11621 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 example operation may include one or more of receiving a sequence of inputs from a user during a conversation between the user and a chatbot within a chat window of a software application, executing a large language model (LLM) on each input from the user to determine a next prompt to output via the chatbot, respectively, wherein each execution of the LLM includes a new chat input from the user and a most-recent state of the conversation between the user and the chatbot within the chat window, and displaying the next prompt within a chat window on a user device.

A method of electronic communication assistance involves receiving a partial electronic communication at an AI assistant computing facility from a first electronic identifier linked to a first user. This communication includes content associated with both the first user and a second user. The method extracts the communication context and encodes the partial communication for processing, creating an encoded version. It retrieves the first user's communication profile from a database using their identifier, containing user attributes, and retrieves the second user's profile similarly. The encoded communication is then processed by a processor to generate a compositional change using at least one of the communication context, the first user communication attribute, or the second user communication attribute. Finally, a revised electronic communication is generated from the partial communication and the compositional change.

An example operation may include one or more of training a large language model (LLM) to learn credit card data via execution of the LLM on content from one or more credit card documents, executing the LLM to generate a sequence of prompts which are output to a user via a chatbot within a chat window of a software application, receiving responses to the sequence of prompts for the user via the chat window of the software application, and retraining the LLM model to further learn credit card data via execution of the LLM on a combination of the sequence of prompts and the received responses.

An example operation may include one or more of receiving a conversation of a user, identifying a goal of the user from the conversation, identifying a different user that is associated with the identified goal of the conversation, generating a call script comprising a description of content therein to be discussed with a different user based on execution of a generative artificial intelligence (GenAI) model on the identified goal, and integrating the call script into a digital calendar of the different user.

This disclosure relates to methods, systems, and devices for AI / ML assisted management of a radio access network (RAN). In particular, an LLM is employed to generate answer to user queries, for example in the form of code that can be executed upon streaming data from the RAN, including performance management and control management databases. To provide prompts the LLM can understand, an LLM agent employs a knowledge base and experience base to transform the language of the queries from the highly technical telecommunications domain to a general domain. Frequently asked questions and their answers may be stored in the experience base and used instead of using the LLM.

A system and method for facilitating natural language conversations between customers and vendors for product purchases is provided. The system ingests product catalogs from vendors, normalizes the data, and provides a conversational interface for customers to query the catalogs. In some examples, a chatbot system receives a natural language query from a customer about a product in a chat interface, identifies vendors offering that product by searching uploaded product catalogs, determines available inventory for the product by querying the catalogs, and generates a natural language response to the customer using the vendor and inventory information. The system can extract product details from the query, search based on those details, rank and recommend vendors and products, update user profiles, offer purchase incentives, and complete transactions within the conversation. The system handles the conversational and technical aspects to enable natural dialog between businesses and customers regarding products.

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.

This disclosure improves computer functionality by enabling various hierarchies of chatbot application programs operative based on data structures containing unstructured texts. Therefore, such hierarchies enable some chatbot application programs to manage other chatbot application programs, which improves virtual assistance, reduces programming efforts, customizes output by user types, and enhances process management.

Systems and methods for generating a message that is to be sent electronically to a user include the use of an artificial intelligence or large language model (AI / LLM) service to provide recommendations about how a message could be changed to improve customer engagement with the message. The systems and methods can present an individual generating the message with options for making changes to a draft of the message, and the individual can then select the changes that are to be made to create a final version of the message. In some instances, the systems and methods include analyzing the change recommendations provided by a AI / LLM service to ensure they will actually improve customer engagement and not clash with or impair the original purpose of the message.

Provided in the embodiments of the present disclosure are a dialogue content generation method, a model training method, a prompt text generation method, a device and a storage medium. The dialogue content generation method comprises: receiving an input question; determining whether the question needs to be answered in combination with external knowledge; if the question needs to be answered in combination with external knowledge, acquiring target knowledge from a preset knowledge base on the basis of the question, generating prompt text corresponding to the target knowledge, inputting into a dialogue content generation model the question, the target knowledge and the prompt text corresponding to the target knowledge, and generating dialogue content by means of the dialogue content generation model, wherein the prompt text is used for prompting the dialogue content generation model to answer the question with reference to the target knowledge; and if the question does not need to be answered in combination with external knowledge, inputting the question into the dialogue content generation model, and generating dialogue content by means of the dialogue content generation model, wherein the dialogue content generation model is obtained by means of performing fine tuning on a pre-trained language model.

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.

