Patent Application
20250232263
Certain embodiments of this disclosure relate to recruiting and hiring of employees based on employer-determined qualifications and/or diversity factors. More specifically, these embodiments relate to methods for searching a database of employee candidates and selecting a candidate that may satisfy the employer-determined qualification and/or diversity factors. Other embodiments of this disclosure relate to career plan management for employment candidates. More specifically, these embodiments relate to methods for using relevant employment management plans to guide employment candidates toward their career goals.
There are systems that may search a database and return elements of the database based on those searches in the employment candidate-search context. Some of these systems allow a user to search based on a plurality of criteria, returning a list of candidates that indicates how many of the criteria each candidate matched, for example by percentage. These systems may equally emphasize all criteria in the search operation. These systems may not be capable of weighing criteria differently, for example deemphasizing certain criteria and/or emphasizing certain criteria.
Some of these systems may not effectively mask from the user the specific criteria that a given candidate matched or did not match, which may contribute to subconscious bias. These systems may fail to detect and/or mitigate subconscious bias.
Certain embodiments of this disclosure may allow a user to search a database of employee candidates and compile results based on those searches. The database may comprise employee candidate profiles each created by an employee candidate. The profile may comprise a plurality of employee-identified qualifications, for example educational credentials or number of years of work experience, and employee-identified diversity factors, for example race or gender of the candidate. The term criteria may be used hereafter to describe a set of qualifications and/or diversity factors. Hereafter the terms criterion, qualification, qualification factor, and factor may be used interchangeably.
The criteria may comprise emphasized criteria and deemphasized criteria. The system may return at least one list of employee candidate profiles. Hereafter a returned employee candidate profile may simply be called a candidate when in the context of a database or database search result. At least one list may contain a plurality of high-matching candidates, some of which may have matched to a given deemphasized criterion and some of which may not have matched to that deemphasized criterion.
The disclosed technology may assist employers with identifying career candidates who are likely to meet certain qualification factors that employers may desire or may wish to emphasize in their workforce. In one embodiment, a system is disclosed for searching a database of employee candidate profiles and processing the results based on qualification factors input by the user. Such a system allows for the results to be parsed to deemphasize one or more qualification factors while emphasizing one or more other qualification factors. In one embodiment, the deemphasized qualification factor is related to diversity, in which an employer may search for potential employees while taking diversity into account but without violating discrimination laws and regulations.
In one embodiment, the system includes an employer end-user portal configured to input search criteria and receive the results dataset related to that search. In such a system, a database module may include a career candidate portal configured to put in self-identifying qualification factors, including factors to be deemphasized, and a processor that generates a database of career candidates from the self-identified factors.
In one embodiment, a processing module may compare the search criteria to the candidate database to generate a full results list which is then categorized into subsets that either include or exclude the deemphasized qualification factor. In such a system, these subsets may then be parsed into at least one list of results that include a mix of career candidates selected separately based on search criteria either including or excluding the deemphasized qualification factor and at least one list that contains all career candidates who meet a significant portion of all qualification factors, including both the deemphasized factor and the emphasized factor.
In one embodiment, a non-transitory computer readable medium having instructions stored thereon that when executed by a processor cause the processor to perform operations of, responsive to input selecting a subset of criteria describing desired diversity and employment qualifications from a list of predefined criteria describing possible diversity and employment qualifications, identifying, from a database having candidate employees indexed against diversity and employment characteristics, candidates having diversity and employment characteristics that satisfy one or more of the subset, ordering a list of the candidates according to a number of diversity and employment characteristics that satisfy the one or more of the subset, and presenting the list without indicating which diversity and employment characteristics were among the diversity and employment characteristics that satisfied the one or more of the subset.
Certain embodiments of this disclosure may lead an employment candidate to create a set of career goals based on the candidate's answers to a set of questions provided by the disclosure. One embodiment will recognize content in the goals and create a career management plan for the candidate. In the follow-up, the embodiment will track the candidate's response to the recommendations in the career management plan and adapt the management plan to guide the candidate toward the final career goals.
Various embodiments of the present disclosure are described herein. However, the disclosed embodiments are merely exemplary and other embodiments may take various and alternative forms that are not explicitly illustrated or described. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one of ordinary skill in the art to variously employ the present invention. As those of ordinary skill in the art will understand, various features illustrated and described with reference to any one of the figures may be combined with features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described. The combinations of features illustrated provide representative embodiments for typical applications. However, various combinations and modifications of the features consistent with the teachings of this disclosure may be desired for particular applications or implementations.
All systems and all modules may be hosted on one server. Alternatively, certain systems or modules may be hosted on one server that does not host other systems or modules. Alternatively, each system and each module may be hosted on multiple servers in a single network or may be distributed over a network. Each of the repositories and databases disclosed herein may be implemented using proprietary databases or standard database software, and each database may be hosted locally, distributed over a network, or hosted remotely, such as on the cloud. These repositories and databases may be periodically updated either automatically or manually.
The following numerals are used to identify the corresponding elements in the figures for the several embodiments. 200-level numbers refer to elements of or associated with the employer module; 300-level numbers refer to elements of or associated with the database module; 400-level numbers refer to elements of or associated with the processing module; 500-level numbers refer to elements of or associated with the deemphasized factor module; 600-level numbers refer to elements of or associated with the emphasized factor module; 700-level numbers refer to elements of or associated with the parsing tool, and so on.
One embodiment of the system monitors the pre-released dataset, the gathered data before it is released to the employer. If the pre-released dataset contains less than a predetermined emphasized threshold of candidate employees, the pre-released dataset may be modified before being released to the employer. Upon reception of a modified released dataset, the employer may be notified of the modification.
One method of modification of the pre-released dataset includes adding candidate employees that have at least one employer selected deemphasized factor and at least one non-selected emphasized factor in the dataset, until the predetermined emphasized threshold of candidate employees has been reached. Application of this method of modifying the pre-released dataset is shown in the following example. If a pre-released dataset comprising 90 candidate employees is generated with an emphasized factor of female, a deemphasized factor of 15 years of manufacturing experience, and a predetermined emphasized threshold of 50%, yet contains 40 female candidate employees and 50 non-emphasized candidate employees, modification of the dataset may be needed to meet the predetermined emphasized threshold, since 40 candidate employees that have emphasized factors is less than 50% of 90 total candidate employees. To meet this threshold, 10 employee candidates that have the at least one deemphasized factor, 15 years of manufacturing experience, and at least one non-selected emphasized factor, such as military veteran, may be added to the pre-released dataset, increasing the total of candidate employees with at least one emphasized factor to 50, and the total candidate employees of the pre-released dataset to 100. Since this will result in the dataset containing at least 50% of candidates of an emphasized factor, which for this example is the predetermined emphasized threshold, the modified dataset may be released (e.g., output for viewing by the requestor).
