A SYSTEM FOR SKILL GAP ANALYSIS AND LEARNING RECOMMENDATIONS

Abstract

A system for skill gap analysis and learning recommendations that utilizes a central processing unit, a neural processing unit, and various modules to collect, process, and manage data concerning an employee's skills and learning requirements. The system provides personalized learning paths, enhances workforce competency, enables data-driven training decisions, monitors skills in real-time, ensures efficient resource utilization, increases employee engagement, supports career progression, is scalable across the organization, and complies with industry standards.

Specification

Description:FIELD OF THE INVENTION
This invention relates to Skill Gap Analysis and Learning Recommendations with NLP.
BACKGROUND OF THE INVENTION
The insistent changes in business surroundings prompt the management to ensure that their employees are fit for the undertakings of the organization. Skill gap analysis and corresponding training program selection have previously been a challenging and labour intensive undertaking. Most of the techniques used involve self-assessments or even appraisals by managers and these techniques are usually subjective in nature and do not capture the complete scenario. The integration of AI in this process may be an easier approach to achieving a better and more fluid skill gap analysis and learning recommendation system.
AI instruments are used to examine different sets of data including job descriptions, industry landscapes, and the performance of workers so as to discover skill and competency generation gaps. This system can then proceed to suggest various courses that can be taken by the employee in order to ensure that the training offered is appropriate considering the individual employee and the organization as a whole.
There are several steps in collecting and processing the data necessary for carrying out AI driven skill gap analysis. First, the system has to do data collection from several places such HRIS, PE, industry reports etc. Language processing technology enables parsing of job descriptions in a more systematic way which can highlight and emphasize the sought skills. Then individual regression models can be built for each employee comparing his skills to worked out requirements.
The system can also take account any evolving trends in the industry thus helping the organization remain competitive by getting its employees ready for the challenges that lay ahead. Upon this assessment of the availability of learning opportunities, for example, the AI system will make special learning recommendations for the specific targets that may include e-learning courses, mentoring or planning new tasks on the job. These recommendations can be channeled through an employee portal to make it possible for the employees to access their template for engagement on their developmental activities.
Adopting a more satisfactory approach to the skill gap analysis process and the learning implications of the identified gaps brings numerous advantages or benefits. In the first place,, it leads to a narrowing down of the scope of the basic skill gap, hence lowering the incidence of training programs developed overly broad or suboptimal training needs which does not match reality.T His ensures that all available resources are channeled appropriately into training thus ensuring that the rate of returns on the investment made by the organization regarding training is very high. In addition, this is further reinforced by giving the employees clear and personal learning targets, a strategy that enables the companies to build up a system of successive development. This does not only serve to improve the levels of satisfaction derived from the jobs and the retention rates, but also enables the organization to remain dynamic in the face of any new and likely challenges. In addition, one more advantage would include using the mentioned data, possessed within the system, helps HR heads predict forthcoming workforce trends and plan accordingly.
US20210192412A1-A Utility patent with new concepts, methods, a comprehensive step-by-step procedure/system to produce semi-autonomous, self-curing customizable Cognitive Intelligent Autonomous Transformation System aided by Digital Assistants based on AI, Machine Learning enhanced RPA, natural language processing, speech recognition and image recognition with Deep Learning and Neural networks, that will transform an existing business system to the latest version supported by vendor for the industry with superior process automation. By Combining AI and cognitive computing in a single operating environment using the same sets of data-configuration data, Master Data, Transaction Data and historical transaction data, we propose to revolutionize existing customer's information systems to be a self-evolving cognitive intelligent automation systems where it not only knows the ultimate target information systems but also how to get there every step of the way seamlessly, similar to autonomous cars taking to destination, except in this case, information systems that run your business.
RESEARCH GAP: Personalized Learning Paths: Each employee skill trainings and practices improvement is performed after establishing each employee skill gaps and providing recommendations of improving those with personalized learning paths which guarantees effectiveness in acquiring new skills.
US10318927B2-A method and system for automating some aspects of a recruiting process, which may implement rules permitting the processes of sourcing candidates, setting up job interviews, and responding to candidate questions to all be automated with a computer. An automated system may specifically be used for conducting a conversation with a candidate over one or more communications channels, which may include pre-interview vetting of the candidate and clarification of aspects of the candidate's provided information, and may include conducting an interview or some aspects thereof. It may also be understood that an automated system may, in addition to conversing with the candidate, be used to provide feedback or additional opportunities to the candidate.
RESEARCH GAP: Data-Driven Training Decisions: The system acts as a source of information that provides empirical evidence regarding their skills level of any employee thus enabling the hr and management team to make relevant decisions on trainings, resources and their career development.
US8504405B2-The present invention relates to a method and related system for assisting and expediting an organization production of a more mature product. The method and system may include implementation of processes using a combination of both electronic hardware and software and implementation locally or over a network such as an intranet or the Internet. In another embodiment, the method may be implemented using a document management system to administer files related to the steps in the method. These files may assist a user in the creation of required documentation. A document management tool may be integrated with the document management system to associate documentation with steps in the method. A navigator tool may be employed to create a graphical display of the steps in the method using data contained in the files. Another embodiment of the present invention uses WebDAV-based communication to coordinate access to multiple document repositories.
RESEARCH GAP: Continuous Improvement: With the help of analysis reporting and recommending, the system actively encourages a positive organizational change climate whereby the workforce remains relevant and competitive to the prevailing changes in the labor market.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Skill Gap Analysis and Learning Recommendations with NLP.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The comprehensive hardware organization of the Skill Gap Analysis and Learning Recommendations system is composed of elements which enable the performance test and the rendering of individualized guidance around the issues of knowledge acquisition. This system is essentially based on the Raspberry Pi which is the processing unit of the system integrated with several sensors and modules for data collection and analysis. The system is also efficient because of the Neural Processing Unit (NPU) which has been linked to the Raspberry Pi, which eliminates information normally complicated to instigate and hence enabling rapid diagnostics of the skill level of employees.

BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: GENERAL ARCHITECTURE OF THE PRESENT INVENTION
FIGURE 2: DETAILED ARCHITECTURE OF THE PRESENT INVENTION
FIGURE 3: FLOWCHART OF THE PRESENT INVENTION
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The comprehensive hardware organization of the Skill Gap Analysis and Learning Recommendations system is composed of elements which enable the performance test and the rendering of individualized guidance around the issues of knowledge acquisition. This system is essentially based on the Raspberry Pi which is the processing unit of the system integrated with several sensors and modules for data collection and analysis. The system is also efficient because of the Neural Processing Unit (NPU) which has been linked to the Raspberry Pi, which eliminates information normally complicated to instigate and hence enabling rapid diagnostics of the skill level of employees.
It is a common practice to utilize Cameras and Microphones when conducting assessments, interviews, or training sessions since these instruments enable the collection of video as well as audio information. Such data is also important for the assessment of communication competencies since the non-verbal coding is part and parcel of the overall evaluation of the competency gap. The Neural Stick goes further in processing power especially in the use of deep learning frameworks that are more likely to find nuanced skill gaps or even skill strengths.
The system also extends the functionality of WiFi/ Bluetooth Modules to receive and send data to the Cloud Server where the information is computationally analyzed. The purpose of the cloud is to enable long-term retention of data and processing capabilities for large-scaled datasets making it accessible and ready for distribution. In addition to that, the External Storage and the SSD components provide safe places to store confidential information including the employees' performance report and customized learning journeys.
For the sake of smooth operation of the system, especially during important evaluation / training time, the whole system has been fitted with a powered unit which runs a 12V 3amp Lithium Polymer Battery which is controlled by a Power Supply Management Module. This arrangement ensures that the system can still be in use even when there is a power shortage. The structure is also intended to support Protection of Sensitive Information particularly around employee data by restricting access to the stored data to authorized personnel only, In adherence to legal requirements of data protection.
The algorithmic flowchart for the Skill Gap Analysis and Learning Recommendations system presents the process of how the skill gaps present in the organization are diagnosed and recover from them. The process initiates with Data Acquisition when data is captured from different resources such as employee assessments, performance reviews, and training sessions. The gathered data is then taken into a data preprocessing stage where it is sorted and prepared to eliminate clean irregularities and to normalize it for easy analysis.
Afterward, the processed data is used to implement the Skill Gap Detection Algorithm. This algorithm employs different models that range from machine learning to pinpoint collection/specific parts where an employee could be deficient in crucial skills. This review includes job specifications, last or existing industry matrices, and performance standards of individuals. In the event that there is a skill deficiency, the system moves a step further to address Learning Recommendations.

