Abstract: The present invention provides for one or more server computers configured to receive user input from a user interface (UI) comprising a first and second job description. The server queries a database to identify a first and second competency score associated with the first and second job descriptions respectively. The server then generates a personalized course profile comprising a plurality of objectives stored in the database and each associated with a third competency score between the first and second competency scores. The server then renders a second UI including an ordered list of the objectives and UI controls for accessing assets associated with the objectives. The server then transmits the second UI to a client computer for display.

Abstract: Systems and methods to monitor veracity of a collection of one or more profiles associated with a user.

Abstract: A server (10) stores a task ID of information on a task that is viewed by a client terminal (20) and a screen shot when the client terminal (20) logs out from the server (10) as a user activity log of the client terminal (20). In addition, when the client terminal (20) logs into the server (10) once again, the server (10) refers to the user activity log of the client terminal (20) and displays a screen shot upon a previous logout. Furthermore, the server (10) refers to a task ID included in the user activity log of the client terminal (20) and displays unread tasks of the client terminal (20).

Abstract: Methods and systems are disclosed for interactively developing an educational course and the materials for it using a backward design, or top-down, approach based on course objectives and course outcomes in one embodiment. Educators or other course creators are able to implement this design approach through an Integrated Design and Development Interface (IDDI). The IDDI guides course development by organizing course content in a relational database. The IDDI maps a plurality of course objectives to a plurality of course outcomes to create a course model. The IDDI uses the course model to generate and/or automatically user interfaces used by a course creator to input lower-level course information.

Abstract: A first content and a second content of a first user are obtained. The second content is configured by the first user to be displayed to a second user instead of the first content. A familiarity degree between the first user and the second user are determined. A third content is generated based on the first content, the second content, and the familiarity degree. The third content is displayed to the second user.

Abstract: The present disclosure relates generally to utilizing paid time off. In one example, the systems and methods described herein may provide an infrastructure to repurpose paid time off into other uses, such as cash, travel, bill payments, and the like.

Abstract: A system and method for determining optimal pathways to a predetermined goal, including generating a unique code for an event within a plurality of data records, where the same unique code will be generated for similar events; constructing at least one pathway using the unique codes based on the events, wherein the at least one pathway includes at least one segment; determining a goal, wherein the goal is the end of the at least one pathway; comparing the at least one pathway to other pathways sharing the determined goal; and optimizing the pathway for achievement of the determined goal.

Abstract: A system includes a memory and at least one processor to compare content in a draft social media post with a library of social media posts and determine a similarity score for the draft social media post, the library of social media posts comprising social media posts that have published in a previous sixty day period and social media posts that are scheduled to publish in a next sixty day period, determine whether the similarity score is less than a maximum allowable similarity score, send a notification that indicates that the similarity score is one of greater than or equal to the maximum allowable similarity score and less than the maximum allowable similarity score, add the draft social media post to the library of social media posts when the similarity score is less than the maximum allowable similarity score, and publish the draft social media post.

Abstract: An information source, such as an advertiser or product vendor, transfers information to contacts for which the information source has no contact data. The information source stores information in an information database which communicates the information to a referrer which has the desired contact data. The referrer uses a communication device to transfer the information to one or more contacts using contact data.

Abstract: A device receives image data of a contractual document that includes an offer including terms of a proposed transaction, converts the image data to text data that identifies text within the contractual document, and receives preferences information for a recipient of the offer. The device identifies key terms within the contractual document by using term identification to analyze the text. The key terms may include a first key term that identifies subject matter of the proposed transaction and other key terms that are part of the offer. The device determines term scores that correspond to likelihoods of the other key terms being favorable to the recipient by using a data model to analyze the key terms and the preferences information. The device, based on the term scores, generates and provides another device with a recommendation to be used in determining whether the accept the offer.

Abstract: Machine learning based techniques are described for identifying sanctionable persons via monitoring a plurality of electronic content sources. This may allow for more rapid identification of prohibited or restricted transactions. A trained sentiment analysis classifier may classify a particular electronic content item as containing sanctionable conduct. An electronic textual analysis of the electronic content item may be performed to identify one or more individual names within the particular electronic content item. An indication as to whether the one or more individual names have been identified as individuals who may be subject to one or more sanction requirements that prohibit one or more online actions may be electronically stored in a data table. Various operations may be performed to block or otherwise restrict online accounts associated with the individual from performing online activities.