Another method of modification of the pre-released dataset includes creating a modified deemphasized factor by modifying the parameters of the at least one deemphasized factor for use in generating the pre-released dataset, followed by adding candidate employees that have the modified deemphasized factor and the at least one selected emphasized factor. This method of modification can be repeated with the modified deemphasized factor being further modified until the pre-released dataset meets the predetermined emphasized threshold, or until a set number of iterations have passed. Application of this method of modifying the pre-released dataset is seen in the following example. If a pre-released dataset comprising 90 candidate employees is generated with an emphasized factor of female, a deemphasized factor of 15 years of manufacturing experience, and a predetermined emphasized threshold of 50%, yet contains 40 female candidate employees and 50 non-emphasized candidate employees, modification of the dataset may be needed to meet the predetermined emphasized threshold, since 40 candidate employees that have emphasized factors is less than 50% of 90 total candidate employees. To meet the predetermined emphasized threshold, parameters of the deemphasized factor may be modified from 15 to 10 years of manufacturing experience. Newly discovered female candidate employees may now be added to the pre-released dataset. If the newly discovered female candidate employees added to the pre-released dataset increase the total female candidate employees to at least 50% of the pre-released dataset, the dataset may be released. If the additional newly discovered female candidate employees do not increase the total female candidate employees to at least 50% of the pre-released dataset, modification of the parameters of the deemphasized factor may be repeated until the total amount of female candidate employees are at least 50% of the pre-released dataset. Upon the pre-released dataset containing at least 50% of candidates of emphasized factors, the dataset may be released.
Another method of modification of the pre-released dataset includes removing candidate employees that do not have an emphasized factor from the dataset. Application of this method of modifying the result of a pre-released dataset is seen in the following example. If a pre-released dataset comprising 1100 candidate employees was generated with a female emphasized factor, a deemphasized factor of 15 years of manufacturing experience, and a predetermined emphasized threshold of 50%, yet contains 500 female candidate employees and 600 non-emphasized candidate employees, modification of the pre-released dataset may be needed to meet the predetermined emphasized threshold, since 500 candidate employees is less than 50% of 1100 total candidate employees. To meet the predetermined emphasized threshold, 100 candidate employees that do not have an emphasized threshold may be removed from dataset, resulting in a total of 500 female candidate employees in a dataset of 1000 candidate employees. Since this will result in the dataset containing at least 50% of candidates of emphasized factors, the dataset may be released.
The predetermined emphasized threshold may be one or a combination of a fixed amount, fixed percentage, variable amount, and variable percentage. A predetermined emphasized threshold of variable percentage may be based on parameters including the selection of the employer's profile type, wherein the employers EEOC requirements may be determined through the employer's profile type.
One embodiment of the system monitors a factor frequency, which may be comprised of the total occurrences (e.g., 12 occurrences) of at least one selected emphasized factor (e.g., veteran) contained in at least one employer generated dataset generated in a response to a job posting query (e.g., 100 candidate employees pulled from a bank of 1000 platform users, yielding a factor frequency of 12%). Note that if 100 out of 1000 platform users identify as veteran, the frequency factor for veteran relative to the platform of users is 10%. In contrast, a factor frequency may be comprised of the total occurrences of at least one selected emphasized factor contained in a series of datasets, including the series of all datasets. The factor frequency may be measured in percentage, ratio, sum, and other numerical metrics. If the factor frequency is not equal to or greater than a first predetermined frequency threshold, the factor frequency may be increased upon future employer generated pre-released datasets. If the factor frequency is not equal to or less than a second predetermined frequency threshold, the factor frequency may be decreased upon future employer generated pre-released datasets. This embodiment may contain a target factor frequency, comprised of a value equal to or greater than the first predetermined frequency threshold, and equal to or less than the second predetermined frequency threshold. This target frequency may be the average of first and second predetermined frequency thresholds.
One method of modifying the factor frequency includes assigning a variable value, a priority parameter, to the emphasized factors. In one example, the weighting in which one of several parameters is assigned may be changed. If veterans are underrepresented in the candidate employee pre-released data set (e.g., being less than 5% of the identified candidates while the threshold is 10%), the weightings associated with those users of the bank that identify as veteran may be altered (e.g. the weighting assigned emphasized and/or non-emphasized factors when matching users to job postings may be changed). If the job posting requires 15 years of experience, and this yields candidates only 5% of which are veterans, the years of experience requirement may be automatically relaxed (e.g., to 10 years) only for those in the bank identifying as veteran. As such, relative to other users that do not identify as veteran, presumably the number of candidates in the pre-released data set identifying as veteran should increase once the matching is performed again. Other modifications to the weighting of parameters are contemplated and can be used. Continuing with the previous example, if those with 15 years of experience are given a weighting score of 90, and those with 10 to 15 years of experience are given a weighting score of 60, and those with 0 to 10 years of experience are given a weighting score of 20 (in which those with the highest rating scores are selected for a job posting requiring 15 years of experience), the system may add bonus weighting score points to veterans to increase their overall score and thus factor frequency in the pre-released data set.
In the above example, the threshold was set at 10%, but could be any suitable number. It may be predetermined or be based on the factor frequency for a demographic or group of the bank of users. For example, if 50% of the platform users identify as female, the threshold could be established such that the above automatic modifications are triggered if the factor frequency in pre-released data (data pulled in response to job postings) for females is less than 25%. That is, weighting parameters for those identified as female could be altered such that at least 25% of candidate employees in pre-released data sets identify as female. This, of course, can be extrapolated to multiple factors (e.g., veteran and disabled, etc.) Single factors were used to facilitate case of explanation. Once the factor frequencies for the represented groups or factors are at or above their corresponding thresholds, the data may be released (output) for viewing by those posting the jobs in certain embodiments. The data may also be released even if the represented groups or factors are not at or above their corresponding thresholds.
The variable value may be used to compare the priority of occurrence in datasets for candidates of emphasized factors. The value differential, the amount the variable value may be adjusted, may be dependent upon previous experienced change, the differential of the factor frequency and the predetermined frequency threshold. One method of calculating the variable differential is to look at the effect to factor frequency from a previous application of variable differential. Application of this method to calculate the value differential is shown in the following example. If the current factor frequency is 10%, the previous factor frequency was 6%, the target factor frequency is 8%, the current variable value is 3, and the previous variable value was 1, giving a previously applied variable differential of 2 (absolute value of previous variable value subtracted from current variable value), the current method would take into account that an application of a variable differential of 2 increased the factor frequency by 4%, a ratio of 1:2. Therefore, to reduce the current factor frequency of 10% to 8%, a value differential of 1 may be applied. If there is no previously applied variable differential, a fixed ratio may be used.
If higher value grading is assigned to higher frequency in datasets related to the search, a request to increase the factor frequency would increase the variable value of the at least one selected emphasized factor, and a request to decrease the factor frequency would decrease the variable value. If higher value grading is assigned to lower frequency in populating query results, a request to increase the factor frequency would decrease the variable value of the at least one selected emphasized factor, and a request to decrease the factor frequency would increase the variable value. Application of this method of modifying the factor frequency is shown in the following example. If a first predetermined frequency threshold for the veteran emphasized factor is 6%, the assigned variable value of the veteran emphasized group is 1, and higher value grading is assigned to higher frequency in datasets, yet a series of datasets show a factor frequency of 3%, modification of the factor frequency may be needed to raise it to the threshold value of 6%. Raising the variable value of the veteran emphasized factor from 1 to 2 would give the veteran emphasized factor greater priority in being added to following employer generated datasets. Greater priority may allow the at least one selected emphasized factor to increase its frequency of being contained in employer generated datasets, therefore, increasing the factor frequency. Similarly, if a second predetermined frequency threshold for the veteran emphasized factor is 9%, the assigned variable value to the veteran emphasized factor is 2, and higher value grading is assigned to higher frequency in datasets, yet a series of datasets show a factor frequency of 10%, modification of the factor frequency may be needed to lower it to the threshold value of 9%. Lowering the variable value of the veteran emphasized factor from 2 to 1.5 would give the veteran emphasized factor lower priority in being added to the following employer generated datasets. Lower priority may allow the at least one selected emphasized factor to decrease its frequency of being contained in employer generated datasets, therefor, decrease the factor frequency.
