Abstract: A job searching and matching system and method is disclosed that gathers job seeker information in the form of job seeker parameters from one or more job seekers, gathers job information in the form of job parameters from prospective employers and/or recruiters, correlates the information with past job seeker behavior, parameters and behavior from other job seekers, and job parameters and, in response to a job seeker's query, provides matching job results based on common parameters between the job seeker and jobs along with suggested alternative jobs based on the co-relationships.

Abstract: A job searching and matching system and method is disclosed that gathers job seeker information in the form of job seeker parameters from one or more job seekers, gathers job information in the form of job parameters from prospective employers and/or recruiters, correlates the information with past job seeker behavior, parameters and behavior from other job seekers, and job parameters and, in response to a job seeker's query, provides matching job results based on common parameters between the job seeker and jobs along with suggested alternative jobs based on the co-relationships.

Abstract: A targeted advertisement placement system allows an advertiser to place an ad in front of an audience based on specific interests of the users in that audience, and based on the advertiser's own specific interests in users who should view the ad. The ad placement system collects information about a population of users and a population of advertisers to create profiles that can include both explicit and implicit criteria defining the user's/advertiser's interest. The explicit criteria can be explicitly provided to the ad placement system, can be extracted from documents provided by the user and/or the advertiser, etc. The implicit criteria include information learned by the system about the interests of both advertisers and users based on preferences implicitly expressed when providing feedback on search results. Using these criteria collected from advertisers and users to build profiles, the system can compare the profiles for matches to determine the best audience before which to place an advertisement.

Abstract: Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction/matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database. The system can parse an input query from a user conducting the search of the database into sub-strings, and can match the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts.

Abstract: A job searching and matching system and method is disclosed that gathers job seeker information in the form of job seeker parameters from one or more job seekers, gathers job information in the form of job parameters from prospective employers and/or recruiters, correlates the information with past job seeker behavior, parameters and behavior from other job seekers, and job parameters and, in response to a job seeker's query, provides matching job results based on common parameters between the job seeker and jobs along with suggested alternative jobs based on the co-relationships.

Abstract: A job searching and matching system and method is disclosed that gathers job seeker information in the form of job seeker parameters from one or more job seekers, gathers job information in the form of job parameters from prospective employers and/or recruiters, correlates the information with past job seeker behavior, parameters and behavior from other job seekers, and job parameters and, in response to a job seeker's query, provides matching job results based on common parameters between the job seeker and jobs along with suggested alternative jobs based on the co-relationships and based on ratings and preferences provided by the job seeker and provides negative filtration of undesirable jobs based on job seeker input and in response to queries from the system in order to efficiently and accurately accommodate job seeker perception.

Abstract: Systems and methods are provided for translating military occupations and MOS codes into civilian skills and civilian occupations. The military duties associated with a MOS code are mapped to equivalent or relevant civilian skills and civilian occupations. A user's selection of MOS codes, subspecialties, trainings, licenses or work experiences are mapped to one or more civilian skills, which in turn can be mapped to one or more civilian occupations and used to generate a set of matching civilian jobs.

Abstract: A targeted advertisement placement system allows an advertiser to place an ad in front of an audience based on specific interests of the users in that audience, and based on the advertiser's own specific interests in users who should view the ad. The ad placement system collects information about a population of users and a population of advertisers to create profiles that can include both explicit and implicit criteria defining the user's/advertiser's interest. The explicit criteria can be explicitly provided to the ad placement system, can be extracted from documents provided by the user and/or the advertiser, etc. The implicit criteria include information learned by the system about the interests of both advertisers and users based on preferences implicitly expressed when providing feedback on search results. Using these criteria collected from advertisers and users to build profiles, the system can compare the profiles for matches to determine the best audience before which to place an advertisement.

Abstract: The APPARATUSES, METHODS AND SYSTEMS FOR ADVANCEMENT PATH CANDIDATE CLONING (“CLONER”) provides mechanisms allowing advancement seekers to identify, map out, structure and interact with various advancement paths to the seeker's goals. In one embodiment, the seekers are career advancement seekers, and the CLONER provides mechanisms allowing the seeker to explore various career paths and opportunities. In one embodiment, the CLONER interacts with a statistical engine, which allows seekers to map their experiences to various advancement states in the statistical engines state structure. By so doing, it allows seeker to explore multiple paths based on various criteria, and allows seekers to plan their career goals. In the process, the CLONER allows interested parties (e.g., an employer) to supply a good known seeker (e.g., a great current employee) and identify other candidates that have similar and/or identical experience in an attempt to “clone” the known seeker's abilities.

Abstract: This disclosure details the implementation of systems, methods and apparatuses for providing an integrated process tracking and management. The disclosure provides solutions for initiating, tracking, correlating and managing complex processes. In particular, the disclosure provides systems, methods and apparatuses for process tracking and management particularly suited to job listing matching and management. In an implementation, the system may be configured to receive and extract base data entry metrics from a user designated input, for example a user's resume or a user designated saved job listing. The base data entry metrics may be correlated with user correlation metrics to derive search parameters for identifying supplemental data entries for a particular user.

Abstract: Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction/matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database. The system can parse an input query from a user conducting the search of the database into sub-strings, and can match the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts.

Abstract: Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction/matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database, parsing an input query from a user into sub-strings, and matching the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts. Furthermore, the system can learn user preferences to construct one or more profiles, including combined internal and profile weights, for producing personalized search results.

Abstract: Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction/matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database. The system can parse an input query from a user conducting the search of the database into sub-strings, and can match the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts.

Abstract: A multi-way nested search utilizes one or more knowledge bases to allow a search engine to search through a database of target documents to find the best match for a given situation. In one node of the search, a user can input one or more search terms in an input string that can be searched against the database of target documents to select and score a resulting set of documents. In another node, a reference document can be input and search terms extracted for searching against the database of target documents to select and score a resulting set. In yet another node, a reverse search can be conducted in which the reference document is scored against the result set of target documents from the reference document search. These various search results and scores can be combined using combining functions to ultimately produce the most relevant set of target documents for the user. The user can control how much weigh is given to each search.