Abstract: A secure education system including a secure education application, an education content database and a student profile database having a plurality of student profiles that include identification data that identifies a person associated with the student profile and evaluation data that valuates an academic proficiency of the person. The application both providing and dynamically adjusting the educational content based on an associated student profile of a user as well as securely determining the identity of the user in order to validate interactions of the user with the educational content. The verified identity and academic proficiency of a person can be compiled into an academic profile and submitted to academic institutions to support the user as a candidate for admissions.

Abstract: Techniques for enhancing usability and electronic resource efficiency using job relevance are disclosed herein. In some embodiments, a list of a predetermined number of top job openings for a member of the social networking system is generated. The list is communicated to a device of the user for presentation in a user interface on the device. The user interface allows the member to browse through and provide an indication of a lack of relevancy of each of the list of the predetermined number of top job openings to the member and an indication of a reason for the lack of relevancy. Based on the user providing the indication of the lack of relevancy and the reason, a modified relevancy assessment of each of the plurality of job openings is generated. The list of the predetermined number of top job openings for the member is regenerated and communicated to the device.

Abstract: A system and method to generate substantive and quality responses to end user reviews for publication via an online review platform. A review associated with a merchant system is identified, the review including multiple review elements. Extracting a set of key review elements from the multiple review elements. A sentiment score associated with a first key review element of the set of key review elements is determined. A review response is generated to include a set of values corresponding to a set of response components, wherein a first value of the set of values comprises the first key review element. The review response including the sentiment score associated with the first key review element is displayed.

Abstract: A system and method of optimizing employee communication to improve workplace engagement and retention is a data collection, storage, and processing software program that can improve employee retention and job performance by employing each employee with an accessible, personalized chatbot assistant. The chatbot assistant directly interacts with an employee to determine optimal solutions based on the employee's implicit or explicit needs and aspirations. The chatbot assistant further makes suggestions based on interactions with the employee and automatically implements any solutions chosen by the employee. Conversational data is collected and used to calculate optimal interactions with each employee. Such data is also valuable in calculating and responding to employee sentiment, thereby improving retention by responding to those calculated emotional scores.

Abstract: The present disclosure relates generally to utilizing paid time off. In one example, the systems and methods described herein may provide an infrastructure to repurpose paid time off into other uses, such as cash, travel, bill payments, and the like.

Abstract: An system and a method for the detection and visualization of reported ethics cases is disclosed. The system receives a set of digital records corresponding to a reported ethics violations. The system converts each of the digital records from the set of digital records into a common digital format. The system deconstructs the uniform text structure of each digital recorded by a natural language processing module to lemmatize words, remove punctuation, and remove stop words. The system inputs each deconstructed uniform text structure into a binary machine learning data model. The system inputs each deconstructed uniform text structure into a multiclass machine learning data model. The system inputs the determined value and the label to an ensemble machine learning data model. The system prioritizes reported ethics violations into one or more lists based on the determination of the possible class and transmits the list to a user interface.

Abstract: A method and system for victim notification functions by receiving a report from an accuser regarding conduct of an accused that is believed to constitute harassment, bullying and/or discrimination, the reporting being composed of structured data. The severity of conduct in the report is scored and ranked and the pervasiveness of conduct in the report is scored and ranked by comparing the reported conduct against prior reports regarding the accused. Other reports by the accuser are also analyzed.

Abstract: The present disclosure relates generally to utilizing paid time off. In one example, the systems and methods described herein may provide an infrastructure to repurpose paid time off into other uses, such as cash, travel, bill payments, and the like.

Abstract: The disclosed embodiments provide a system for predicting hiring priorities. During operation, the system determines hiring features characterizing hiring activity by a company for a set of titles. Next, the system applies a first machine learning model to the hiring features to produce a first set of scores representing future hiring volumes and applies a second machine learning model to the hiring features to produce a second set of scores representing future hiring growth. The system then generates a first ranking of the titles by the first set of scores and a second ranking of the titles by the second set of scores. Finally, the system outputs at least a portion of the first ranking as a prediction of future hiring volumes by the company and at least a portion of the second ranking as a prediction of future hiring growth by the company.