8115 results about "Knowledge management" patented technology

A system and method for AI-driven advertising and content generation that integrates connectionist and symbolic AI techniques to deliver personalized, contextually relevant user experiences. The invention leverages specialized agent networks, knowledge graphs, and retrieval-augmented generation to create dynamic, adaptive content with seamlessly integrated advertisements. It employs a sophisticated ad integration layer and experience broker to ensure relevance and engagement. The system prioritizes security, traceability, and user preferences while optimizing ad placement and content delivery. By bridging connectionist and symbolic AI, the platform enables immersive, tailored interactions across multiple scenarios and time horizons, enhancing user engagement and advertiser value in an AI-driven digital landscape.

An online social wager-based gaming system is disclosed, featuring dynamic cross-provider game filtering and persistent cross-provider voice-interactive group play. The system allows for automated multi-seat group game reservations and incorporates a distributed ledger for bet verification. Features include a Group Connect module for dynamic group formation and live audio across various game providers, enabling real-time play. A Professional Companion Connect module facilitates live webcam or microphone gambling sessions with professional companions, under contractual agreements. The User Games module enhances personalization by integrating user-uploaded images into the gaming environment. A cryptocurrency / blockchain-based bet tracking system ensures transparency and security in managing bet transactions through a controlled digital wallet system. This integrated platform aims to provide a cohesive, interactive, and personalized online casino experience, addressing limitations of traditional solitary online gaming by fostering social connections and enhancing user trust and engagement.

The invention discloses a teaching content dynamic adjustment system and method based on education assistance, and relates to the field of education assistance, and the system comprises a global perception module, an intelligent analysis module, a decision generation module, and a teaching content updating module. The global sensing module is used for capturing behavior details and environment data of students in a classroom and learning tracks of the students on a learning platform, and the intelligent analysis module is used for capturing behaviors of the students in the classroom and environment data of the classroom, analyzing a current teaching effect based on the captured data and sending the analyzed current teaching effect to the classroom. And an external learning platform is fused for mastering student learning. According to the teaching content dynamic adjustment system and method based on education assistance, teaching adjustment strategy suggestions can be provided for teachers, potential relations between knowledge points of different subjects can be mined through the knowledge association graph, interdisciplinary fusion teaching strategies can be made, and the comprehensive thinking ability of students can be cultivated.

A system and method for providing a digital financial advisor using fine-tuned large language models is disclosed. The system includes a data advisor application, data fusion suite advisor engine, human advising engine, a knowledge base and large language model (LLM) fine-tuning engine to create specialized language models (SLMs) that mimic specific human financial advisors. Multiple digital avatars embodying the appearance and communication style of human advisors are generated. The system processes user and client profile data, along with advisor-specific information, to provide personalized financial advice. It handles client queries, escalating complex issues to human advisors when necessary. The digital advisor system communicates with profile datastores, user devices, and external data sources for comprehensive financial analysis. Continuous learning capabilities allow the system to improve its performance based on interactions and feedback, combining AI efficiency with personalized human-like advisory.

Systems and methods for controlling rights related to digital knowledge are disclosed. A sample system may include an input system to receive digital knowledge from a user, a tokenization system to tokenize the digital knowledge and a ledger management system to create, manage, and store things on a distributed ledger and provide provable access to the digital knowledge. A smart contract system may create a smart contract including triggering action is and respond with a defined smart contract action on an occurrence of the triggering event. The smart contract system may also process commitments to the smart contract.

The invention discloses an intelligent auxiliary teacher lesson preparation system and method based on an education large model. The method comprises the following steps: S1, generating a standardized teacher lesson preparation data set; s2, constructing a teaching knowledge graph; s3, generating preliminary intelligent lesson preparation content; s4, presenting the preliminarily generated intelligent lesson preparation content, and providing an interactive interface combining a natural language and voice recognition; s5, generating the adjusted intelligent lesson preparation content; and S6, according to historical lesson preparation data of the teacher and the interactive feedback data, performing adaptive learning and optimization on the intelligent lesson preparation content, adjusting weights of key knowledge points in the teaching knowledge graph, updating a content generation strategy of the large education model, and generating a final intelligent lesson preparation scheme. According to the method, the response capability of the intelligently generated content to the teaching intention of a teacher is remarkably improved, the teaching content and the test question style are supported to dynamically cooperate with classroom feedback along with the teaching rhythm, and an intelligent decision support basis is provided for large-scale personalized teaching.

