92 results about "Job description" patented technology

A method is described. The method comprises: extracting one or more contents related to an organization from one or more data sources based on one or more job role names and one or more contextual inputs; determining, using an artificial intelligence engine, at least one of an organizational structure, one or more roles, one or more responsibilities, one or more hierarchical relationships, one or more access levels, one or more service actions, and one or more departments of the organization from the one or more contents; and generating, using the artificial intelligence engine, one or more job descriptions based on at least one of the organizational structure, the one or more roles, the one or more responsibilities, the one or more hierarchical relationships, the one or more access levels, the one or more service actions, and the one or more departments.

The invention discloses an occupational matching and recommendation method based on natural language processing. The method comprises the steps that S1, resume data and position description data of job seekers are collected; s2, performing multi-level feature extraction on the preprocessed job seeker resume data and position description data through a BERT model; s3, enhancing the multi-level features by using a weak supervised learning technology, and performing deep modeling on potential semantics of the text through a self-supervised adversarial network; s4, constructing a semantic matching network; s5, calculating the matching degree between the job seeker resume data and the position description data through the semantic matching network and the feature set; s6, generating and providing a corresponding position recommendation list; s7, the job seeker resume data state change and the position market dynamic state are monitored in real time; and S8, collecting feedback information of the job seeker and the recruiter. According to the invention, the recommendation accuracy and the matching degree between the job seeker and the position are improved, and the recommended position is ensured to highly meet the occupational development demand of the job seeker.

A talent recruitment system includes architecture having a first AI module for generating a job description wherein the first AI module leverages a Large Language Model trained on a proprietary dataset of successful job descriptions. The talent recruitment system includes a sourcing module for automated candidate sourcing wherein the sourcing module includes use of an AI-driven engine leveraging a global database of candidate profiles. The talent recruitment system includes a screening module for AI-Enhanced resume screening wherein the screening module screens and ranks resumes by applying consistent criteria in relation to the job requirements.

To provide an information processing system etc. that enables personnel working in an organization to search for positions within the organization. [Solution] According to one aspect of the present invention, there is provided an information processing system including a processor, the processor being configured to execute each of the following steps by reading a program, wherein in the acquisition step, position information for internal positions in an organization is acquired, the position information including at least one of related department, recruitment requirements, and job content as recruitment conditions, and in the position information display control step, the position information is displayed on a viewer's terminal according to the relationship between the registration information of managed persons working in the organization and the position information, wherein the viewer is the managed persons or an administrator who manages the managed persons, and the position information displayed on the viewer's terminal includes first position information for positions that are hiring and second position information for positions that are not hiring.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-modal mixed interview question generation system and method based on artificial intelligence. According to the method, customized interview questions are provided for candidates with different backgrounds and different skill levels by dynamically selecting questions based on candidate resumes and knowledge maps, the question difficulty and knowledge emphasis are automatically adjusted through multiple rounds of answering effects and interviewer labeling data, the effectiveness and flexibility of interview evaluation are improved, and the interview evaluation efficiency is improved. Answering results and behavior anomaly detection are fused, knowledge mastering is evaluated, potential non-behavior factors can be found, manual question screening time is shortened through an automatic process, consistency of interview experiences of different candidates is guaranteed, subjective prejudice is reduced, new knowledge documents and post description can be accessed at any time, a multi-modal graph is continuously iterated, and the method is high in practicability and high in practicability. And changes of new technical fields and new post requirements are adapted.

The invention discloses an employee matching method and system based on artificial intelligence, and belongs to the technical field of human resource matching, and the method comprises the following steps: resume analysis: automatically analyzing a resume of a job seeker, and extracting key information; position description analysis: automatically analyzing the position description of the recruitment enterprise, and extracting key requirements; feature matching: performing feature extraction and matching on the key information of the job seeker and the position through a deep learning algorithm; similarity evaluation: according to a feature matching result, evaluating a matching degree between the job seeker and the position, and giving a matching score; and user interaction: the job seeker and the recruitment enterprise query a matching result and related information through a user interface. According to the invention, through technologies of deep learning, natural language processing and the like, resumes of job seekers and position description of recruitment enterprises are automatically analyzed, so that efficient and accurate matching between the job seekers and positions is realized.

The invention discloses a personalized intelligent interview system, and relates to the technical field of artificial intelligence and recruitment interview, and the system comprises a position information analysis module which is used for inputting an original position description text, carrying out the analysis through employing a natural language processing NLP technology, and extracting structured position demand data containing skill items, characteristic items and quantification levels; the resume / position intelligent matching module is used for receiving the structured position demand data, receiving a job seeker resume text, performing intelligent matching analysis by constructing a skill semantic vector space, and outputting a multi-dimensional matching score matrix and an investigation key index set; according to the job demand and the job seeker resume, personalized interview topic screening and optimization are realized, and the interview pertinence and accuracy are improved; the job seeker is comprehensively evaluated, and the resume / position matching information is combined, so that a more comprehensive evaluation and scoring basis is provided, and an enterprise is helped to make a more accurate recruitment decision.

