the creation of individualised strategies for psychological fitness management in response to the user's current level of psychological fitness. The technique may, in certain implementations, comprise providing the user with information on their current level of psychological fitness as well as ideas for how they may better manage their psychological fitness via the use of a user interface.
[00025] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms module, functionality, and component as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors Executable instructions stored on the computer-readable media or memory can include, for example, an operating system, a data management framework , and/or other modules, programs, or applications that are loadable and executable by the processor(s) or any appropriate hardware logic components/CPU(s).
[00026] The present invention provides a machine learning-based method for psychological fitness level detection and management. The method includes the following steps:
Step 1: Data Collection
[00027] The first step involves collecting data from various sources. The sources include wearable devices, social media activity, physiological sensors, and self-reporting. The wearable devices can include smartwatches, fitness trackers, and other devices that can measure physical activity, heart rate, and sleep. Social media activity can include posts, comments, and likes on social media platforms such as Facebook, Twitter, and Instagram. Physiological sensors can include electroencephalogram (EEG) sensors, electrocardiogram (ECG) sensors, and skin conductance sensors. Self-reporting can include surveys or questionnaires that ask individuals to report their psychological fitness levels.
Step 2: Data Pre-processing
[00028] The collected data is pre-processed to remove noise, correct errors, and convert data into a suitable format for machine learning algorithms. The pre-processing steps include data cleaning, data transformation, and data normalization.
Step 3: Feature Extraction
[00029] The pre-processed data is then fed into a feature extraction algorithm that extracts relevant features from the data. The features can include heart rate variability, sleep quality, social media sentiment analysis, and other features that are indicative of psychological fitness.
Step 4: Machine Learning Algorithm Training
[00030] The extracted features are then used to train a machine learning algorithm. The machine learning algorithm can be a supervised or unsupervised learning algorithm, such as support vector machines (SVMs), decision trees, or neural networks. The algorithm is trained to detect patterns indicative of psychological fitness in the data.
Step 5: Psychological fitness Level Detection
[00031] The trained machine learning algorithm is then used to detect the level of psychological fitness experienced by the individual. The algorithm outputs a psychological fitness score that indicates the level of psychological fitness experienced by the individual.
Step 6: Psychological fitness Management Recommendations
[00032] Based on the psychological fitness score, the method provides personalized psychological fitness management recommendations to the individual. The recommendations can include relaxation techniques, physical exercise, meditation, counseling, and other psychological fitness management techniques.
Advantages:
[00033] The present invention has several advantages over traditional methods for psychological fitness level detection and management. The machine learning algorithm can analyze data from multiple sources and detect patterns that are indicative of psychological fitness, making it a more reliable method for psychological fitness detection. Additionally, the personalized psychological fitness management recommendations can be tailored to the individual's needs, making it a more effective method for psychological fitness management.
[00034] It will be obvious to a person skilled in the art that, as the technology advances, the inventive concept can be implemented in various ways. The above described embodiments are given for describing rather than limiting the disclosure, and it is to be understood that modifications and variations may be resorted to without departing from the spirit and scope of the disclosure as those skilled in the art readily understand. Such modifications and variations are considered to be within the scope of the disclosure and the appended claims. The protection scope of the disclosure is defined by the accompanying claims.
[00035] Conditional language such as, among others, include, including, comprise, comprising, can, could, might or may, unless specifically stated otherwise, is understood within the context to present that certain examples include, while other examples do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that certain features, elements and/or steps are in any way required for one or more examples or that one or more examples necessarily include logic for deciding, with or without user input or prompting, whether certain features, elements and/or steps are included or are to be performed in any particular example. Conjunctive language such as the phrase at least one of X, Y or Z, unless specifically stated otherwise, is to be understood to present that an item, term, etc. may be any of X, Y, or Z, or a combination or sub-combination thereof.As described above, the exemplary embodiment provides both a method and corresponding apparatus consisting of various modules providing functionality for performing the steps of the method. The modules/engines may be implemented as hardware (embodied in one or more chips including an integrated circuit such as an application specific integrated circuit), or may be implemented as software or firmware for execution by a computer processor. In particular, in the case of firmware or software, the exemplary embodiment can be provided as a computer program product including a computer readable storage structure embodying computer program code (i.e., software or firmware) thereon for execution by the computer processor.
[00036] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, each refers to each member of a set or each member of a subset of a set.
[00037] Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and sub-combination of these embodiments. Accordingly, all embodiments may be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and sub-combinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or sub-combination.
[00038] The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.