A computer-implemented method includes receiving patient responses over a network from a recorded screening interview, utilizing a custom Large Language Model (LLM) to generate transcriptions and insights from the audio, performing video sentiment analysis to assess emotional states, and based on these data, generating an AI model to predict risk levels for various mental health conditions. The method further comprises presenting personalized questions based on previous screening insights, creating longitudinal summaries of patient histories, continuously improving the AI models through reinforcement learning, and integrating a recommendation engine to suggest targeted interventions. Additionally, the method includes tracking outcomes over time and enabling causal inference across multiple screenings, thereby providing a comprehensive platform for mental health assessment, intervention, and outcomes tracking.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

The invention discloses an AIGC enabling auxiliary system for college second foreign language teaching and an implementation method. The system is cooperatively operated by five core modules; the intelligent voice recognition and user demand analysis module analyzes the learning demand of a learner by using a voice-to-text technology, and assists the customization of teaching content; the AIGC content generation module gathers multi-source data, generates personalized teaching resources by using a frontier model and continuously updates and evolves the personalized teaching resources; the learning progress management and evaluation module collects learning data, dynamically adjusts a personalized learning plan and provides evaluation and feedback; the real-time interaction and feedback module builds a multi-dimensional interaction channel, collects feedback of all parties, analyzes and processes the feedback and presents an application result; the multi-platform support module guarantees the stable operation of the system on a plurality of operating systems and devices, and efficiently completes data synchronization sharing and performance optimization adaptation. According to the invention, an all-around learning environment is established for second foreign language learners in universities, and the learning efficiency and experience are significantly improved.

The Trust-Informed Engagement and Restraint (TIER) system is a modular behavioral governance architecture for conversational AI that enables dynamic, real-time enforcement of trust-aligned interaction policies. The system comprises a Behavioral Governance Framework defining policy rules and an Enforcement Wrapper hosting Core Modules that operate externally to the AI model. These modules regulate behavioral traits including trust calibration, emotional tone, conversational containment, authority modulation, and cross-modal consistency. TIER continuously monitors interaction metrics and applies policy-driven constraints during live sessions without requiring model retraining or internal access. Unlike static filters or post-hoc moderation systems, TIER provides proactive, session-aware behavioral governance with auditable enforcement across domains. The architecture supports model-agnostic deployment in regulated and sensitive environments such as healthcare, finance, legal services, and intelligent assistance platforms.

Natural language communications may be automated using language models and session contexts. A first session context may be created for communications between a language model and a first entity. Prompts may be generated using this session context, and responses from the language model may be used to communicate with the first entity. A second session context may also be created for communications between a language model and a second entity. Context transfer information may be generated by processing the first session context with a language model. The context transfer information may be added to the second session context. The second session context with the context transfer information may then be used to generate a communication to the second entity using a language model.

A System for providing Interactive Transaction Frameworks, utilizing biometric authentication and semantic web technologies is disclosed. The system registers users with a unique biometric-based identifier, and employs ontologies, schemas, and knowledge graphs to create a semantic foundation. A Large Language Model integrated with the Knowledge Graph Module enables intelligent processing of Interactive Transaction data. The system composes Interactive Transactions with fields mapped to semantic triples, maintains them in an Interactive Transaction Library, and manages user permissions. A Transaction Publishing Platform facilitates Interactive Transaction distribution and submission-response cycles. The system refines inputs and responses, determining outcomes based on these interactions. The system offers a sophisticated, secure, and adaptable approach to Interactive Transaction management and processing across various domains.