The present artificial intelligence-based system (100) automates job matching by evaluating each candidate's resume against job requirements. It includes a user interface (102) for administrators to upload job descriptions and candidate resumes, a database directory module (104) for data storage, a document extension module (106) for file verification, and a processing module (108) with a Large Language Model (110), at least one vector database (118), and a Scoring module (112). The processing module interprets resume contents through machine-readable instructions, performs similarity searches, and scores candidates in real-time based on their suitability for each job requirement. The method (200) of the present invention involves receiving and managing job descriptions and resumes, verifying document formats, processing resumes and job descriptions, and displaying ranked results. Within the processing step (208), the method further encompasses processing and interpreting resume contents, scoring candidates for real-time suitability, and comparing qualifications and skills against job descriptions.

A method, system and computer program for advanced candidate evaluation and selection for recruitment processes are proposed. The method comprises collecting data concerning a recruitment process, including job description data, candidates data, and prioritization parameters regarding specific needs of the job position, the candidates data comprising, for each candidate, personal details thereof and a record of an interview; extracting information for enhancing the recruitment process by processing the collected data using natural language, and categorizing the extracted information into different domains; generating representations of the extracted information using an embedding model; calculating a similarity measure of the generated representations by comparing them with those within a same domain using comparison techniques; computing a fit percentage of each candidate to the job position by aggregating and weighting the calculated similarities based on the one or more prioritization parameters; and ranking the candidates based on the fit percentage.

The invention relates to a large-model-driven automatic talent resume screening and interview evaluation method, and aims to improve recruitment efficiency, reduce human prejudice and improve fairness and accuracy of recruitment decisions. According to the technical scheme, the method comprises the steps of intelligent resume analysis, wherein key information (such as skills, experiences and achievements) of a resume is extracted through a large model, and adaptation degree scores and recommendation reasons are generated in combination with position description; and automatic interview evaluation: generating customized interview questions based on recruitment demands and a large model, analyzing answers (such as logicality and expression ability) of candidates in real time, and providing a comprehensive evaluation report. And dynamic feedback optimization: continuously optimizing the model through historical recruitment data, and supporting enterprise self-defined evaluation standards to adapt to different industries and posts. And system integration: combining a resume analysis engine, an interview evaluation module and a data visualization board to form a whole-process AI auxiliary decision-making system.

The invention relates to the field of high-performance computing cluster resource scheduling management, and discloses a Slurm scheduling specification integration method and system, and the method comprises the following steps: 1, analyzing a heterogeneous job description file, and extracting a resource demand parameter and a dependency relationship through a regular expression rule base; 2, based on the extracted original parameters, converting the original parameters into SLURM standard parameters through a preset mapping rule; step 3, according to the converted standard parameters; 4, calling a Slurm interface command to submit a script; and step 5, monitoring the execution state of the submitted job, and triggering a re-submission process for the abnormal job with the resource overrun or dependency missing. Through a multi-level analysis architecture and a regular expression rule base, job description files in different formats are effectively compatible, the problem that analysis of a non-standardized input format by a traditional method fails is solved, unified processing of cross-platform job definition is achieved, and the manual adaptation cost is remarkably reduced.

The present disclosure provides a method of automating the collection and screening of resumes in which a user inputs a job description in a job input module and selects potential candidates through a resume input module and the job parsing module may extract the keywords from the job description and a resume parsing module determines keywords from the candidates resumes and a scoring module that provides a score which indicates how closely matched a resume is to the job description.

Exemplary systems include an AI-powered assistant that automatically analyzes available data to create a contextual framework based on the nature of the query, the persona, and the permissions of the user, allowing it to provide relevant and personalized responses to user queries. The system can also generate contextual personas based on job descriptions within an organization, respond to specific tasks associated with roles, and integrate with network appliances and flow data to embed AI-generated insights into logging streams and ensure compliance with defined policies.

Embodiments are directed to managing skill proficiencies. Declared skills may be determined based on a job description and natural language processing (NLP) actions declared in one or more extraction models. An inference prompt for a large language model (LLM) may be generated based on the job description such that the job description and the declared skills may be included in the inference prompt. The LLM may be trained with the inference prompt to generate a response such that the inference prompt may be iteratively updated based on validations of the response. The LLM may be retrained with the updated inference prompt to generate an updated response that includes the inferred skills that may be separate from the declared skills. A job profile that corresponds to the job description may be updated to include the declared skills and the separate inferred skills.

A triggering event is received. A paging entity and an action entity are identified based on a job description associated with the triggering event. Job data is retrieved from a data source based on the paging entity. The pagination engine is integrated with a plurality of data sources for job data retrieval. An action on the job data is performed based on the action entity. The action engine is integrated with a plurality of downstream components for action processing.

The invention relates to a large language model-based job matching scene query understanding method. The method comprises the following steps of: receiving a job name, a job description and job requirement information input by a user; performing noise filtering and coding normalization processing on the input post information to generate a standardized text; geography and organization entities are extracted through a named entity recognition model, cue words are finely adjusted through a large language model, structural constraint conditions are formed by technical recognition educational background, working years and ages, and a condition analysis template is dynamically generated. And finally, through a large language model, verifying the accuracy of the keyword items, and allocating weight coefficients for the lexical items. Therefore, the efficiency is remarkably improved. Through automatic semantic analysis of a large language model, manual deep intervention of professional knowledge learning of posts is not needed, and core information such as post responsibilities and skill requirements can be rapidly extracted and converted into semantic retrieval features.