The invention provides an intelligent human resource training recommendation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining post data, skill data, performance data, training records and development target data of a target, building an employee database, and constructing a knowledge graph; post data, skill levels, performance expressions, training records and individual development targets of the employees are integrated to form a comprehensive portrait; dividing all courses according to difficulty levels, selecting courses with corresponding difficulty according to post requirements and performance data of the employees, and generating a group of preliminary course lists matched with current skill levels and post requirements of the employees; semantic analysis is carried out on skill requirements of employee portraits and recommended course contents, a semantic analysis result is obtained, and according to employee portrait data, a semantic matching algorithm is used to calculate the matching degree between the employees and each training content; and according to the matching degree scores of the employees and the training contents, generating a course list subjected to semantic matching optimization.

Methods, systems, and computer-readable media for causal training of physics-informed neural networks (PINNs). The shortcoming of conventional PINNs may be due to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. This is a fundamental limitation and a key source of error that ultimately steers FINN models to converge towards erroneous solutions. Methods can include a re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. This modification alone is enough to introduce significant accuracy improvements, allowing us to tackle problems that have remained elusive to PINNs.

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.

The invention relates to the technical field of computer data processing, in particular to an automatic Excel template data backfilling method based on an LLM semantic understanding technology, which is based on the application of a large language model LLM, a natural language processing NLP, a natural language to NL2SQL and an automatic data filling technology. Through semantic understanding and the NL2SQL technology, efficient and accurate extraction and filling of template data are achieved, the problems that an existing method is low in efficiency and prone to making mistakes are solved, the working efficiency and data quality under the scenes of grassroots social governance and the like are improved, and the burden of workers is relieved.

The invention discloses a smart home voice interaction control method and system, and relates to the technical field of smart home. The problems that in the prior art, the intention of a user cannot be accurately understood through non-voice information such as emotion and expression of the user, a control strategy is solidified and lacks individuation, and user requirements and emotion cannot be met in real time are solved. By fusing voice and non-voice information, accurate recognition of user intentions is enhanced, recognition accuracy of voice instructions is improved, emotional states and requirements of users are understood more comprehensively, particularly in a noisy environment or a complex situation, recognition and response effects of the system are obviously improved, and user experience is improved. By utilizing the emotion analysis and context understanding technology of the user, the interaction mode is dynamically adjusted according to the emotion state of the user, the emotion demands of different users can be flexibly adapted, more humanized services are provided, the control strategy is continuously optimized according to the feedback of the user, the user satisfaction is improved, and the user experience is improved. Therefore, the intelligent degree and the user experience of the smart home system are improved.

The present disclosure relates to using a large language model (LLM), provided with user context information and user high-level goal information to generate contextualized action recommendations that can help the user achieve the high-level goal(s). In one exemplary embodiment, a user system, and an AI action recommendation system that is associated with an LLM, are communicatively coupled and cooperatively implement a contextualized action recommendation virtual assistant. Input data comprising personal information data of the user that includes at least one high-level goal of the user, and user context data obtained from the user system can be collected and used to generate a prompt that is input to the LLM. The LLM can then generate a contextualized action recommendation for the user based on the prompt, and the contextualized action recommendation can be presented to the user via a virtual assistant user interface on a display of the user system.

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 multimedia interactive teaching and examination system and method, and relates to the technical field of education informationization, and the method comprises the steps: obtaining user information and a learning state, and generating a learning portrait; based on the learning portrait, utilizing the generative AI to dynamically generate personalized teaching content, and marking the abnormal state of the user in real time; storing an abnormal state marking result of the user to a dynamic feature field of the learning portrait, and dynamically optimizing a learning path; based on the optimized learning path, a personalized assessment task is dynamically generated, and answer data and learning behavior data of the user are recorded; and performing multi-dimensional evaluation on the performance of the user through a semantic analysis method by utilizing the answer data and the learning behavior data. According to the method, through the comprehensive analysis model and the multi-modal feature vector, multi-dimensional evaluation of the performance of the learner is realized, multiple aspects of knowledge mastery, behavior performance, emotional state and the like are covered, and detailed feedback and improvement suggestions are provided for the learner.

The present disclosure, among other benefits, provide a technological solution which leverages electronic templates, information systems, and / or machine learning to generate gaming tickets which automatically generate and provide natural language descriptions of gaming opportunities. Aspects disclosed herein provide a wagering ticket win curation system, integrated with an online and / or retail sportsbook platform, to assist players during bet selection and placement, additionally documenting the winning outcomes of a placed wager in a plain language, in a manner that is easy to comprehend.