An Automated AI-Driven Lifestyle Orchestration System proactively manages a user's daily life by intelligently populating calendars with relevant, time-sensitive marketing objects. This is achieved through sophisticated AI-driven analysis of user directives, preferences, and real-time location data obtained via computing devices like smartphones. The system leverages advanced media identification modules employing fingerprinting and watermarking to accurately match user-captured media (via a Percipient Sample Pack (PSP)) to specific content and associated products. It facilitates dynamic commerce through hierarchical linked lists for purchasing authentic or similar items, supported by an affiliate program. This inventive solution significantly improves personal organization and digital commerce by transforming passive interaction into a proactive, intelligent, and monetizable user experience.

The invention discloses a medical health question and answer method and device based on an intelligent agent and a medium, and relates to the technical field of artificial intelligence medical question and answer, and the method comprises the steps: receiving an intention analysis report through a master control intelligent agent, accessing a dynamic dialogue context pool, and generating an intelligent agent cooperation instruction according to the state of the intention analysis report and the state of the dynamic dialogue context pool; performing credibility evaluation on the preliminary medical answer report by using a credibility calibration agent, generating a confidence score and an evidence conflict level, and performing multi-dimensional weighted fusion and risk mode recognition by using a dynamic risk evaluation strategy to generate a diversified disposal instruction; and when the diversified treatment instruction is issuing permission, performing safety compliance check on the preliminary medical answer report to generate compliance medical answers. According to the invention, multi-dimensional control of credibility and security of medical answers is realized, and finally the beneficial effects of providing personalized medical questions and answers and ensuring that information is real, reliable, compliant and safe are achieved.

The invention relates to a multi-agent medical diagnosis and treatment system which comprises a plurality of agents. The plurality of agents comprise: a task planning agent analyzes input content and generates a corresponding task process, and the task planning agent sequentially issues task instructions to a data management agent, a tool use agent and a medical decision agent according to the task process; the task instructions comprise a data task instruction, a tool task instruction and a decision task instruction; the data management agent extracts patient data from the database and sends the patient data to the task planning agent according to a data task instruction issued by the task planning agent; the tool using agent performs feature extraction processing on the patient data according to the patient data and the tool task instruction issued by the task planning agent so as to obtain key features and send the key features to the task planning agent; and the medical decision-making agent carries out diagnosis and treatment reasoning according to the key features and the decision-making task instruction issued by the task planning agent, and a diagnosis report is obtained and displayed.

An example operation may include at least one of storing first interaction content with a service provider; receiving second interaction content from a communication session between a source device and a service provider device of the service provider; identifying at least one contextual attribute associated with the source device; determining a response based on execution of at least one large language models (LLMs) on the second interaction content, the at least one contextual attribute associated with the source device, and the first interaction content with the service provider; and outputting the response to at least one of the source device and the service provider device during the communication session.

The invention discloses an interactive processing method and device based on course management, and relates to the technical field of intelligent education, and the method comprises the steps: collecting student information, carrying out the preprocessing of the student information, and generating an initial learning path according to the preprocessed student information; in the process that the student learns according to the initial learning path, learning behavior data of the student is monitored, and a personalized learning path is generated according to the learning behavior data of the student; monitoring emotion change data, cognitive load data and attention level data of the trainee in the personalized learning path in real time, constructing a learning state evaluation model, and evaluating the current learning state of the trainee; and dynamically generating an immersive learning situation according to the current learning state of the student. According to the invention, the pertinence and utilization efficiency of educational resources are improved, and the enthusiasm and effect of learning are enhanced.

Systems and methods for receiving an enterprise resource dataset associated with a customer from an enterprise application associated with the customer, the enterprise resource dataset including a plurality of financial inputs captured on or before a first date, a plurality of customer recommendations, and a plurality of recommendation replies, receiving, from a first machine learning model, a predicted customer state including one or more predicted financial outputs corresponding with the predicted customer state at a second date subsequent to the first date, providing the plurality of financial inputs to a second machine learning model trained to predict a customer recommendations, determining, a customer recommendation corresponding to an action to be performed at a third date subsequent to the first date, wherein performing the action causes the first machine learning model to predict an updated customer state differing from the predicted customer state.

