337 results about "User feedback" patented technology

A system for adaptive text rendering includes an editor, a context analyzer, an adaptive text generator, and a feedback and learning engine. The editor renders a text box on a digital canvas and receives user input via the text box. The context analyzer extracts contextual information from digital content associated with the digital canvas. The adaptive text generator uses the user input, text box dimensions, and contextual information to direct an artificial intelligence (AI) model to generate text. It then formats the generated text to fit within the text box based on one or more fit criteria. The feedback and learning engine captures and evaluates user feedback related to the formatted text and generates tuning adjustments to refine the adaptive text generator or the AI model.

The invention relates to a system (100) for generating real-time causal reasoning insights in real time. The system (100) comprises a global causal graph creation module (101), a natural language query processing module (102), an information extraction module (103), a query-centric subgraph generation module (104), and an adaptive causal reasoning engine (105). The queries from users are processed to identify relevant causal relationships in the global causal graph. The adaptive causal reasoning engine (105) refines the insights iteratively based on user feedback, improving accuracy. The system (100) converts technical insights into structured narratives. A visualization module presents causal relationships through graphs and charts. The feedback mechanism allows users to correct errors and adjust outputs, facilitating user-driven data analysis and causal reasoning, delivering actionable insights for a variety of applications.

We provide the system. [Solution] A means of storing information by having users input specific professional skills and knowledge, A computing device searches for educators based on user-specified conditions and lists suitable candidates; A communication means having a call or chat function that allows users and educators to conduct online training sessions via a terminal, A computing device records the progress of self-driving technology training sessions and user feedback, and provides compensation to educators. A system that includes this.

The present disclosure relates to the technical field of electronic commerce, in particular to an e-commerce marketing method and system based on time sequence promotion, the method comprising: collecting user behavior data, commodity attribute data, marketing history data and time sequence time node data of an e-commerce platform to obtain a standardized time sequence data set; extracting user behavior time sequence features, commodity heat time sequence features and marketing effect time sequence features based on the standardized time sequence data set to construct a time sequence feature matrix; determining core promotion content, target population and promotion channels of each promotion period through a preset time sequence prediction model to generate an initial time sequence promotion strategy; performing a promotion operation at a corresponding time sequence node according to the initial time sequence promotion strategy, collecting user feedback data and promotion effect data in real time and inputting the data into the time sequence prediction model to dynamically adjust the promotion content, channel delivery ratio and time sequence node arrangement, etc., to form a time sequence promotion strategy; and performing effect evaluation on the time sequence promotion strategy based on evaluation indexes to generate an evaluation report.

The systems and methods presented enable the testing of Financial Crime Assessment and Monitoring Systems (FCAMS). At its core, the YANEZ AI Sanction Assistant powers a synthetic input generator, which produces configurable synthetic inputs based on target financial crime scenarios. An assessment module then evaluates these synthetic inputs, integrating the inputs into FCAMS and collecting results from the system under review. A reporting module analyzes the received results, producing test scores, insights, suggested actions, and a range of metrics and analytics. A feedback module gathers user feedback and incorporates it to refine the results. An AI-driven engine continuously learns from input scenarios, configurations, results, and feedback to improve synthetic input generation and the overall assessment process.The data ingestion module aggregates and processes up-to-date data from regulatory sources, commercial databases, and publicly available information, ensuring that the synthetic inputs remain comprehensive and reflect the latest financial crime trends.

The present application relates to a kind of biological image online retouching method and system supporting natural language interaction, belong to image auxiliary processing technical field.Wherein, the method includes: based on recursive data structure analysis matrix, the multi-level channel tensor is structured storage as key-value pair dictionary, and output lightweight browsing compatible data string;Multi-modal visual rendering model is constructed, and image attention weight is aggregated layer by layer, and text-image semantic correlation diagram is obtained;Based on vector database semantic matching library, the cosine similarity of high-dimensional embedding vector and candidate vector in vector database semantic matching library is calculated, and user retouching matching scheme is generated;Establish adaptive online feedback optimization mechanism, use user feedback as reward signal to adaptively adjust semantic similarity matching threshold and vector space distribution, assist in correcting semantic retrieval parameter, and convert browser end contour rendering image into interactive real-time visual output image.

A computer-implemented method of performing robotic simulation and design may include performing a simulation where a robot interacts with a user, and generating sensor data indicative of the robot interaction with the user during the simulation. The computer-implemented method may include generating discussion data indicative of user feedback from the simulation, generating form data associated with the user feedback from the simulation, and combining the sensor data, the discussion data, and the form data in a multimodal signal. The computer-implemented method may include determining a design characteristic of the robot based on the multimodal signal, and augmenting a robot model to incorporate the design characteristic using a machine learning algorithm.