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 method includes creating, via a server, a plurality of virtualized human personalities associated with respective human users; receiving, via the server, a cyberattack message; determining, via the server, the cyberattack message targets a human user of the respective human users; selecting, via the server, a virtualized human personality of the plurality of virtualized human personalities based on the virtualized human personality being associated with the human user targeted by the cyberattack message; and responding, via the server, to the cyberattack message using the virtualized human personality selected from the plurality of virtualized human personalities.

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 apparatus and method are provided, which provide automated analysis and generation of marketing or advertising content, e.g., using large language models (LLMs). The apparatus extracts specific insights from input advertisements, such as needs served, brand personas, products advertised, target audiences, tone, and topical categories. These insights are summarized in the formats commonly used in digital marketing, including brand evaluations, comparative analyses of campaigns, possible future advertising content examples, and examples of user personas together with the imaginary persona stories supporting them. The apparatus may leverage multi-modal prompt engineering to have LLM identify key features of advertisements, generalize analyses, present examples, and generate customer personas, stories, marketing content examples. Sample outputs include brand values and goals identification, persona analysis with examples, campaign differentiators comparison, and Artificial Intelligence (AI)-enhanced customer persona generation. The automation and scalability of such formerly manual marketing tasks provide actionable insights to facilitate rapid and informed decision-making.

A data processing system for providing a service to extract information from a resource includes: a network interface for communicating over a computer network; a scraper tool to receive user instruction specifying a target resource and to extract content from the specified resource, wherein the user instruction further specifies a desired restructuring of the extracted content; and a prompt generator to structure the extracted content into a prompt for an Artificial Intelligence (AI) model, the prompt further directing the AI model to restructure the extracted content based on the user instruction. The prompt generator is to call the AI model with the generated prompt. The service is to receive restructured content from the AI model and provide the restructured content to a workstation submitting the user instruction, the restructured content presenting the content of the target resource in a form according to the user instruction.

A system for implementing blended learning, including: • a central processing unit (CPU) configured to receive, process and store data from a range of user interfaces, learning management software modules, classroom interaction devices and cloud-based data storage; • an AI engine integrated into the CPU, configured to analyze students' interactions with educational content, classroom activities, and performance data in real time, with the AI ​​engine applying machine learning techniques to create personalized learning paths for each student based on their individual learning style, past performance, and engagement patterns; • A range of devices for classroom interaction, including at least one smartboard, tablet, or interactive projector, each configured to capture real-time data on classroom activities such as student participation, oral responses, and collaborative tasks. The captured data is then transferred to the CPU for analysis by the AI ​​engine to refine and dynamically adapt learning paths for each student; • a student user interface, wherein the student interface displays dynamically generated content tailored to the student's progress and performance, and wherein the content includes multimedia materials such as videos, tests and reading material adapted based on the student's interaction history and learning needs; • a CPU-connected teacher interface, wherein the teacher interface displays real-time analytics and insights from student data, including performance trends, engagement levels, and potential learning gaps, and wherein the AI ​​engine provides the teacher with actionable recommendations for adapting teaching strategies, resources, and interventions based on the individual needs of the students and their performance in class; • a cloud-based storage system, whereby the system securely stores student profiles, historical performance data and learning content, and whereby the CPU retrieves, updates and stores data from the cloud-based system in real time to ensure continuous adaptation of the learning experience.

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 relates to the technical field of computers, in particular to a personalized learning recommendation method and device based on AI workflow and electronic equipment, after a user authorizes, learning behavior data of the user on a teaching platform is collected in real time, based on the collected data, the current learning state and ability level of the user are evaluated, the learning requirement is determined, and according to the learning requirement, the learning state and ability level of the user are recommended. According to the method, the difficulty encountered when a user learns course content is predicted, a course set suitable for current learning of the user is screened out from a preset course library according to a prediction result and learning requirements, and a personalized course recommendation list is generated in combination with interest preference and real-time learning behavior data of the user. Meanwhile, by collecting and analyzing feedback suggestions of the user on the recommended course and adjusting the recommendation strategy in real time according to the feedback, immediate feedback and recommendation adjustment on new questions or demands of the user are achieved.

A disclosed method facilitates AI-generation of a customized email per a methodology that significantly reduces the risk of the customized email including hallucinated facts or undesirable personal identity information (PII). The method includes identifying an email template and a recipient identifier that identifies a recipient of the customized email based on user inputs to an email application; mining contextual data stored in association with the recipient identifier; generating a large language model (LLM) prompt based on the email template and the contextual data; providing the LLM prompt as input to a trained large language model (LLM); receiving the customized email as an output from the LLM; and returning the customized email to the email application for display within a user interface.