DE202021106820U1 - Improving employee retention and feedback system in industry through machine learning - Google Patents

Abstract

An advanced employee retention and feedback system (10) in industry using machine learning, the system being processed by at least one computing unit (1), the system (10) comprising:
an employee feedback module (2) used to receive the feedback from the organization's employee (3), the employee feedback module (2) comprising a number of questions relating to the organization's work , the employee's job satisfaction and the employee's requirements for the employer, the employee feedback module (2) assigns a secret code to each employee and provides the employer with a hidden profile, the employee feedback module Module (2) a database (4) of the employee with his past work history in the organization and
the current feedback from the employee's manager; and
an employee profile management unit (5), wherein the employee profile management unit (5) comprises a profile of the employees (3) with an employee satisfaction index calculated by a machine learning model, the employee satisfaction index according to the employee feedback in an employee feedback module (2 ) with a profile feedback from colleagues or
the manager of the employee is calculated, whereby the employee profile management unit (5) sends data about the lower employee satisfaction to a higher management (6) of the organization and to the associated manager,
the employee profile management unit (5) facilitating for senior management (6) to visually monitor the various employee satisfaction indices in an employer's graphical user interface.

Description

FIELD OF THE INVENTION

The present invention relates to the field of relates to a computer-implemented system for identifying, retaining and motivating employees through a management and reward system.

In particular, the present invention relates to the advancement of employee engagement and feedback system in industry through machine learning.

BACKGROUND OF THE INVENTION

The subject matter discussed in the background section should not be assumed to constitute prior art merely by virtue of its mention in the background section. Likewise, it should not be assumed that any problem mentioned in the background section or associated with the subject matter of the background section has already been recognized in the prior art. The subject matter of the background section merely presents various approaches, which in themselves may also be inventions.

IN202141028831 - A STUDY OF EMPLOYEE RETENTION STRATEGIES IN THE IT INDUSTRY - RANKING OF EMPLOYEE RETENTION STRATEGIES BASED ON THE DEMOGRAPHIC VARIABLES OF GENDER, AGE AND EXPERIENCE IN THE PRESENT COMPANY: Employee retention plays an important role in a company as it influences the performance of employees. The purpose of this study is to examine the impact of employee retention strategies, i.e. employee reward programs, flexible work environment, timely promotions, career development programs, better compensation and performance-related bonuses on employee retention and employee performance. The results of the study show that employee retention strategies have a positive effect on employee retention and employee performance. The study is also helpful for researchers and academics to understand the role of employee retention in an organization. The main purpose of this study is to identify and analyze the key strategies affecting employee retention. The following general strategies were examined in more detail in this study: job security and a good work environment, a better compensation system, effective training and development programs, rewards and recognition, employee participation in managerial activities, welfare measures, promotion opportunities, challenging work assignments, opportunities for career growth and development , a fair rating system. A total of 615 self-administered questionnaires were completed and the data analyzed using the SPSS_v20 software. The results show that employee retention strategies play a significant role in employee retention as well as organizational excellence. The results of this study will help companies understand the importance of employee retention strategies in achieving superior overall organizational performance and retaining competent employees.

US2011028266 - CUSTOMER SERVICE DATA RECORDING DEVICE, CUSTOMER SERVICE DATA RECORDING METHOD AND RECORDING MEDIUM: In order to be able to determine the correlation between customer satisfaction and employee satisfaction, a voice recording unit 102 records conversations between employees and customers; an emotion recognition unit 155 recognizes employee and customer emotions based on the employee and customer speech in the conversation; a satisfaction calculation unit 156, 157 calculates employee satisfaction and customer satisfaction based on the emotion recognition output from the emotion recognition unit 155; and a customer service data recording unit 159 refers to employee satisfaction data indicative of employee satisfaction and customer satisfaction data indicative of customer satisfaction and records them as customer service data in a management server database DB.