An automated conversational multi-player gaming platform is described. A machine-learning model is trained to use a natural language processor and a speech synthesizer as an automated dealer communicating with players of a game of chance. The trained machine-learning model receives requests from people to become players of a game of chance, outputs private game content associated with the game of chance to the players' private user interfaces, and outputs public game content associated with the game of chance to the players' public user interface. The trained machine-learning model receives selections of game playing options and wager values associated with the game of chance from the players via their private user interfaces, and outputs information identifying one player as a winner of the game of chance and a credit of the winner's wager value to the players' public user interface and / or the winner's private user interface.

The invention relates to the technical field of data processing, and discloses a coal-based solid waste physicochemical property classification method and system based on regional ecological characteristics. The method comprises the following steps: collecting coal-based solid waste data, and establishing a resource and ecological database; analyzing association between the resource characteristics and the ecological elements, and obtaining association parameters and influence factors; performing classification modeling to form a classification model and characteristic prediction; evaluating a commercial value, and generating a value score and a market scheme; monitoring the whole life cycle, and obtaining circulation data and benefit evaluation; analyzing and deciding, and making a resource management scheme. According to the method, correlation analysis of coal-based solid waste resource characteristics and regional ecological elements is realized, a scientific classification model is constructed, and a full-life-cycle resource management scheme is provided, so that the utilization efficiency and the management level of coal-based solid waste resources are improved.

The invention discloses a method for constructing a nursing field text annotation corpus based on deep learning, and relates to the field of artificial intelligence technology and medical information processing. Comprising a data collection and preprocessing step, a BERT-based nursing field entity recognition model construction and training step, an automatic labeling and post-processing step, a corpus construction and management step and an application and intelligent support step. According to the method, the problems of efficiency and accuracy of text labeling in the nursing field are solved, and the continuously developing and changing text data processing requirements in the nursing field can be better met. The entity recognition model architecture specially aiming at the text characteristics in the nursing field is constructed based on deep learning, and challenges can be effectively handled when complex and diversified nursing text data is processed. And meanwhile, by utilizing the automatic labeling capability of deep learning, the labeling efficiency is improved, the manpower and time cost is reduced, and a new direction, a new mode and new experience are provided for nursing informatization development.

The present disclosure relates generally to a gaming system, device, and method supportive of rewarding responsible gaming behaviors. An illustrative method includes presenting a representation of the electronic game on a display device, where the representation of the electronic game includes displaying a plurality of game features, executing the electronic game, determining, during execution of the electronic game, that a player of the electronic game has taken an action that corresponds to a responsible gaming action, correlating the responsible gaming action to a reward for the player, and presenting information to the player that describes the reward for the player along with instructions for redeeming the reward.

Systems and methods for providing a guidance function for use of large language models within educational environments are provided herein. In an example, a system includes instructions to receive, from a first client device, a request to generate content within an application and initiate, by the application, a guidance function. The guidance function observes interactions between the first client device and a content generator and determines a first interaction between the first client device and the content generator. The guidance function also determines a first text portion generated based on the first interaction between the first client device and the content generator and generates a relationship between the first text portion and the first interaction between the first client device and the content generator. The guidance function may also store the relationship between the first text portion and the first interaction.

The invention relates to the technical field of information, and discloses an enterprise employee welfare gift bag automatic generation method, and the method comprises the following steps: S1, firstly providing a welfare management system for employees, and enabling the employees to log in the system to browse and autonomously purchase welfare products; s2, processing the collected employee preference data by using a data analysis algorithm; s3, an enterprise administrator sets welfare gift bag generation rules through a system interface; s4, according to the preference model of the employee and the rule set by the enterprise, screening out the commodity meeting the condition from the commodity library; s5, notification reminding is sent to the employees through the enterprise internal platform; and S6, the employee can adjust the commodity in the rule range, and evaluation and feedback are carried out on the gift bag. By collecting the preference data of the employees and analyzing and processing the data, the requirements of the employees are accurately judged, personalized customization of welfare gift bags is achieved, and the satisfaction degree of the employees to welfare is improved.

