77 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.

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.

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 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.

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.

A method and system for generating recommendations and personalized learning paths is disclosed. The system helps identify users' skill gaps through an Agentic AI tutor, which provides tailored instructions on the projects to build a complete portfolio and achieve job qualification. This qualification process, informed by industry standards, job descriptions, and hiring manager input, accelerates the employment by automatically recommending qualified candidate to hiring managers. In some embodiments, the method includes receiving a first set of data from a first user, generating recommendations for the first user by processing the first set of data using a non-parametric algorithm, using AI models to generate a personalized learning path for the first user based on the recommendations, providing guidance to the first user through verified activities aligned with the personalized learning path, and dynamically displaying the personalized learning path to the first user and one or more second users.

The invention discloses a person and post matching method and device, equipment, a storage medium and a program product, and relates to the technical field of human resource management, and the method comprises the steps: obtaining a post specification inputted by a user, and carrying out the analysis and standardization processing of the post specification, and obtaining a processed post specification; matching in a preset candidate database by combining the large model subjected to fine tuning training with a first requirement in the processed post specification to obtain candidate members; and in combination with a second requirement in the post specification after processing, scoring the candidate persons through the large model after fine tuning training to obtain final matching persons. According to the method, personnel are screened according to the first requirement in the processed post specification based on the large model subjected to fine tuning training, and then scoring and sorting are performed according to the second requirement for the screening result, so that more accurate post personnel recommendation is realized, and the accuracy, real-time performance and interpretability of personnel and post matching are improved.

The present invention relates to the fields of Computer Science, Artificial Intelligence, Machine Learning and Deep Learning, specifically focusing on Human Resources technology, automated interviewing, and evaluation processes. The present invention discloses various embodiments of an AI-driven interview system and method enabling automated and structured interviews of various prospective candidates wherein data from structured and unstructured job description texts (201) are extracted using LLM / NLPs (706) and contextual questions are framed. The embodiments disclosed is also adapted to evaluate and assess the skill of the interview candidate (110) on one or more parameters and assign a rating (113) to the user using LLMs. The embodiments disclosed are also adapted to artificially generate video of interviewer (117), perform fraud detection (111) and incorporate redundancy for human intervention (610).

Embodiments are directed to managing data for skill extraction pipeline. Skill spans may be determined based on skill information associated with job descriptions such that each skill span includes words included in the skill information. A comparison of the skill spans with other skill spans associated with a skill knowledge graph may be generated. Skill nodes may be generated for the unmatched skill spans. The new skill nodes may be integrated into the skill knowledge graph. Accordingly, the skill graph may be automatically updated for use in job / career applications including report generation.

An artificial intelligence (AI)-driven system and a method for orchestrating and automating a talent acquisition process are disclosed. The AI-driven system comprises a job description generating subsystem, a resume screening subsystem, an interview link generating subsystem, an anomaly detection subsystem, a report generating subsystem, and an autonomous decision-making subsystem. The job description generating subsystem generates precise job descriptions by analyzing hiring needs of organizations The resume screening subsystem evaluates one or more resumes against pre-defined criteria in the job descriptions. The interview link generating subsystem schedules interviews by generating interview links. The anomaly detection subsystem monitors suspicious behaviors of one or more candidates during the interview. The report generating subsystem generates a comprehensive final report that consolidates all aspects of an interview process. The autonomous decision-making subsystem makes decisions at each phase of the talent acquisition process using a cognitive architecture and automatically progresses to subsequent phases.

The invention discloses a resume analysis and scoring method, device and equipment based on a large model, and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: fusing the multi-modal features of an image verification code, and obtaining a target resume based on a processing result; based on an initial paragraph segmentation result obtained after the target resume is segmented by a preset paragraph length, marking a target attention field in the target paragraph segmentation result; extracting key information of a target concerned field based on a resume type by using the large model, merging the key information based on text similarity, comparing obtained initial structured data with a target resume to obtain a difference result, and processing the initial structured data based on the difference result by using the large model to obtain target structured data; and constructing a label system based on the post description information, performing weight distribution on the label system to obtain a weight distribution result, and scoring the target structured data by using the weight tree model and the weight distribution result to obtain a scoring result. Therefore, the resume processing efficiency can be improved.