CA2666264 - Wear Warning and Control System: A wear warning and control system informs the employer of an employee's risk of wear and tear. The employee is assigned to a discrete fluctuation category. The discrete fluctuation category is determined from the satisfaction behavior of the employee. Employee satisfaction behavior can be categorized according to behavior type. The portal logic executed by the churn alert and control system generates team churn risk reports. From the risk reports for the employee teams, the portal logic also creates reports on the risk of churn for project teams. The reporting logic executed by the churn alert and control system delivers the discrete churn category, employee satisfaction behavior, employee team churn risk reports and project team churn risk reports through an authorized connection via a communication interface.

IN202141023567 - AN INNOVATIVE SYSTEM TO CONTROL THE RATE OF EMPLOYEES: An innovative system to control the rate of employee turnover through management using computer applications. A turnover rate alert and control system alerts an employer when an employee is about to leave. The employee is assigned to a specific fluctuation group. The discrete fluctuation group is determined by employee satisfaction activities. Employee satisfaction behavior can be characterized according to its type. The portal logic executed by the turnover alert and control system generates risk reports on staff turnover. The gateway logic generates reports on the attrition risk of project teams from the employee team attrition risk reports. The reporting logic of the turnover alert and control system sends separate turnover group, employee satisfaction, employee team turnover risk reports and project team turnover risk reports over an approved connection via a communication interface.

AU2009202262 - Wear Warning and Control System: A wear warning and control system informs the employer of an employee's risk of wear and tear. The employee is assigned to a discrete fluctuation category. The discrete fluctuation category is determined from the satisfaction behavior of the employee. Employee satisfaction behavior can be categorized according to behavior type. The portal logic executed by the churn alert and control system generates team churn risk reports. From the risk reports for the employee teams, the portal logic also creates reports on the risk of churn for project teams. The reporting logic executed by the attrition alert and control system delivers the Discrete Attrition Category, Employee Satisfaction Behavior, Employee Team Attrition Risk Reports and Project Team Attrition Risk Reports through an authorized connection via a communication interface.

US20020120494 - METHOD AND SYSTEM FOR GATHERING EMPLOYEE FEEDBACK: A computer-implemented method for collecting employee feedback, the method comprising the steps of: identifying a variety of categories related to working conditions at an employer, collecting employee feedback, organizing the Employee feedback by category in an assessment report and allow access to the assessment report for employers and other entities.

US0090307025 - Attrition Warning and Control System: An attrition warning and control system informs an employer of the risk of attrition to an employee. A discrete turnover category is assigned to the employee. The discrete fluctuation category is determined from the satisfaction behavior of the employee. Employee satisfaction behavior can be categorized according to behavior type. The portal logic executed by the churn alert and control system generates team churn risk reports. From the risk reports for the employee teams, the portal logic also creates reports on the risk of churn for project teams. The reporting logic executed by the churn alert and control system delivers the discrete churn category, employee satisfaction behavior, employee team churn risk reports and project team churn risk reports through an authorized connection via a communication interface.

IN201941038236 SYSTEM AND METHOD FOR DEVELOPING A WORKFORCE PLANNING MODEL TO IDENTIFY EMPLOYEE RETENTION: A system for developing a workforce planning model to identify employee retention is disclosed. The system comprises a workforce data collection subsystem configured to collect demographic data associated with at least one employee of an organization from a survey process; a workforce planning model generation subsystem to identify a plurality of employee retention factors corresponding to the at least one employee in the organization based on collected demographic data to determine a plurality of specific employee retention factors using a univariate statistical analysis technique and a multivariate structural equation modeling (SEM) technique analyze; to calculate an unstandardized regression weight value (URW), a standardized regression weight value (SRW), a standard error value (SE), a t-value and an R2 value for a variety of employee retention factors analyzed and to generate the workforce planning model to determine the retention of the at least one identify employees in the organization. 1

US20040049413 APPARATUS, PROCEDURE AND COMPUTER PROGRAM FOR ACCOMMODATION RESERVATION: A feedback collection section receives feedback about a hotel from an employee based on his or her previous personal experiences with the hotel, a favorites processing section provides information about the employee's favorite hotels, a recommendation processing section provides information about recommended hotels selected based on the employee's feedback, and a room availability query section provides a list of available rooms obtained from a travel agency website by adding the Feedback from the employee is created.