The application discloses a kind of fusion training method and system for multiple cognitive abilities.The fusion training method includes: obtaining the cognitive evaluation result of user for attention, inhibition and memory;Based on the cognitive evaluation result, obtain the first training parameter related to attention and inhibition;Based on the first training parameter, obtain the second training parameter related to memory;Based on the first training parameter and the second training parameter, configure the cognitive training task of corresponding difficulty level and training duration for user;Push the cognitive training task to user for cognitive training, for simultaneously training the attention, inhibition and memory of user;Obtain the cognitive training data of user, and feedback the current cognitive training result to user.The fusion training method can integrate multidimensional cognitive ability training into one, break the common single cognitive ability training form, and help to improve and transfer the comprehensive ability of user.

The application relates to the technical field of natural language processing, can be applied to the creation content generation scene of financial technology and medical health, and discloses a creation content generation method, device, equipment and storage medium. A user initial creation requirement is received, and a structured core creation demand is extracted based on a preconfigured understanding instruction; a content first draft, a guiding question and a multi-modal prompt word are synchronously generated based on a preconfigured generation instruction, the content first draft and the guiding question are pushed to the user; user feedback information is received, knowledge demand is analyzed based on a preconfigured analysis instruction and an optimization instruction, and matched professional information is called from a preset knowledge base, a revised text is generated in combination with the feedback information and the content first draft; multi-modal content with semantic alignment is generated based on the multi-modal prompt word and the revised text, and the revised text and the multi-modal content are integrated and pushed to the user. The application realizes high-quality, personalized and automatic generation of creation content through preconfigured exclusive instructions in the whole process.

The present invention relates to a system that automatically generates 2D animations based on text and embeddings, maintaining visual and semantic consistency by combining model IDs and embeddings of characters, backgrounds, and motions. Information regarding scenes, characters, backgrounds, actions, and dialogue is extracted from input text content through natural language processing (NLP) and semantic analysis. By combining character embeddings and motion embeddings with model IDs, background images are synthesized to automatically generate animation scenes while maintaining consistency in the appearance and movement of the characters. Adaptive learning based on user feedback is performed to continuously improve the generation performance of the system, and post-production work is automated to maximize production efficiency. By enabling the efficient and consistent production of 2D animations in large-scale user environments, this system reduces production costs and time and supports non-experts in easily creating animations.

This invention discloses an online error correction and rapid learning system and method based on human-machine collaboration. The system includes a process demonstration and verification unit, which executes the learned process through demonstration or semi-automatic methods to obtain and display the execution steps and results; a multimodal feedback acquisition unit, which receives voice and text feedback, senses the user's operational trajectory during takeover, and captures screen changes before and after error correction; a feedback parsing and mapping unit, which parses user feedback into process correction signals and maps them to specific steps, conditions, or parameters; and an online process update unit, which updates the task model without restarting the application system and retains historical versions for comparison or rollback. This invention solves the problem that existing semi-structured operation process human-machine collaborative systems lack error correction for operation processes and execution results.

The application discloses a body intelligent agent interaction system and a control method, and relates to the technical field of artificial intelligence. The method comprises the following steps: a terminal sensor module is configured to collect physical signals of environment or user interaction, and generate standardized event data; a reflection processing module comprises a lightweight decision unit, which is used for receiving scene labels and user feedback information to self-optimize protected core behavior logic, and deep evolution is performed after a growth authorization signal is received; a narrative processing module is used for processing complex interaction cognition, generating scene labels and sending the scene labels to the reflection processing module, sending an inhibition instruction and a growth authorization signal to the reflection processing module, and performing context interpretation; wherein, the reflection processing module and the narrative processing module communicate through a preset interface, and the narrative processing module has no right to directly modify the core behavior logic; the system guarantees the explanation right and adjustment right of the narrative layer to the reflection layer, and prevents tampering of the core instinct at runtime through strict permission isolation.

The present disclosure provides a computer-implemented method of facilitating provisioning of an aid of a user. Further, the method may include receiving a user feedback data generated by a user. Further, the user feedback data may be associated with one or more of a service and a product provided by an entity. Further, the method may include analyzing the user feedback data. Further, the method may include determining an indication of a grievance feedback data based on a criterion of the user associated with one or more of the service and the product. Further, the method may include transmitting a communication data to a user device associated with the user and an advisor device associated with an advisor. Further, the advisor may be capable of provisioning an advisory service. Further, the advisory service may be associated with resolution of the grievance.

The application discloses a progressive question generation method based on semantic analysis and a knowledge graph. The question generation demand text is preprocessed and semantically analyzed, the core knowledge point corresponding essential keyword set and the additional condition corresponding optional limiting word set are extracted, the concept mapping is carried out in the college professional knowledge graph, the curriculum outline is associated, the hierarchical semantic constraint framework containing hard constraint and soft constraint is constructed and consistency detection is carried out, the candidate materials are obtained by using the inverted index hard matching recall and the knowledge graph soft expansion recall, the candidate questions are generated by inputting the field fine-tuning large language model, the final questions are output by carrying out multi-objective learning sorting and reordering, teaching logic checking and quality evaluation, the incremental samples are formed by receiving user feedback, the generation model and the sorting model are updated, the accuracy, diversity and controllability of the question generation are improved, and closed-loop optimization is supported.