Another method of modifying the factor frequency includes assigning a modified deemphasized factor protocol to the at least one selected emphasized factor, by modifying the parameters of the at least one deemphasized factor for the at least one selected emphasis factor when an employer requests a new dataset. This protocol may assign a protocol score, a priority parameter, a value to control the amount of modification to deemphasized factors, to emphasized factors under the protocol. One method of determining the protocol score would be to look at the effect to the factor frequency from a previous modification of the protocol score. Application of this method to determine the protocol score is shown in the following example. If the current factor frequency is 10%, the previous factor frequency was 6%, the target factor frequency is 8%, the current protocol score is 3, and the previous protocol score was 1, the current method would take into account the previous change in protocol score of 2 that increased the factor frequency by 4%, a ratio of 1:2. Therefore, to reduce the current factor frequency of 10% to 8%, the protocol score may be modified by 1. If the emphasized factor is not currently on modified deemphasized factor protocol, a fixed ratio may be used.
Application of modified deemphasized factor protocol modifying the factor frequency is shown in the following example. If a first predetermined frequency threshold for the veteran emphasized factor is 6%, yet a series of dataset results show a factor frequency of 3%, modification of the factor frequency may be changed to raise it to the threshold value of 6%. To modify this factor frequency, a modified deemphasized factor protocol with a protocol score of 1 may be assigned to the veteran emphasized factor. Following this protocol assignment, if an employer requests a dataset comprising the female emphasized factor, and the deemphasized factor of 15 years of manufacturing experience, the parameters of the deemphasized factor of 15 years manufacturing experience may be modified to 10 years manufacturing experience for candidate employees that have the veteran emphasized factor. In this situation, a veteran female with 10 years manufacturing experience may share the same priority as a non-veteran female with 15 years manufacturing experience for this dataset. If dataset monitoring shows that after modified deemphasized factor protocol has been assigned, the factor frequency still has not reached the desired target, the parameters of the protocol may be further modified to increase factor frequency. Application of this method of further modifying the factor frequency is shown in the following example. If a first predetermined frequency threshold for the veteran emphasized factor is 6%, and the veteran emphasized factor is currently under modified deemphasized factor protocol with a protocol score of 1, yet a series of dataset results show a factor frequency of 4%, the protocol score of the veteran emphasized threshold may be increased to 2. Following this protocol score increase, if an employer requests a dataset comprising the female emphasized factor, and the deemphasized factor of 15 years of manufacturing experience, the parameters of the deemphasized factor of 15 years manufacturing experience may be modified to 5 years manufacturing experience for candidate employees that have the veteran factor. In this situation, a veteran female with 5 years manufacturing experience may share the same priority as a non-veteran female with 15 years manufacturing experience for this dataset. If, however, a first predetermined frequency threshold for the veteran emphasized factor is 6%, and the veteran emphasized factor is currently under modified deemphasized factor protocol with a protocol score of 2, yet a series of dataset results show a factor frequency of 8%, the protocol score of the veteran emphasized threshold may be decreased to 1.5. Following this protocol score decrease, if an employer requests a dataset comprising the female emphasized factor, and the deemphasized factor of 15 years of manufacturing experience, the parameters of the deemphasized factor of 15 years manufacturing experience may be modified to 7.5 years manufacturing experience for candidate employees that have the veteran factor. In this situation, a veteran female, with 7.5 years manufacturing experience may share the same priority as a non-veteran female with 15 years manufacturing experience for this dataset.
One method of attempting initiation of factor frequency modification may be based on at least one of a predetermined time interval, and a predetermined trigger event. This is an attempted initiation as if the factor frequency is equal to or greater than a first predetermined frequency threshold and the factor frequency is equal to or less than a second predetermined frequency threshold, the factor frequency may not need to be modified. Application of these methods of initiating a factor frequency modification attempt is shown in the following examples. If a predetermined time interval is used, and the predetermined time interval is 1 month, and a month has elapsed from the last factor frequency modification initiation attempt, the system may compare the factor frequency to at least one of the first predetermined frequency threshold, and the second predetermined frequency threshold. If the factor frequency is equal to or greater than a first predetermined frequency threshold, and equal to or less than a second predetermined frequency threshold, the time interval may be reset without factor frequency modification. If a trigger event is used, and the trigger event is set to start when the factor frequency is equal to or greater than twice the value of a second predetermined frequency threshold, the event of the factor frequency rising equal to or greater than twice the value of the second predetermined frequency threshold may initiate a factor frequency modification attempt. If a combination of a predetermined time interval and a trigger event is used, with the predetermined time interval set to 1 month, and the trigger event is set to start when the factor frequency is equal to or greater than twice the value of a second predetermined frequency threshold, a factor frequency modification attempt may occur when at least one of, the factor frequency is equal to or greater than twice the value of a second predetermined frequency threshold, or one month has elapsed from the last factor frequency modification attempt, including those started by the event triggers.
The first and second predetermined frequency thresholds may be one or a combination of fixed rate, fixed percentage, variable rate, and variable percentage. A predetermined frequency threshold of variable percentage may be based on the amount of candidate employees that have the at least one selected emphasized factor compared to sums including, the amount of candidate employees that do not have the at least one selected emphasized factor and all candidate employees. A predetermined frequency threshold of variable percentage may be based on the amount of people that have at least one selected emphasized factor compared to groups including all local population, and global population.
The employer module 200 is configured to receive search criteria and generate a code that can be compared to a database containing corresponding searchable data. The employer module 200 may include any device, now known or later discovered, capable of converting user inputs into a machine-readable format.
The database module 300 is configured to receive information from individual users related to predetermined personal and professional characteristics, to convert the information into a machine-readable code, and to compile and process that code to produce a searchable database.
The database module 300 may include any device, now known or later discovered, capable of converting user inputs into a machine-readable format.
The processing module 400 is configured to receive the search data generated by the employer module 200, compare it to the database generated by the database module 300, and categorize the results of that comparison to generate a result data set that can be viewed by the user. The categorization function of the processing module 400 is accomplished by running the comparison results through both the deemphasized factor module 500 and the emphasized factor module 600 and then generating different lists of results using the parsing tool 700.
The deemphasized factor module 500 is configured to isolate the information available through the database module 300 that is most comparable to the search information available through the employer module 200 and to separate the results into subsets.
The emphasized factor module 600 is configured to isolate the information available through the database module 300 excluding those criteria designated as deemphasized that is most comparable to the search information available through the employer module 200 and to create an additional subset.
The parsing tool 700 then distributes the subsets generated by the deemphasized factor module 500 and the subset generated by the emphasized factor module 600 into lists of results that are viewable by a user.
The search criteria selection tool 204 functions to allow users to select qualification factors from a list of such factors. The employer end-user portal 202 may be accessible by a user via a user device 201, which may be any device or collection of devices that can receive user inputs and translate the inputs into machine-readable code. For example, user the device 201 may be a smart phone or personal computer in the possession of a user. The user device 201 communicates user inputs to the search generator 206 via the employer end-user portal 202.