JP2009237636 - HUMAN RESOURCE MATCHING SYSTEM: Problem: To provide a human resource matching system to achieve appropriate staff allocation by properly selecting employees and allocating jobs to them according to each employee's skill level. Solution: It is checked whether there is an employee who satisfies the information entered from a portable terminal 2. When an employee who meets the requirements is found, the information about the employee is transmitted to a task terminal 2. If no employee is found who matches the requirements profile, it is checked whether there is an employee who corresponds to the quasi-requirements profile that leads to the requirements profile. When an employee who satisfies the quasi-requirer is found, the qualification conditions required for the requester are acquired from a qualification decision condition database 14 . Insufficient qualification is predetermined. A development method adapted to the predetermined insufficient ability and the information relevant thereto are acquired from an employee development dictionary database 15, and the information is transmitted to the task terminal.

Groupings of alternative elements or embodiments of the invention disclosed herein are not intended to be limiting. Each group member may be referenced and claimed individually or in any combination with other members of the group or other elements contained herein. One or more members of a group may be included in or removed from a group for reasons of convenience and/or patentability. Where such inclusion or deletion occurs, the specification is deemed to contain the group as amended, thereby satisfying the written description of all Markush groups used in the appended claims.

As used in the present specification and the following claims, the meaning of "a", "an" and "the" includes the plural unless the context clearly dictates otherwise. As used in the present specification, the meaning of "in" also includes "in" and "am" unless the context clearly dictates otherwise.

The enumeration of value ranges serves only as a short name for each individual value that falls within the range. Unless otherwise noted here, each individual value is included in the description as if it were individually listed here. All of the methods described herein can be performed in any suitable order, unless otherwise indicated herein or the context clearly dictates otherwise.

The use of examples or exemplary language (e.g., "such as") with respect to particular embodiments is intended solely to better illustrate the invention and should not be construed as limiting the scope of the otherwise claimed invention. No language in the specification is intended as an indication of a unclaimed element essential to the practice of the invention.

The information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in this country.

SUMMARY

Before describing the present systems and methods, it should be noted that this application is not limited to the systems and methods described, as there may be several possible embodiments that are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the specification is for the purpose of describing particular versions or embodiments only and is not intended to limit the scope of the present application.

The present invention substantially overcomes and solves the technical problems of the prior art. In response to these problems, the present invention discloses advantageous employee retention and feedback systems in the machine learning industry.

Presenting as an aspect of the present invention “An advanced employee retention and feedback system in industry using machine learning, the system being processed by at least one computing unit, the system comprising: an employee ter feedback module used to receive the feedback from the organization's employees, the employee feedback module comprising a number of questions relating to the organization's work, employee satisfaction about the work and requirements of the employee to the employer, the employee feedback module assigns each employee a secret code and provides the employer with a hidden profile, the employee feedback module maintains a database of the employee with their past work history for the organization and the current one includes feedback from the employee's manager; and an employee profile manager, wherein the employee profile manager comprises a profile of the employees with an employee satisfaction index calculated by a machine learning model, the employee satisfaction index according to the employee feedback in an employee feedback module with a profile feedback from the Colleagues or the manager of the employee is calculated, the employee profile management unit sends lower employee satisfaction data to the management of the organization and to the associated manager, the employee profile management unit provides senior management with a facility to calculate the different employee satisfaction index in a graphical user interface of the employer monitor visually.

character list

In order to clarify various aspects of some embodiments of the present invention, a more detailed description of the invention will be given by reference to certain embodiments illustrated in the attached figures. It is understood that these figures represent only illustrated embodiments of the invention and are therefore not to be considered as limiting the scope of the invention. The invention will be described and illustrated with additional specificity and detail through the use of the accompanying figures.

• 1 ein Blockdiagramm zur Darstellung des fortschrittschlichen Mitarbeiterbindungs- und Feedbacksystems in der Industrie unter Verwendung von maschinellem Lernen zeigt.

In order that the advantages of the present invention may be readily understood, a detailed description of the invention is discussed below in conjunction with the attached figures, which should not, however, be construed as limiting the scope of the invention to the attached figures, in which:

• 1 Figure 12 shows a block diagram representing the industry's advanced employee retention and feedback system using machine learning.