Claims
I/We Claim:
1. A machine learning (ML)-based method for psychological fitness level detection and management, comprising receiving sensor data from internet of things (IoT)based sensing node; pre-processing the sensor data to remove noise and artifacts; training an ML algorithm using historical sensor data and corresponding psychological fitness levels; using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level; and transmitting the determined psychological fitness level to a remote computing device.

2. The method of claim 1, wherein the IoT based sensing node comprising one or more sensors selected from galvanic skin response (GSR)sensor, electrocardiogram (ECG)sensors, and photoplethysmography (PPG)sensors.
3. The method of claim 3 wherein the ML algorithm is selected from a neural network, artificial neural networks (ANNs), multilayer feed-forward neural networks (MLFs), Convolutional neural networks (CNN), Bayesian neural network (BNN), Deep Feed-Forward NN (FFNN), Deep Recurrent NN (RNN)and combination thereof.
4. The method of claim 1 further comprising using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level.
5. The method of claim 5 further comprising using the ML algorithm to continuously learn and improve its accuracy in detecting psychological fitness levels.
6. The method of claim 1 further comprising generating personalized psychological fitness management plans based on the user's psychological fitness level.
7. The personalized psychological fitness management plans of claim 7 comprising breathing exercises, meditation techniques, and psychological fitness-reducing activities.

8. The method of claim 1 further comprising displaying psychological fitness level information and psychological fitness management suggestions to the user through a user interface. 

SYSTEM TO CALCULATE A CANDIDATE'S PSYCHOLOGICAL FITNESS LEVEL
Abstract
Embodiments of the present disclosure may include a machine learning (ML)-based method for psychological fitness level detection and management, including receiving sensor data from internet of things (IoT) based sensing node. Embodiments may also include pre-processing the sensor data to remove noise and artifacts. Embodiments may also include training an ML algorithm using historical sensor data and corresponding psychological fitness level/psychological fitness levels. Embodiments may also include using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level detection level. Embodiments may also include transmitting the determined psychological fitness level detection level to a remote computing device. , Claims:Claims
I/We Claim:
1. A machine learning (ML)-based method for psychological fitness level detection and management, comprising receiving sensor data from internet of things (IoT)based sensing node; pre-processing the sensor data to remove noise and artifacts; training an ML algorithm using historical sensor data and corresponding psychological fitness levels; using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level; and transmitting the determined psychological fitness level to a remote computing device.

2. The method of claim 1, wherein the IoT based sensing node comprising one or more sensors selected from galvanic skin response (GSR)sensor, electrocardiogram (ECG)sensors, and photoplethysmography (PPG)sensors.
3. The method of claim 3 wherein the ML algorithm is selected from a neural network, artificial neural networks (ANNs), multilayer feed-forward neural networks (MLFs), Convolutional neural networks (CNN), Bayesian neural network (BNN), Deep Feed-Forward NN (FFNN), Deep Recurrent NN (RNN)and combination thereof.
4. The method of claim 1 further comprising using the ML algorithm to classify the sensor data as corresponding to a particular psychological fitness level.
5. The method of claim 5 further comprising using the ML algorithm to continuously learn and improve its accuracy in detecting psychological fitness levels.
6. The method of claim 1 further comprising generating personalized psychological fitness management plans based on the user's psychological fitness level.
7. The personalized psychological fitness management plans of claim 7 comprising breathing exercises, meditation techniques, and psychological fitness-reducing activities.

8. The method of claim 1 further comprising displaying psychological fitness level information and psychological fitness management suggestions to the user through a user interface.