The search generator 206 is configured to receive the machine-readable code versions of user inputs and to compile the resulting data in a format that may allow it to be compared to the content of a database. The employer end-user portal 202 and the search generator 206 may exist on either the same or separate devices. However, the employer end-user portal 202 and the search generator 206 may exist on the same device. They may be connected to each other via a network.
The deemphasized factor 306 may be, for example, an employment qualification factor such as race, gender, or sexual orientation that contributes to diversity in the workplace but that cannot be a basis for hiring under applicable laws or regulations. The emphasized factors 308 may include, for example, employment qualification factors that are legally permissible hiring criteria such as experience in a relevant field of employment or applicable skills. In other examples, the deemphasized factor 306 may be employment qualification factors that are legally permissible hiring criteria such as experience in a relevant field of employment or applicable skills. And the emphasized factors 308 may include an employment qualification factor such as race, gender, or sexual orientation that contributes to diversity in the workplace but that cannot be a basis for hiring under applicable laws or regulations.
The candidate end-user portal 302 is designed to receive inputs from users regarding individual users' professional and/or personal employment qualification factors. Particular user inputs are categorized as either a deemphasized factor 306 or as one of several emphasized factors 308. This may be carried out via a factor input tool 304 which may but need not include a list of factors from which users may select factors that apply to said users. Factors included on this list may be categorized as a deemphasized factor 306 or may be categorized as one of several emphasized factors 308.
The candidate end-user portal 302 may be accessible by a user via a user device 301, which may be any device or collection of devices that can receive user inputs and translate the inputs into machine-readable code. For example, the user device 301 may be a smart phone or personal computer in the possession of a user. The user device 301 communicates user inputs to the factor input processor 310 via the candidate end-user portal 302.
The factor input processor 310 is configured to receive the machine-readable code versions of user inputs and to compile the resulting data into data subsets corresponding to the categorization of given datum according to the factor input tool 304 as a deemphasized factor 306 or as one of several emphasized factors 308. The factor input processor 310 compiles the data subsets into a candidate database 312 against which searches, such as those compiled by the search generator 206, may be performed. The candidate end-user portal 302, factor input processor 310, and candidate database 312 may exist on either the same or separate devices. They may be connected to one another via a network.
The search data receiver 402 communicates employer search data to the candidate database comparison tool 404. The candidate database comparison tool 404 receives employer search data from the search data receiver 402 and receives the candidate database 312 (as shown in
The data from the candidate database comparison tool 404 is analyzed by the full results generator 406, which compiles a more traditional dataset of results that does not distinguish between deemphasized factors and emphasized factors. The categorization tool 408 receives the output of the full results generator 406. The categorization tool 408 analyzes and categorizes such output to determine what matches between data from the employer module 200 and data from the database module 300 will be included in at least one final results dataset. This data is received by result the dataset generator 412 which compiles at least one set of results that accounts for the difference between deemphasized factors and emphasized factors. This dataset may be viewed by the user.
The result dataset generator 412 may also receive the full output of the full results generator 406 and may generate an additional result dataset that does not distinguish between deemphasized factors and emphasized factors that may be viewable by the user.
The categorization tool 408 functions to receive and categorize the output of the full results generator 406. The deemphasized factor module 500 receives the data output of the full results generator 406 and categorizes the data output of the full results generator 406 into a plurality of subsets which are in turn communicated to the parsing tool 700. The emphasized factor module 600 receives the data output of the full results generator 406, re-processes the data to effectively remove those with the deemphasized factor from the search results and re-categorize the results into a subset.
The data subsets of the deemphasized factor module 500 and the data subset of the emphasized factor module 600 are then received by the parsing tool 700. The parsing tool 700 determines which subsets will be included in the final results and then sends those subsets to the result dataset generator 412 to be compiled into the final results.
The high percentage isolation tool 502 functions to identify and isolate a predetermined number of data points from the data output of the full results generator 408, those isolated data points tending to show the highest relative degree of correlation between the search output of the employer module 200 and the database output of the database module 300.
The resulting isolated data is then sent to and received by the randomized selector 504. The randomized selector 504 provides each data point with a random identifier and sends the data to the subset A generator 506 and the subset B generator 508. The subset A generator 506 creates a data subset A which comprises a number of data points equal to n. The subset B generator 508 creates a data subset B which comprises a number of data points equal to the total number of isolated data points generated by the high percentage isolation tool 502 minus n, i.e., all isolated data points not included in data subset A created by the subset A generator 506. Data subset A and data subset B are then communicated to the parsing tool 700 for further processing.
The deemphasized factor removal tool 602 functions to re-categorize the data generated by full the results generator 408 to remove any reference within the results data to the identified deemphasized factor. For example, the deemphasized factor removal tool 602 may function by deleting those portions of the data produced by the full results generator 408 that describe the deemphasized factor. The re-categorized data is then sent to and received by the high percentage isolation tool 604.
The high percentage isolation tool 604 functions to identify and isolate a predetermined number of data points from the data output of the full results generator 408, those isolated data points tending to show the highest relative degree of correlation between the search output of the employer module 200 and the database output of the database module 300, excluding the effect of the deemphasized factor. The resulting isolated data is then sent to and received by the subset C generator 606. The subset C generator 606 creates a data subset C which comprises a number of data points equal to n. Data subset C is communicated to the parsing tool 700 for further processing.
Data subset B and data subset C, the data subsets received by the inclusion tool 704, are sent to and received by the top results generator 412. The top results generator 412 compiles the data subset B and data subset C into a list of highly correlative matches between the search output of the employer module 200 and the database output of the database module 300 that is viewable by the user.
The viewable list generated by the top results generator 412 may indicate and display emphasized factors shared by the search and the database results. The viewable list generated by the top results generator 412 does not indicate or display deemphasized factors. The viewable list generated by the top results generator 412 may, but need not, be ordered from the match demonstrating the greatest degree of correlation between the search output of the employer module 200 and the database output of the database module 300 to the match demonstrating the lowest degree of such correlation.
The exclusion tool 702 excludes data categorized into data subset A from inclusion in the list of highly correlative matches generated by the top results generator 412, but the exclusion tool 702 does not necessarily exclude data categorized into data subset A from inclusion in top results dataset generator 412.
In one embodiment, prior to the input of search criteria into the employer end-user portal 202, the employer end-user enters log-in data associated with a particular employer account. In one embodiment, the employer end-user portal 202 will receive the associated information upon the input of search criteria and create an associated record of factors users of the employer account have included in searches over time. This record may be used to adjust the categorization of factors as either emphasized or deemphasized in later searches by that employer end-user.
In one embodiment, the past search record may be generated as part of the employer module 200. For example, the past search record may be generated within the employer end-user portal 202 and communicated to search the search data receiver 402 with other employer search data from the employer module 200. Alternatively, the past search record may constitute a separate module within the employer module 200 which communicates with the search generator 206 before the employer module 200 data is communicated to the search data receiver 402.
The system 100 is capable of handling a wide range of user inputs that define specific career objectives, with customization being a feature. These inputs may be received via the employer end-user portal 202 or candidate end-user portal 302 and can take the form of typed text, voice commands, or selections from predefined templates using the search criteria selection tool 204 or factor input tool 304. Artificial intelligence (AI)-powered natural language processing (NLP) models can enhance input handling by extracting the user's intent and identifying contextual nuances. For example, if the user specifies the career objective of “becoming a senior software engineer,” the processors 400 activate machine learning algorithms within the candidate database comparison tool 404 to deconstruct this objective into actionable steps. These steps are tailored to the user's profile by cross-referencing the objective with career pathways, skill requirements, and market trends stored in the candidate database 312. AI models also simulate career trajectories based on similar users, identifying tasks such as acquiring certifications like Solutions Architect or Certified Java Professional, attending professional networking events, or managing projects related to cloud architecture.