DETAILED DESCRIPTION

The present invention relates to an advanced employee retention and feedback system in the machine learning industry.

1 shows a block diagram of the advanced employee retention and feedback system (10) in the industry using machine learning.

Although the present disclosure is described with the purpose of advanced employee engagement and feedback system in the machine learning industry, it should be appreciated that the same was done only to illustrate the invention in an exemplary manner and to highlight another Purpose or function for which explained structures or configurations could be used and is covered within the scope of the present disclosure.

The system (10) for employee retention and feedback in industry using machine learning is disclosed in this application.

The system (10) is processed by at least one computing unit (1).

The system (10) comprises an employee feedback module (2) and an employee profile management unit (5).

Employee feedback module (2) is used to get feedback from employees (3) of the company.

The employee feedback module (2) consists of a series of questions about the way the organization works, employee satisfaction with the job and what employees expect from the employer.

The employee feedback module (2) assigns each employee a secret code that provides the employer with a hidden profile.

The employee feedback module (2) comprises a database (4) of the employee with his previous work history for the company and the current feedback from the employee's manager.

The employee profile management unit (5) includes a profile of the employees (3) with an employee satisfaction index calculated by a machine learning model.

The employee satisfaction index is calculated based on employee feedback in an employee feedback module (2) with profile feedback from the employee's colleagues or manager.

The employee profile management unit sends data on lower employee satisfaction to the higher management (6) of the organization and to the associated manager.

The employee profile management unit (5) facilitates senior management (6) to visually monitor the various employee satisfaction indices in an employer graphical user interface.

The mobile computing device is used to evaluate the employee feedback module (2). The employee feedback module (2) contains questions in the form of objective questions.

The arithmetic unit (1) is a processor-based arithmetic unit.

The Employee Satisfaction Index is calculated based on an extensive data analysis of employees in the industry using training sets and classification models.

Those skilled in the art will understand that one or more of the elements described may well be combined into a single functional element. Alternatively, certain elements can be broken down into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. In addition, the actions of a block diagram do not need to be performed in the order shown, and not all actions need to be performed. Also, those actions that are not dependent on other actions can be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.

Although embodiments of the invention have been described in language specific to structural features and/or methods, the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of embodiments of the invention.

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• US2011028266 [0005]
• CA2666264 [0006]
• IN 202141023567 [0007]
• AU2009202262 [0008]
• US20020120494 [0009]
• US0090307025 [0010]
• IN 201941038236 [0011]
• US20040049413 [0012]
• JP2009237636 [0013]

Claims (5)

An advanced employee retention and feedback system (10) in industry using machine learning, the system being processed by at least one computing unit (1), the system (10) comprising: an employee feedback module (2) used to receive the feedback from the organization's employee (3), the employee feedback module (2) comprising a number of questions relating to the organization's work , the employee's job satisfaction and the employee's requirements for the employer, the employee feedback module (2) assigns a secret code to each employee and provides the employer with a hidden profile, the employee feedback module Module (2) a database (4) of the employee with his past work history in the organization and the current feedback from the employee's manager; and an employee profile management unit (5), wherein the employee profile management unit (5) comprises a profile of the employees (3) with an employee satisfaction index calculated by a machine learning model, the employee satisfaction index according to the employee feedback in an employee feedback module (2 ) with a profile feedback from colleagues or the manager of the employee is calculated, whereby the employee profile management unit (5) sends data about the lower employee satisfaction to a higher management (6) of the organization and to the associated manager, the employee profile management unit (5) facilitating for senior management (6) to visually monitor the various employee satisfaction indices in an employer's graphical user interface. The industry's most advanced employee retention and feedback system (10) using machine learning claim 1 , wherein the mobile computing unit is used to evaluate the employee feedback module (2). The industry's most advanced employee retention and feedback system (10) using machine learning claim 1 , wherein the employee feedback module (2) contains questions in the form of objective questions. The industry's most advanced employee retention and feedback system (10) using machine learning claim 1 , wherein the processing unit (1) is a processor-based processing unit. The industry's most advanced employee retention and feedback system (10) using machine learning claim 1 , where the employee satisfaction index is calculated using large data analysis of employees by the same in the industry using training sets and classification models...

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