The invention relates to the technical field of intelligent education, and discloses a personalized historical exercise recommendation and automatic correction system based on a large model, and the system comprises a cross-modal data collection module which is used for collecting character input, time behavior and interaction track data of a student in an answering process, and outputting a collection result to a cognitive map construction module; and the cognitive map construction module receives the acquisition result output by the cross-modal data acquisition module, and is used for generating a cognitive map representing the mastering degree of the students on the historical knowledge points and the relationship thereof based on the acquisition result, and sending the cognitive map to a cognitive drive recommendation module and an automatic correction module. The cross-modal data acquisition module is introduced, so that dominant content answered by the students is acquired, diversified recessive behavior data such as answering duration, editing and modifying tracks, mouse hovering positions and answering pauses of the students are further acquired, and the system can surpass traditional simple answering right and wrong judgment due to the multi-dimensional data capture capability.

Techniques including receiving, by a cloud environment of a cloud service provider, a requested resource usage for a resource associated with a subscription of a user account with the cloud service provider. The techniques further include determining, as part of managing resources at a subscription level independent of managing resources at a user account level, a resource usage by the subscription. The techniques further include determining that a usage limit for the resource is greater than the resource usage. Based on the usage limit being greater than the resource usage, the techniques further include determining that a remaining resource allocation for the subscription is greater than or equal to the requested resource usage. Based on the remaining resource allocation being greater than or equal to the requested resource usage, the techniques further include providing the resource to the user account and updating the resource usage.

A method of automatic pre-prompt generation includes receiving at least one user preference from a user device, modifying a system prompt for a machine-learning language model based on the received at least one user preference to generate a modified system prompt, providing the modified system prompt as an initial input to the machine-learning language model, receiving a natural-language text prompt provided by the user to a chat application on the user device, receiving a user identifier from the user device, querying a first database with the user identifier to retrieve first information, generating a representation of the first information and the natural-language prompt, querying a second database using the representation to retrieve second information, and generating a modified text prompt based on the natural-language prompt, the first information, and the second information.

A computer-implemented system for automated document generation receives user input and processes it through artificial intelligence models to create professional documents. The system combines user-provided information with specialized knowledge databases containing domain-specific requirements and formatting standards to generate enhanced processing instructions. Trained machine learning models process these instructions to produce contextually appropriate content that addresses specific technical and legal requirements. The generated content is formatted using configurable templates to create completed documents that meet professional standards. The system provides adaptive user interfaces that adjust functionality based on user expertise levels, supports multiple operational modes including guided and automated generation, learns from example documents to improve output quality, and enables comprehensive workflow management through network-accessible services. Applications include patent applications, legal responses, technical documentation, and other specialized professional documents requiring domain expertise and regulatory compliance.

Provided are a dialogue processing method and apparatus, and an electronic device, a storage medium, a computer program product and a computer program, which relate to the technical field of computers and specifically relate to the technical fields of artificial intelligence such as large language models, natural language processing, knowledge graphs, and deep learning. The specific implementation scheme comprises: acquiring a topic of the current dialogue of a first user and a first historical dialogue library associated with the first user; determining the similarity between the topic of the current dialogue and each historical dialogue in the first historical dialogue library; on the basis of the similarities, acquiring a plurality of candidate historical dialogues from the first historical dialogue library; on the basis of the forgetting coefficient and weight of each candidate historical dialogue, acquiring a reference dialogue from the plurality of candidate historical dialogues; and generating a reply statement on the basis of the reference dialogue and the topic of the current dialogue.

Techniques are disclosed for generating a ranked list of content for a user account of a service provider. A server device can determine that a first user account of a service provider is linked with a second user account of the service provider. The server device can then determine whether the first user account has been registered with the service provider a time period less than a threshold time. In response, the server device can determine interaction information associated with the second user account and generate, using a ranking model taking the interaction information as an input, a ranked list of content offered by the service provider. The server device can then cause the user device to present content at a graphical user interface of the user device according to the ranked list of content.