Once the employee's skill gap has been identified in the Learning Recommendations stage, the system develops corresponding Individual Development Plans. In other words, some suggestions may be online courses, field trips, workshops and other such activities learning. Subsequent information of this kind reaching HR managers, team leaders, and other persons or units who will execute the specified measures.
The flowchart also incorporates a Feedback Loop that ensures that learning interventions that have been implemented are evaluated and monitored in the feedback loop. Monitoring employee learning is ongoing, relative changes are made to the recommendations to ensure that improvements on skill development are consistent with the needs of the organization. If the system sees that these recommendations are ineffective towards attaining the expected learning improvements, it automatically powers some videos which initiate a review process.
The last stage of the process is Visualization and Reporting oriented with production of reports on skill gaps, learning progress of the individual's workforce competencies. These are in turn furnished on the system through dashboards so that HR & Management are enabled to be proactive in making strategic decisions on talent management and resource allocation for employee development.
ADVANTAGES OF THE INVENTION
Personalized Learning Paths: The system helps employees in identifying the gaps in skills and recommends appropriate ways to bridge the gaps ensuring that a particular employee is trained on the relevant areas leading to efficient enhancement of the skills.
Enhanced Workforce Competency: The system, through the recommendations provided and continuous analysis of the existing skill gaps, helps to improve the productivity of the workforce which has a positive impact on the productivity of the performance of the entire organization.
Data-Driven Training Decisions: The system analyzes the existing provision of skills to the employees and presents relevant data that HR and management can use in deciding on the most appropriate training programs, resources to allocate as well as career development tactics.
Real-Time Skill Monitoring: The system is also capable of providing real-time data by regular assessment in a bid to analyze employee skills and make pertinent adjustments on skills development interventions in order to suit job requirements.
Efficient Resource Utilization: The system aids the appropriate usage of training resources by focusing on the improvement of the less that are technical and relevant to the organization ensuring that the Lesh and the time used in training is as minimal as possible with maximum productivity.
The engagement of employees in their work has increased: Thus, an employer skill-gap analysis can provide effective solutions in improving personalized learning recommendations and also creating a strong employee engagement since the employees are well assured that the organization is willing to support them in their professional growth.
Support for Career Progression: Skills identified as gaps within the organization move up the career advancement ladder and so the system enables progression for the employees by equipping and top, enhancing the chances of working within that organization.
Scalable Across the Organization: This feature means that the analysis of all employees' skills gaps and the leeching patterns learnt within the recommendations will be done across all at the same time regardless of the definition of the workforce, be it complicated or simple.
Compliance with Industry Standards: The system has been developed in such a way that an employee is examined to meet the career standards and qualifications by the introduction of skills and pertinent training materials within that context.

, Claims:1. A system for skill gap analysis and learning recommendations, comprising:
a central processing unit configured to collect, process, and manage data concerning an employee's skills and learning requirements; and
a neural processing unit connected to the central processing unit and configured to perform real-time processing of complex information on an employee's skill level.
2. The system as claimed in claim 1, wherein the neural processing unit is further configured to:
record video and audio data of employee training, assessment, interview, or teaching activities to assess non-verbal and verbal communication.
3. The system as claimed in claim 1, further comprising:
a neural stick configured to enhance processing capabilities of the system, particularly where deep learning models are used to detect minor shortcomings or improvements in the skills of the employee.
4. The system as claimed in claim 1, further comprising:
a WiFi/Bluetooth module configured to transfer data to a cloud server for long-term storage and further processing.
5. The system as claimed in claim 4, wherein the cloud server is configured to store computational processes and archives that contain employee skill information for extended periods.
6. The system as claimed in claim 1, further comprising:
external storage and a solid-state disk (SSD) configured to provide storage for sensitive employee data.
7. The system as claimed in claim 1, further comprising:
a 12V 3amp lithium polymer battery controlled by a power supply management module configured to enable the system to operate without interruption during critical evaluation or training.
8. The system as claimed in claim 1, wherein the system is configured to protect sensitive employee information and allow for retrieval of employee records only by authorized personnel.
9. The system as claimed in claim 1, further comprising:
a feedback loop configured to ensure that learning interventions are evaluated and monitored, and relative changes are made to the recommendations to ensure that improvements on skill development are consistent with the needs of the organization.
10. The system as claimed in claim 1, further comprising:
a visualization and reporting component configured to produce reports on skill gaps, learning progress, and individual workforce competencies, wherein the reports are furnished on the system through dashboards to enable HR & Management to make strategic decisions on talent management and resource allocation for employee development.

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