This invention discloses a case retrieval method and system based on multi-agent collaboration, belonging to the field of legal information retrieval technology. The method includes: acquiring standardized questions that meet evaluation criteria; combining a case database to deduce selected cases corresponding to the standardized questions; calculating the matching score between the user's original question and the selected cases, and outputting the selected cases as recommendations when the matching score reaches a set threshold; simultaneously performing legal scenario analysis on the selected cases, and when the analysis results indicate that the case involves multiple legal scenario preferences, triggering scenario clarification questions to obtain user preference information, and optimizing subsequent case recommendations based on user feedback. This invention can achieve clarification of user legal questions, semantic understanding, case analysis, case attribute extraction and cause-of-fact prediction, and case retrieval and recommendation, improving the accuracy of legal case recommendations and user experience.

This invention discloses a memory-based behavioral adversarial interactive identity verification method and apparatus based on a metaverse. The method involves: acquiring first behavioral information from user feedback based on received interactive information during the interaction process; determining a difficulty weight index for each interactive message received by the user in historical interactions and its historical average response time; constructing a memory tree for each user based on the difficulty weight index; building an adversarial model; obtaining second behavioral information through the adversarial model's feedback of interactive information; performing conditional loop-based adversarial verification based on the first and second behavioral information to obtain a verification result; determining the order of interactive information by combining the memory tree and the previous verification result during the adversarial verification process; obtaining a comprehensive verification score based on all verification results and the difficulty weight index; and determining the user's identity verification result based on the comprehensive verification score, thus making the identity verification result more accurate and objective.

We provide the system. [Solution] An analysis means for analyzing natural language input and generating appropriate administrative procedure information, A providing means that provides the information generated by the analysis means to the user, A feedback collection method to gather user feedback and use it to improve the service, An interface means for a mobile device that displays information, A notification system that processes data in real time and immediately notifies users, A system that includes this.

The application discloses a sequence recommendation method based on semantic discretization and multi-interest modeling, and belongs to the field of natural language processing. The method encodes item metadata text into multiple discrete semantic tokens, extracts multiple interest query vectors from user historical comments, and calculates a recommendation score at the semantic level by matching the interest query and the semantic token of the candidate item. Meanwhile, the user historical interaction sequence is used to model collaborative preference, so as to fuse the semantic score and the collaborative score to generate a final recommendation result. Through user feedback aggregation and a multi-query mechanism, the modeling ability of user multi-interest preference is improved. Through fine-grained matching of the user interest query and the discrete semantic representation of the candidate item, the semantic interaction ability between the user and the candidate item is improved.

A blockchain-based knowledge-sharing intelligent customer service AI response accuracy optimization system, belonging to the fields of blockchain and artificial intelligence technology, is disclosed. The system includes a multi-source knowledge acquisition unit, a blockchain knowledge sharing unit, a knowledge quality assessment unit, an AI response optimization unit, and an interactive feedback and iteration unit. The multi-source knowledge acquisition unit collects customer service knowledge data from different departments, business lines, and application scenarios. The blockchain knowledge sharing unit receives pre-processed knowledge data and constructs a decentralized knowledge-sharing network based on distributed ledger technology. The knowledge quality assessment unit performs multi-dimensional quality assessments of the knowledge stored in the blockchain. The AI ​​response optimization unit trains and optimizes the intelligent customer service AI model based on high-quality knowledge samples. The interactive feedback and iteration unit collects user feedback from the intelligent customer service system. All units operate collaboratively to form a closed-loop mechanism.

This invention discloses an intelligent precision temperature and humidity control dryer system based on the Internet of Things (IoT), belonging to the field of data analysis technology. It includes: a dataset establishment module, which, for a target garment, executes a baseline drying process for that garment, collects multi-dimensional sensor data in real time, generates time-series data of the garment drying process, receives user feedback ratings on the drying effect, and maps these ratings to the time-series data to establish a target garment drying process-feedback effect dataset; a predictive control instruction module, which establishes a personalized drying strategy model for the garment to obtain predictive control instructions for the drying execution components; and an optimal drying state trajectory module, which, for the garment to be dried, identifies its static attributes, acquires the current drying process state data in real time, obtains predictive control instructions for the drying execution components, dynamically adjusts the drying execution parameters in real time, and generates the optimal drying state trajectory for the garment. This invention improves energy efficiency.