The system 100 customizes recommended tasks by considering multiple factors, including geographical location, local job markets, and personal circumstances such as household size and number of dependents. Geospatial models may analyze regional job opportunities and cost-of-living data to determine actionable steps. If the system detects limited opportunities locally for the user's objective, it may suggest remote work options or relocation to regions with higher demand. Dynamic filtering, powered by collaborative filtering and reinforcement learning, may ensure that task recommendations align with the user's account type. For instance, sponsored accounts tied to corporate programs are limited to company-approved resources like internal training modules and mentorships, while non-sponsored accounts access a broader array of resources, including MOOCs, third-party certifications, and industry blogs. User preference models may be used to evaluate the suitability of resources, suggesting alternatives if constraints like budget or time availability are detected.
The system 100 collects and analyzes various types of user input data through the candidate end-user portal 302 using the factor input processor 310. Inputs include educational history, income levels, career status, job titles, skill sets, and demographic data like household size. Anomaly detection and data validation techniques may enhance the data collection process. For example, if a user with a bachelor's degree in electrical engineering reports an income of $70,000 annually but lists unrelated work experience, the system highlights inconsistencies and suggests updates. Data stored in the candidate database 312 is processed by the categorization tool 408 to tailor specific, actionable steps for achieving the user's career objectives. For example, the system might recommend completing a master's degree in systems engineering or obtaining certifications like Certified Systems Engineering Professional (CSEP). Complementary pathways may also be identified, such as transitioning into aerospace engineering, by analyzing skill overlap and market trends.
Broader economic trends, market forecasts, and user-specific financial or demographic data further enhance the system's recommendations. Time-series models may forecast demand for specific skills and industries, promoting relevance in recommendations. For instance, if data analytics skills are projected to grow in demand, the system prioritizes tasks such as enrolling in Python programming courses or applying for internships in data-centric roles. Dynamic task prioritization accounts for both static inputs, such as educational background, and dynamic factors, such as local job market shifts and personal constraints so recommendations are actionable and realistic.
The system 100 incorporates a notification mechanism via the top results generator 410, which may employ event triggers to send timely alerts. For example, if a user is pursuing certification in project management, the system sends reminders to complete course modules, register for exams, or attend preparatory workshops. Reinforcement learning may optimize the timing of these notifications by analyzing user engagement patterns. Additionally, the system proactively notifies users of upcoming industry events or webinars to maximize opportunities.
Notification preferences are customizable, and the system may adapt to user progress through behavior analysis. For instance, users who fall behind in completing tasks receive more frequent alerts, while highly engaged users are sent periodic updates with advanced recommendations. For example, if a user nearing a critical deadline has yet to start essential modules, the system might escalate the reminders while providing alternative resources, such as concise video tutorials or study guides.
When a user completes a task, such as earning a professional certification, the system 100 automatically generates updates for relevant stakeholders. The new data may be integrated into the user's profile and analyzed for its impact on career objectives. For instance, if a user completes a cybersecurity certification, the system notifies hiring managers with tailored recommendations for internal roles or projects that align with the user's new qualifications. Natural language generation (NLG) may ensure these updates are clear and contextually appropriate.
The system 100 provides tools for hiring managers to track employee progress using interactive dashboards powered by the full results generator 406. Models may analyze trends in employee development, such as skill acquisition rates or project performance, and suggest tailored development plans. For example, if an employee demonstrates advanced project management skills, the system might recommend additional leadership training or opportunities to manage cross-functional teams.
Scoring algorithms within the candidate database comparison tool 404 evaluate and rank the quality of data sources for task recommendations. The scores may be dynamically adjusted based on reliability, relevance, and recency. For example, certifications from government-accredited institutions or well-reviewed programs are ranked higher than lesser-known sources. User feedback and engagement data further refine these rankings so that recommendations remain personalized and effective.
Content format also influences recommendations, with higher scores being assigned to resources that align with user learning preferences. For instance, hands-on coding labs and real-time feedback platforms receive priority for users who engage actively in practical learning environments, while text-based resources are suggested for users with a preference for self-paced study.
Filtering mechanisms exclude low-quality or irrelevant data sources, leveraging anomaly detection algorithms to flag outdated or poorly rated content. For example, courses with declining completion rates or negative reviews are deprioritized, while newly emerging certifications with strong initial feedback are elevated. AI techniques are used to align resources with user customization preferences, such as flexibility in schedules or affordability.
Upon task completion, the system prompts users for feedback using sentiment analysis models. Positive feedback enhances the associated data source's score, making it more likely to appear in future recommendations, while negative feedback triggers a reevaluation. For instance, if users report difficulties in accessing or completing a specific certification, the system adjusts its rankings accordingly.
The dynamic feedback loop continuously improves the system's recommendations by integrating insights from user interactions. The system adapts to evolving user needs and market conditions, maintaining the relevance and quality of its suggestions.
Security credentials are factored into source scoring, with higher rankings assigned to platforms employing Secure Sockets Layer (SSL) or Transport Layer Security (TLS) encryption. For example, certification providers with robust security measures are prioritized to protect sensitive user data during transactions.
Search engine rankings and citation analysis also influence the prioritization of data sources. Metrics such as domain authority, backlink quality, and citation frequency can be evaluated. For instance, a widely cited data science certification with flexible payment plans and strong academic endorsements is ranked higher.
Thus, particular embodiments of the subject matter have been described in this specification. In some cases, the actions recited herein can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or any sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be preferable or advantageous.
All systems may be hosted on one server. Alternatively, certain systems may be hosted on one server that does not host other systems. Alternatively, each system may be hosted on multiple servers in a single network or may be distributed over a network. Each of the repositories and databases disclosed herein may be implemented using proprietary databases or standard database software, and each database may be hosted locally, distributed over a network, or hosted remotely, such as on the cloud. These repositories and databases may be periodically updated either automatically or manually.
In step 1402, the candidate may input the career and mobility goals into the account created in step 1401. In step 1403, the system may locate key words throughout the candidate account. In step 1404, the system may recommend the candidate native and reliable content based on the key words located in step 1404. In step 1405, the system may monitor and track access and use of the content delivered to the candidate in step 1405. In step 1406, the system may monitor the candidate's account for updates regarding new goals and accomplishments. In step 1407, the system may produce reporting based on the candidate's progression. Steps 1451-1454 correspond to the employer's workflow process.
In step 1451, organizations, including those with legal obligations, such as EEOC obligations, and social responsibility for all inclusive diversity recruitment may create an employer profile. In step 1452, the employer may associate an account type with their profile. Account types may include government organizations, government contractor, and non-government contractors. In step 1453, the employer may generate datasets that may include non-selected subsets that best match the diversity criteria requested. In some embodiments, the diversity criteria may be associated with the account type selected in step 1452. In step 1454, the system may continuously update emphasized and deemphasized factor status based on results including at least one pre-released dataset. In step 1455, the system may track and appropriately report organization recruitment behavior.