The invention provides a multi-solver CFD self-correction method and system based on semantic orchestration, and the method comprises the steps: carrying out the retrieval and slot filling processing of multi-modal input, and generating a simulation intention intermediate representation; carrying out structured verification, default completion and mechanism examination to obtain reachability classification; mapping is established for a CFD solver, and short-distance concurrent trial running and preferential long-distance running are carried out on an equivalent example and job description compiled into a target solver through a mixed strategy; the method comprises the following steps: collecting a structured fingerprint in a running period, generating a minimum change set only reaching a necessary field when a triggering condition is established, writing back to an intention layer, and automatically recompiling and continuing running after the intention layer is formed; and introducing a physical information neural network and a digital twinborn body to provide suggestions or approximate fields, generating reports, and performing Hash signature archiving on measurement point and section data. According to the method, the correctness and the traceability are ensured, and meanwhile, the one-time runnability, the cross-solver transportability and the engineering efficiency are remarkably improved.

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.

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.

System for automating job matching comprises a user interface 102 for uploading job descriptions 114 and resumes 116, and displaying ranked results of candidate suitability of each job requirement. A database directory 104 stores the job descriptions and resumes. A document extension module 106 examines the file extensions of the stored documents to verify a file type or format. A processing module 108 comprises a large language model (LLM) 110 for processing and interpreting resume content through machine-readable instructions (e.g. natural language processing). This may include generating embeddings (by tokenising the documents, semantically analysing them, and assessing a textual context) and retrievers for each document, merging retrievers for respective resume and job description into one document, and creating filters for embedding and compressing the merged document. A master retriever and prompt template may be created. The LLM is coupled to vector database 118 for performing similarity searching with an embedding vector. A scoring module 112 (e.g. having rubric-based scoring templates and matching algorithm) scores candidates based on suitability for each job requirement. The scoring module may include computing scores based on relevant experience (112a, fig.1A), projects (112b, fig.1A), qualifications (112c, fig.1A), and skills (112d, fig.1A) in the candidate resumes.

The invention discloses a job configuration information adjusting method and device and electronic equipment. Relates to the distributed field, and the method comprises the following steps: determining whether there is an abnormal job in a historical job set according to a multi-tuple information table, the multi-tuple information table at least comprising a job dependence object, job execution information and job description of each historical job; under the condition that the abnormal job exists in the historical job set, adjusting the job dependence object of the abnormal job according to the job dependence object and the job execution information of each historical job in the multi-tuple information table to obtain first adjustment information; adjusting the job execution information of the abnormal job according to the target job description of the abnormal job to obtain second adjustment information; and adjusting the initial configuration information of the abnormal operation according to the first adjustment information and the second adjustment information to obtain target configuration information. Through the method and the device, the problem of relatively low efficiency of adjusting the operation configuration information in related technologies is solved.

This invention discloses a resume screening and display method and system for human resource management. It constructs a keyword-dimension mapping table based on job description text using a natural language processing engine; analyzes keyword frequency, syntactic position, modification strength, and semantic relevance to generate a job requirement intensity vector; retrieves historical recruitment data based on job meta-information to generate a historical weight vector; adaptively calculates a smoothing coefficient based on the amount of historical data; and integrates dual-source weights to generate a dynamic weight vector; standardizes the scoring of each dimension of the resume, using a normalized formula based only on valid dimensions to calculate the matching degree, avoiding score distortion caused by missing fields; and finally displays the results sorted by matching degree. This invention abandons the preset weight mode, achieving dynamic screening with a tailored approach for each job, improving matching accuracy, operational efficiency, and the quality of human-machine collaborative decision-making, providing a scientific, efficient, and transparent intelligent solution for human resource management.

The invention discloses a position publishing platform based on artificial intelligence and electronic equipment, and the position publishing platform executes the following processing after being started: carrying out the reasoning analysis of enterprise business data and market data of a target enterprise through an AI position mining large model, and determining a list of positions needing to be recruited by the target enterprise; in response to a target position selected by the target enterprise from the recommended positions, calling an AI position generation large model to perfect multiple contents of the target position according to basic information of the target position and recruitment requirements of a knowledge base in the reference enterprise and / or historical position description information of the target position, and generating target position description information; and performing reasoning analysis on the position features in the target position description information and the target recruitment crowd through an AI position publishing and tracking large model, and selecting a target recruitment publishing channel to publish a target position. According to the invention, automation of the position release process is realized, and the recruitment efficiency and quality of enterprises are improved.

