Abstract: The instant disclosure discloses methods and systems for merging outputs of matching artificial intelligence (AI) engines that are configured to match jobs to candidates, candidates to candidates and jobs to jobs. The methods and systems illustrate how multiple artificial intelligence engines or algorithms executing machine learning-based models can be used to identify candidates or jobs that match other candidates or jobs.

Abstract: Computerized systems and methods allow a user, in a single set up process, to set up multiple sub-meetings in a compound meeting and have individualized invites sent to the invited individuals. A dynamic, interactive calendar graphic can be used where the user can drag meeting time indicators for each invited individual to desired time slots. Once suitable meeting times are established, the user can cause the invites to be sent electronically to each invitee, and each invite is individualized to the meeting start and stop times for the recipient.

Abstract: In an embodiment, a plurality of ballots (1) for a plurality of candidates, (2) generated by a plurality of search engines, and (3) for a job description are received. Each ballot from the plurality of ballots is generated by a search engine from the plurality of search engines different than remaining search engines from the plurality of search engines. A mathematical representation that indicates, for each candidate from the plurality of candidates, how many other candidates from the plurality of candidates that candidate was ranked higher than in the plurality of ballots is generated. A final ballot ranking the plurality of candidates is generated using a trained statistical model and based on the mathematical representation. A candidate from the plurality of candidates for the job description is identified based on the final ballot.

Abstract: A computer-implemented method is described herein. The method can include identifying a fingerprint associated with a webpage. The fingerprint can be identified in response to a user loading a webpage that includes a job posting. An application programming interface (API) call to query an API can be generated based at least in part on the fingerprint. The API can be associated with a video content provider. The API can be queried to identify a video content to be added to the webpage based at least in part on the API call. The video content can be added automatically to a location on the webpage in response to receiving the video content and based at least in part on the fingerprint.

Abstract: In some embodiments, a method can include generating a resume document image having a standardized format, based on a resume document having a set of paragraphs. The method can further include executing a statistical model to generate an annotated resume document image from the resume document image. The annotated resume document image can indicate a bounding box and a paragraph type, for a paragraph from a set of paragraphs of the annotated resume document image. The method can further include identifying a block of text in the resume document corresponding to the paragraph of the annotated resume document image. The method can further include extracting the block of text from the resume document and associating the paragraph type to the block of text.

Abstract: In some embodiments, a method can include generating a resume document image having a standardized format, based on a resume document having a set of paragraphs. The method can further include executing a statistical model to generate an annotated resume document image from the resume document image. The annotated resume document image can indicate a bounding box and a paragraph type, for a paragraph from a set of paragraphs of the annotated resume document image. The method can further include identifying a block of text in the resume document corresponding to the paragraph of the annotated resume document image. The method can further include extracting the block of text from the resume document and associating the paragraph type to the block of text.

Abstract: In an embodiment, communication data including an interaction between at least two individuals in an organization is received via at least two communication platforms. Content of the interaction is extracted to produce extracted content. A frequency of interaction and a type of interaction between the at least two individuals is determined based on the extracted content. A map representative of social interaction within the organization is generated. The map includes an indication of the frequency of interaction and the type of interaction between the at least two individuals. A new frequency of the interaction and a new type of interaction for at least a first individual of the at least two individuals with another individual is determined based on the map. A signal representing the new frequency of interaction and the new type of interaction with the other individual in the organization is provided to a compute device.

Abstract: In some embodiments, a method can include receiving a set of job descriptions and a set of candidate profiles. Each job description is associated with a first subset of candidate profiles from the set of candidate profiles. The method can further include executing a model to identify, from the first subset of candidate profiles, a second subset of candidate profiles that satisfy a fit metric and a third subset of candidate profiles that does not satisfy the fit metric. The method can further include calculating a bias metric based on a true positive value, a false positive value, a true negative value, and a false negative value that were calculated based on auditing the second subset of candidate profiles and the third subset of candidate profiles. The method can further include updating the set of job descriptions based on the bias metric.

Abstract: In some embodiments, a method can include receiving a set of job descriptions and a set of candidate profiles. Each job description is associated with a first subset of candidate profiles from the set of candidate profiles. The method can further include executing a model to identify, from the first subset of candidate profiles, a second subset of candidate profiles that satisfy a fit metric and a third subset of candidate profiles that does not satisfy the fit metric. The method can further include calculating a bias metric based on a true positive value, a false positive value, a true negative value, and a false negative value that were calculated based on auditing the second subset of candidate profiles and the third subset of candidate profiles. The method can further include updating the set of job descriptions based on the bias metric.