For candidate employees, the process begins with Step 1501, where users create an account and provide demographic, skillset, and experience data. AI may enhance this step through NLP and entity extraction, analyzing the inputs to identify missing or incomplete information. For example, if a candidate lists “software engineer” as a job title without specifying key details such as programming languages or project experience, the system uses pre-trained models, such as BERT (Generative Pre-trained Transformer) or GPT (Generative Pre-trained Transformer)-based NLP engines, to suggest related attributes. It may generate prompts like, “Please add details about programming languages used (e.g., Python, Java) or notable projects completed.” These prompts are dynamically tailored by analyzing similar user profiles and extracting patterns from a database of successful candidate profiles.
In Step 1502, candidates input career and mobility goals, which include data such as desired job roles, geographic preferences, and financial objectives. To assist candidates in providing comprehensive data, recommendation algorithms and decision trees trained on historical user data and industry data may be used. For example, collaborative filtering models can predict likely goals or inputs based on similarities with other users. If a candidate is interested in relocating, a geospatial AI model can identify optimal locations by cross-referencing cost-of-living indices, job availability, and industry growth trends. Additionally, the system employs sentiment analysis on user responses to refine its prompting strategy so questions are neither overwhelming nor redundant.
Step 1503 involves analyzing the candidate's data to extract key phrases and patterns. This step can employ advanced NLP models capable of context-aware keyword recognition, such as transformers fine-tuned on employment and career-related datasets. For example, if a candidate lists “team leadership” as a skill, the system might identify correlations with roles in project management or organizational development using clustering algorithms. Semantic search models, often leveraging embeddings from vectorized text representations, match user input to relevant job categories, skill sets, or certifications in the database for highly relevant and actionable insights.
In Step 1504, the system generates personalized recommendations for courses, certifications, or job openings. These recommendations can be powered by hybrid recommender systems that combine collaborative filtering to identify commonalities among similar users and content-based filtering to analyze the specific attributes of available resources. For instance, a candidate aiming for a cybersecurity career might receive recommendations for certifications such as “Certified Ethical Hacker” or “CompTIA Security+” based on their profile data and global hiring trends, as identified by real-time scraping and analysis of job postings. These systems can be further enhanced with reinforcement learning techniques, which allow the model to optimize recommendations based on user feedback, such as clicks or course enrollments.
Step 1505 tracks the candidate's engagement with these recommendations using behavioral analytics and clickstream data. AI algorithms may monitor user interactions, such as time spent reviewing a recommendation or partial completion of suggested tasks. Predictive models evaluate engagement levels to detect drop-off points and generate targeted interventions, such as reminder emails or alternative suggestions. For example, if a candidate frequently views but does not register for recommended courses, the system might deploy a multi-armed bandit algorithm to test different types of follow-up strategies, such as discounts or testimonials, to maximize conversion rates.
In Step 1506, candidates self-report milestones, such as earning certifications or completing internships. The system employs document recognition and verification algorithms, often powered by deep learning models such as convolutional neural networks (CNNs), to authenticate uploaded credentials. For example, if a user submits a scanned certificate, the system can cross-reference its metadata with certification databases or validate its authenticity using optical character recognition (OCR) and signature detection. If a user submits evidence of an obtained degree or work history, the system can access relevant data sources to cross-confirm its validity.
Step 1507 generates progress reports based on the candidate's achievements and engagement history. These reports can leverage data aggregation pipelines and visualization frameworks, integrating insights derived from a candidate's actions with predictive analytics. The system may use time-series models, such as Long Short-Term Memory (LSTM) networks, to forecast potential career trajectories based on past behavior and market trends. For example, a report might indicate that completing a data analytics bootcamp increases the likelihood of securing a mid-level role by 35%, based on aggregated outcomes from similar users, public data, or employer data.
For employers, the process begins with Step 1551, where organizations create accounts and define hiring criteria. The use of AI may simplify this step through adaptive form generation, which tailors inputs based on the employer's industry, size, and goals. Knowledge graphs are employed to suggest criteria that align with best practices, such as emphasizing certifications in data science for tech companies.
In Step 1552, employers specify their account type, which determines how the system optimizes search parameters. Clustering algorithms can be used to group similar employer profiles and recommend proven strategies for talent acquisition. For example, if an employer in healthcare is seeking administrative staff, the system might highlight skills such as familiarity with electronic health records based on its analysis of successful placements in similar organizations.
Step 1553 involves generating datasets of candidates using models designed to balance relevance, diversity, and/or other criteria. Multivariate optimization algorithms may ensure that datasets reflect a wide range of attributes, including technical skills, soft skills, and experience levels. For instance, a weighted scoring model might prioritize candidates with project management certifications but also include those with strong leadership potential, as inferred from NLP analysis of their profiles. Reinforcement learning can be used here to refine dataset generation over time, ensuring continual improvement based on employer feedback.
In Step 1554, emphasized and deemphasized factors can be updated dynamically by analyzing hiring trends and performance metrics. For example, natural experiments within the system might reveal that candidates with certain certifications consistently achieve higher performance ratings post-hire. These insights can be incorporated into machine learning pipelines, automatically adjusting the weightings of different factors for future searches.
Finally, Step 1555 provides employers with detailed reports on recruitment outcomes. Dashboards powered by business intelligence tools are used to visualize metrics such as time-to-hire, cost-per-hire, and diversity statistics. Predictive models may further enhance decision-making by simulating hiring scenarios. For example, a Monte Carlo simulation might forecast the impact of relaxing certain hiring criteria on the diversity of future hires. This technical framework demonstrates the system's ability to bridge gaps in employment processes to facilitate better outcomes for all involved.
The Profile section may incorporate NLP to analyze user-uploaded documents such as resumes or cover letters. For instance, the system uses NLP to identify gaps in qualifications or areas for enhancement, such as missing certifications or underemphasized skills, and then suggests updates based on successful profiles in the system's database. The Career Plan section may employ reinforcement learning to dynamically generate and optimize a detailed roadmap for achieving the candidate's goals. As the user progresses, the system refines this roadmap by adapting recommendations based on changing market trends or user preferences. The Applications section may use a recommendation engine to match users with job opportunities tailored to their skills and aspirations. For example, the system might evaluate the alignment of job descriptions with the candidate's profile using semantic similarity algorithms and recommend positions that maximize the candidate's fit and success probability. The Search Jobs section may integrate AI-enhanced search capabilities, utilizing semantic analysis and intelligent filtering to produce more accurate results. If a user searches for roles related to “data engineering,” the system might suggest alternative job titles such as “cloud architect” or “big data analyst” based on labor market trends and transferable skills identified through clustering algorithms.
In addition to these primary submenus, the system may incorporate additional submenus, including “Goals,” “Recommendations,” “Forecast,” and “Outcomes.” Each of these submenus may also be enhanced by AI to deliver advanced capabilities. The Goals submenu may allow candidates to define their career objectives using a guided process facilitated by NLP. For instance, if a candidate enters an ambiguous goal such as “advance my career,” the system prompts the user with questions to clarify and refine the goal into specific, measurable objectives. The Recommendations submenu may use collaborative filtering and content-based models to suggest tailored resources, such as courses, certifications, or professional networking events. For example, if a candidate has previously expressed interest in project management, the system might recommend certifications like PMP or Scrum Master and suggest local workshops or webinars. The Forecast submenu may provide predictive insights into the candidate's career trajectory by applying time-series forecasting models. These models estimate the likelihood of achieving specific goals within a set timeframe based on historical data and industry trends. For instance, the system may forecast a 70% probability of achieving a managerial role within two years if the candidate completes specific milestones. The Outcomes submenu may aggregate and display the results of the candidate's actions, using AI-driven dashboards to quantify the impact of completed tasks. For example, the system might calculate and display the return on investment (ROI) for a completed certification by analyzing salary growth trends for similar qualifications in the candidate's industry.