The invention relates to the field of human resource management, and discloses a talent pool storage and intelligent matching management method and system based on data driving, and the method comprises the steps: firstly obtaining a talent storage pool, and collecting comprehensive resume data to analyze the growth potential dimension of stored talents; constructing a dimension association network, collecting post description texts and performance target data, extracting responsibility key items and achievement constraint items, and analyzing core demand features; a feature demand vector is generated, a demand weight point is positioned, and a post adaptation base value is calculated; then updating talent potential in combination with an increment updating mechanism, extracting increment potential characteristics and calculating a potential increment value; and finally, generating a dynamic adaptation list based on bidirectional optimization of the value, feeding back rematching to the adaptive talent when a task is newly added, and formulating an intelligent matching scheme. According to the invention, the adaptation accuracy of talent resource configuration can be improved.

This application provides a method for finding employees based on OKRs, an electronic device, a storage medium, and a program product, relating to the field of computer technology. This method integrates OKR data with departmental responsibility data into user job description data. It constructs a multi-level matching optimization logic around the employee search request, from initial matching degree to revised matching degree and then to target matching degree, combining accuracy, timeliness, and practicality tailored to actual enterprise collaboration. This method abandons the drawbacks of traditional employee search methods that rely on static information, incorporating OKR data reflecting employees' current work priorities into the core matching dimension. This ensures that the personnel matching results highly align with the enterprise's real-time business needs, fundamentally improving the matching degree. Simultaneously, by adjusting the initial matching degree through capability assessment parameters, it optimizes the matching results based on employees' actual ability levels. Furthermore, by combining organizational structure hierarchy to determine the target matching degree, it fully considers the characteristics of internal collaboration levels, optimizes collaboration paths, reduces trial-and-error costs and communication costs in cross-departmental and cross-position collaboration, thereby improving the accuracy of recommendation results.

The application relates to the technical field of talent evaluation, and discloses a multi-dimensional adaptive examination generation method and system based on a dynamic skill map. The method comprises the following steps: real-time analysis of recruitment website and enterprise job description data, extraction of atomized skill phrases containing skill names, levels and correlation degrees, and generation of unified semantic representation; obtaining a standardized skill map through a large language model and transmitting the standardized skill map to a central database through a blockchain; generating an evolution strategy based on skill node weights and user historical answering characteristics, and differentiating and executing the evolution strategy by edge nodes; selecting a target edge node and configuring a skill knowledge cache; analyzing a candidate natural language answering request to generate ability matching characteristics, determining a candidate test question set, and outputting a test paper; and updating an ability evaluation model and an evolution strategy in combination with an answering result and a response time. The application can improve the authority and reliability of the evaluation result to meet the scientific and accurate needs of enterprises for talent evaluation.

The present invention provides an intelligent talent recommendation and cultivation platform and method based on a learning map and a job portrait. In the platform, a job portrait analysis module receives the input recruitment positions, conducts portrait analysis on the recruitment positions, and obtains job portraits of each recruitment position reflecting its job description and quality and ability requirements; a learning map analysis module plans the promotion path and outputs a learning map based on the promotion path; a team analysis module conducts big data analysis on the job skills, knowledge, learning courses, learning interests, development potential and behaviors of employees within the team to obtain the ability / potential information of each employee; a recommendation module recommends employees to the recruitment positions according to the ability / potential information of the employees and the job portraits of the recruitment positions. The platform utilizes information technology and big data analysis to realize the function of intelligent job matching and improve the accuracy of matching.

We will flexibly recruit workers for a single job posting. [Solution] The recruitment system described herein generates multiple job postings that include job descriptions and recruitment slots, with at least a portion of the job descriptions being common to all of them, and accepts applications for these multiple job postings. When predetermined conditions are met, the recruitment system adds at least a portion of the difference between the recruitment slots and the number of applicants for the first job posting, which is one of the multiple job postings, to the recruitment slots for the second job posting, which is one of the multiple job postings.