This invention discloses a method for constructing AI memories and generating virtual personalities based on full-volume dialogue storage, relating to the fields of artificial intelligence and human-computer interaction. During operation, the system establishes a structured storage mechanism for full-volume dialogues, constructing a traceable multi-dimensional AI memory bank. It extracts multi-dimensional features such as semantic preferences, emotional tendencies, and behavioral patterns through a deep semantic analysis model, aggregating them into high-dimensional memory representations with temporal evolution characteristics. A dynamic update mechanism based on memory strength, novelty, relevance, and decay functions is implemented to achieve adaptive optimization of the memory bank. A personality mapping model is constructed, mapping memory representations to a low-dimensional personality vector space composed of openness, agreeableness, conscientiousness, emotional stability, and autonomous evolutionary tendencies. Based on the personality vectors, the system regulates the AI's language style, emotional expression, and decision-making preferences, and combines these with user feedback signals to form a closed-loop evolutionary mechanism of "memory-personality-feedback."

The application discloses a code review method and related equipment, and relates to the technical field of software development. The method comprises the following steps: if a review triggering event for target code is detected, obtaining context information of the target code; based on the context information, calling a preset large language model to perform primary code review on the target code, and generating a primary review result; if the primary review result meets a preset condition, obtaining user feedback information for the primary review result; based on the context information, the primary review result and the user feedback information, calling the preset large language model to perform secondary code review on the target code, and generating a target review result. The application introduces a large language model primary review based on context and a secondary review mechanism combined with user feedback in the code review process, realizes the automation and iterative optimization of code review, reduces the dependence on manual work, improves the review efficiency, and improves the accuracy and reliability of the review result.

This invention relates to the field of intelligent clothing design, specifically to a method and system for optimizing down jackets based on generative adversarial networks (GANs) and user feedback. The method includes: a generation module that generates down jacket appearance renderings and structured pattern diagrams based on user body shape parameters and style semantic descriptions using a GAN; a feedback acquisition module that obtains user ratings, comfort evaluations, and local annotation information through virtual or physical try-ons, and collects feedback from professional pattern makers on pattern structure corrections; a feedback learning module that converts the multi-source feedback into sample weighted signals, structural regression signals, and preference prediction signals; a model optimization module that updates the weights of the generation model based on an optimization objective function including adversarial loss, structural constraint loss, and feedback joint loss; and an output module that generates optimized down jacket design results and editable pattern files. This invention can improve down jacket design efficiency and fit rate, and enhance the matching degree between design schemes and user preferences.

A computing platform may receive, from a user device, historical shopping information indicating previously purchased items and / or previous routes within shopping environments for a first user of the user device. The computing platform may input, into a shopping gamification model, the historical shopping information, which may output shopping recommendation information indicating one or more of: recommended items or recommended routes within a first shopping environment. The computing platform may send, to the user device, a shopping gamification interface that includes the shopping recommendation information and one or more commands directing the user device to display the shopping gamification interface. The computing platform may receive, from the user device, user feedback information indicating acceptance or rejection of the shopping recommendation information by the first user. The computing platform may update, based on the user feedback information, the shopping gamification model.

This invention relates to the field of science popularization creation technology, and discloses a method for creating original science popularization articles on wild animals that integrates science and fun. The method includes the following steps: S1, a stage of segmented insight and demand tagging management of age-appropriate users; S2, a stage of anchoring and age-appropriate conversion of science and literature-based content in wild animal science popularization; S3, a stage of AI-assisted full-link content creation and production driven by original IP; S4, a stage of three-level traceability-based content compliance and authority verification; S5, a stage of personalized and precise push notifications and universal distribution across all channels based on user tag matching; and S6, a stage of collecting user feedback from all dimensions and closed-loop content iteration and optimization. This invention achieves the scientific and systematic creation of wild animal science popularization content. Through a dual-track creation framework integrating science and literature, it breaks the drawbacks of traditional one-way knowledge transmission in science popularization content, allowing wild animal science popularization knowledge to be deeply integrated with humanistic themes and positive values, making it more suitable for the cognitive characteristics and acceptance habits of primary and secondary school students.

The application discloses a place real-time recommendation method based on combination weighting and user feedback, which comprises the following steps: extracting features of place-related data of three modes of eye movement data, operation records and text data in a period of time, and obtaining place attention degree ranking lists under each mode; initializing subjective weights; calculating objective weights; comprehensively calculating subjective weights and objective weights by using a difference coefficient method, and calculating an output place recommendation list; obtaining feedback information of a user on the place recommendation list, calculating normalized loss cumulative gains of the feedback information and the place attention degree ranking lists under different modes, and updating relative importance degrees and subjective weights of three modes of information. The application can recognize, comprehensively process and fuse data from a multi-element acquisition system, and obtain more comprehensive, accurate and reliable information than any single data source, so that the inherent shortcomings of a single data source are effectively made up, and high-coverage and high-precision information is generated.