The right side of the interface, in this example, contains input fields for creating a career management plan. These fields may dynamically adapt to the user's behavior and input history. For example, if the system detects that a user has frequently modified their career preferences, it prioritizes questions that explore transferable skills or emerging opportunities. AI techniques may ensure the process remains seamless by contextualizing and tailoring these questions based on user-specific data.
Beyond the standard questions in the career plan, the system may prompt candidates to define three milestones that contribute to their overarching career objectives. Models may analyze the user's input and cross-reference it with historical success data to generate milestone suggestions. For example, a candidate aspiring to become a senior software engineer might be presented with milestones like “earn a cloud computing certification,” “lead a team project,” or “contribute to an open-source repository.” These milestones are updated annually or quarterly based on user engagement and market trends.
Once goals and milestones are recorded, the system may apply NLP to extract key phrases from the candidate's inputs, such as education level, desired industry, geographic preferences, and specific career goals. The system uses these extracted phrases to identify patterns and gaps, comparing them to a database of successful career plans. For instance, if the user's inputs lack a critical certification common among peers in their desired role, the system flags this and suggests relevant courses or certifications.
The milestones are processed to generate actionable recommendations by applying, for example, web scraping and ranking algorithms to identify relevant content online. The system evaluates these resources for credibility and relevance using a combination of sentiment analysis and user engagement metrics. For example, a candidate preparing for job interviews might receive recommendations for articles on behavioral interview techniques or videos demonstrating effective answers to common questions. Additionally, the system matches candidates with companies stored in its internal database, identifying potential employers based on alignment with the candidate's career goals.
Recommendations are tailored to the user's preferences for frequency and format, with options for daily, weekly, or biweekly updates. AI-powered filters may prevent users from being overwhelmed by irrelevant information. For example, collaborative filtering algorithms prioritize the top four most relevant resources each week based on user engagement patterns. If a candidate frequently interacts with video content, the system may prioritize video tutorials over written guides. A clickstream monitoring system tracks engagement, logging every viewed or selected recommendation for future refinement of suggestions.
If a candidate has not acted on recommendations, the system may use behavioral analytics to identify barriers and generate alternative suggestions. For example, if cost is a limiting factor preventing the candidate from enrolling in a recommended course, the system may suggest scholarships or free resources. The candidate's progress against their plan may be periodically evaluated to document unmet goals and revise the plan to include additional resources or adjusted timelines. For instance, if a candidate fails to achieve a milestone within the expected quarter, the system recalibrates its recommendations to provide more achievable steps or prioritize essential tasks.
The system may be further configured to gather and process comprehensive user data, including personal and situational information, to refine career recommendations dynamically. When a user creates an account and defines their career objectives, they are prompted to input details about their household situation, such as the number and ages of dependents, caregiving responsibilities for elderly parents, and other relevant constraints. These inputs are stored and processed alongside traditional career data, such as education, skills, and employment history, while maintaining adherence to privacy regulations through advanced encryption protocols like TLS.
The system may prompt the user with open-ended questions to gather nuanced information about their current situation and flexibility. For example, users might be asked, “How do you feel about your current living situation, and what level of flexibility do you have to accommodate career changes, such as relocating or delegating caregiving responsibilities?” Responses to these questions are analyzed using advanced artificial intelligence techniques, including NLP for extracting key data points, sentiment analysis to assess emotional tone and urgency, and named entity recognition to identify specific constraints or opportunities mentioned. For instance, a user who expresses a desire to advance their career but highlights a lack of mobility due to young children would have this constraint automatically flagged and weighted in subsequent recommendations.
The system uses predictive analytics and constraint recognition algorithms to evaluate the feasibility of the user's career objectives based on their inputs. If an objective is deemed impractical due to household limitations or not viable due to advances in technology (e.g., automation or computerization eliminating certain job roles in the future), AI or other models generate alternative but related career paths tailored to the user's situation. For instance, a user desiring to become a physician, who indicates limited flexibility due to caregiving, may be recommended a career as a nurse practitioner, which offers similar fulfillment in healthcare with fewer training years and more immediate employment opportunities. The system's recommendation engine uses cross-referencing with career path databases, labor market trends, and geospatial data to ensure its suggestions are relevant and actionable.
This capability may rely on an integrated pipeline of AI, machine learning, or other models. NLP processes extract structured data from unstructured user responses, while sentiment analysis helps quantify the emotional weight behind statements. Predictive models, such as decision trees or neural networks, analyze the impact of constraints on career feasibility, assigning weights to various factors such as caregiving duties or financial limitations. The system also integrates data sources, such as local job market trends, salary projections, and training availability, to further improve the relevance of its recommendations.
AI, machine learning, or other models provide users with tailored suggestions that address their unique constraints. For example, it may recommend asynchronous online certifications or flexible learning programs for users with limited time availability. Geospatial models identify local job opportunities or remote roles, minimizing the need for relocation. For a single parent seeking a leadership role in marketing, the models might prioritize remote team management certifications and local opportunities compatible with their caregiving responsibilities. These recommendations are presented through an interface that visualizes an actionable career roadmap, with milestones and explanations of how each recommendation aligns with their circumstances.
The system incorporates feedback loops to refine its recommendations. As users engage with tasks or update their inputs, such as gaining additional flexibility or completing certifications, the system dynamically adjusts its suggestions using reinforcement learning algorithms. For instance, if a user completes an online course in data analytics, the models contemplated herein may prioritize related certifications or internships to build on their progress. Additionally, users are notified of opportunities like upcoming industry events or webinars, with notifications for timing and relevance using behavioral analytics.
The NLP components of the system can utilize models like BERT or GPT to extract structured data from user inputs. For example, when a user inputs a goal like, “I want to transition into a data science role,” the model tokenizes the input, identifies key entities such as “data science,” and links them to associated skills like Python, machine learning, and data visualization.
Fine-tuned BERT models trained on job-related corpora (e.g., resumes, job descriptions, and career advice documents) can classify such statements into predefined career paths and generate specific task recommendations, such as “obtain a TensorFlow certification.” Moreover, these models can incorporate contextual nuances to refine recommendations, such as identifying transferable skills from adjacent fields or suggesting intermediate steps to bridge knowledge gaps.
For candidate search functionality, ranking models like LambdaMART are appropriate for optimizing candidate prioritization based on employer-defined criteria. LambdaMART, a gradient-boosted tree ensemble model, handles ranking problems effectively by learning pairwise or listwise preferences from training data. For instance, when an employer prioritizes “5+ years of experience in cloud computing,” LambdaMART assigns higher relevance scores to candidates who meet this criterion. The training dataset for this model could consist of historical employer searches, paired with labeled relevance scores derived from employer satisfaction or successful hires. Additionally, adaptive weighting strategies can be implemented to dynamically re-prioritize candidates based on evolving market trends or feedback loops within the system.
To address potential bias in ranking candidates, adversarial debiasing techniques can be applied. In this setup, a neural network trained for ranking is paired with an adversarial network that predicts sensitive attributes such as gender or ethnicity from the ranking output. The primary network minimizes its loss while simultaneously reducing the adversary's ability to predict these sensitive attributes, effectively reducing bias in the rankings. This technique is particularly useful for aligning with objectives without directly exposing sensitive attributes to the end-user. Furthermore, differential privacy mechanisms can be integrated to protect sensitive data during training while maintaining robust performance across demographic groups.
Recommendation systems for task suggestions can leverage a hybrid approach combining collaborative filtering and content-based methods. Neural collaborative filtering (NCF) provides a framework by representing users and tasks as embeddings in a latent feature space. These embeddings are fed into a neural network, which learns complex interactions between user preferences and task attributes. For example, if a user frequently interacts with courses on machine learning, the system increases the weight of this preference in the embedding space, resulting in more tailored recommendations such as advanced data science certifications or relevant job opportunities. Additionally, NCF can incorporate temporal factors to account for changing user preferences, such as shifts in industry demand or emerging skills.
In unsupervised learning, k-means clustering can group candidates based on multidimensional attributes such as skills, experience, and education. Dimensionality reduction techniques like t-SNE (t-distributed Stochastic Neighbor Embedding) or PCA (Principal Component Analysis) can visualize high-dimensional candidate data, enabling interpretable clustering. For instance, PCA might reduce a dataset of 100 features (e.g., technical skills, certifications, years of experience) into two dimensions, revealing clusters like “entry-level software engineers” or “senior project managers.” Advanced clustering algorithms like DBSCAN can further identify outlier candidates who may possess niche skill sets, which can be valuable for specialized roles.
Reinforcement learning frameworks can dynamically optimize candidate rankings and task recommendations based on real-time feedback. A Deep Q-Network (DQN) could treat each candidate selection as an action and employer satisfaction as a reward. For instance, if an employer selects a lower-ranked candidate and provides positive feedback post-hire, the system learns to give more weight to similar profiles in future queries. Similarly, for task recommendations, reinforcement learning can prioritize tasks that historically lead to higher completion rates or better user outcomes. Actor-critic methods, such as A3C (Asynchronous Advantage Actor-Critic), can enhance scalability, enabling faster convergence in dynamic environments.
For predicting user progress and dynamically adjusting career plans, time-series models like LSTMs or GRUs (Gated Recurrent Units) analyze sequential user data. For example, an LSTM model could predict whether a user is likely to achieve their goal of transitioning to a senior software engineer role within two years based on task completion rates, time spent on each task, and feedback received. The system could use these predictions to suggest alternative certifications or milestones if the user's progress appears delayed. Multivariate time-series models could further enhance accuracy by incorporating external factors, such as industry trends or economic indicators, into the predictions.
Autoencoders provide a method for data validation and anomaly detection. For instance, if a candidate's profile lists “10 years of experience” but indicates they graduated only five years ago, an autoencoder trained on consistent profile data would register a high reconstruction loss for this input, flagging it as an anomaly. This flagged data can then be reviewed or corrected to improve the reliability of the candidate database. Variational autoencoders (VAEs) can extend this capability by generating synthetic data to fill gaps in sparse datasets, enhancing model robustness.
Graph-based techniques using Graph Neural Networks (GNNs) like GraphSAGE or GAT (Graph Attention Network) enable advanced modeling of relationships between entities such as candidates, job requirements, and industries. A graph with nodes representing candidates and edges representing skill overlaps or career transitions can reveal indirect career pathways. For example, a candidate with a background in software engineering and certifications in cloud computing might be recommended for DevOps roles based on connections inferred through the graph. Temporal graph models can further account for the evolution of candidate profiles over time, providing proactive recommendations based on emerging trends.
Content scoring models trained on features such as course completion rates, user feedback, and citation frequencies can rank task resources. For example, a Random Forest model could use attributes like SSL certification (indicating secure platforms), instructor credentials, and user ratings to assign quality scores to learning resources. Higher-scoring resources would then be prioritized in recommendations, such as promoting highly-rated MOOCs over less-reviewed alternatives. Ensemble methods, such as Gradient Boosting Machines (GBMs), can improve accuracy by combining multiple weak learners to produce a stronger prediction model.
For engagement monitoring, Markov Chain models can track user interactions with the platform and predict behavior. For example, a first-order Markov Chain might model transitions between states like “view task,” “start task,” and “complete task.” If significant drop-offs occur between “view” and “start,” the system can intervene by sending targeted reminders or offering incentives, such as discounts on premium courses. Advanced variants, such as Hidden Markov Models (HMMs), can uncover latent behavioral patterns, offering deeper insights into user engagement dynamics.
Embodiments of the subject matter and operations described in this specification can be implemented in digital electronic circuitry or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.
Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on one or more computer storage medium for execution by or to control the operation of a data processing apparatus, such as a processing circuit. An exemplary processing circuit such as a CPU may comprise any digital or analog circuit components configured to perform the functions described in this specification, such as a microprocessor, microcontroller, application-specific integrated circuit, programmable logic, or some other component or components.
Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal, for example, a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus.
While this specification contains many specific implementation details, there should not be construed as limitations of the scope of this disclosure or of what may be claimed. Rather, they are descriptions of features specific to particular embodiments. Some features that are described in the context of separate embodiments in this specification may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may be implemented separately in multiple embodiments or in any suitable sub-combination. Additionally, although certain features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a particular claimed combination can in some cases be removed from the combination, and the claimed combination may be directed to a sub-combination or variation thereof.
Similarly, while operations may be depicted in the drawings as taking place in a particular order, this should not be understood as requiring such operations be performed in the particular order shown or in sequential order to achieve desirable results. In some circumstances, multitasking and parallel processing may be preferable or otherwise advantageous. Likewise, the order of operations depicted in the drawings should not be understood as requiring that all illustrated operations be performed.
The separation of various system components in the described embodiments should also not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together into a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described in this specification. In some cases, the actions recited herein can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or any sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be preferable or advantageous.
The processes, methods, or algorithms disclosed herein can be deliverable to or implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored as data and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as Read Only Memory (ROM) devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, Compact Discs (CDs), Random Access Memory (RAM) devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software executable object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software, and firmware components.
The words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the disclosure and claims.
As previously described, the features of various embodiments may be combined to form further embodiments that may not be explicitly described or illustrated. While various embodiments may have been described as providing advantages or being preferred over other embodiments or prior art implementations with respect to one or more desired characteristics, those of ordinary skill in the art recognize that one or more features or characteristics may be compromised to achieve desired overall system attributes, which depend on the specific application and implementation. These attributes include, but are not limited to cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, serviceability, weight, manufacturability, ease of assembly, etc. As such, embodiments described as less desirable than other embodiments or prior art implementations with respect to one or more characteristics are not outside the scope of the disclosure and may be desirable for particular applications.
This application is a continuation-in-part of U.S. application Ser. No. 16/242,956, filed Jan. 8, 2019, which claims the benefit of U.S. provisional application Ser. No. 62/614,759, filed Jan. 8, 2018, and U.S. provisional application Ser. No. 62/622,742, filed Jan. 26, 2018, the disclosures of all of which are hereby incorporated in their entirety by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 62622742 | Jan 2018 | US | |
| 62614759 | Jan 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16242956 | Jan 2019 | US |
| Child | 19028895 | US |
