424 results about "User engagement" patented technology

The invention discloses a dynamic visual content collaborative generation method based on a cognitive logic chain, and belongs to the technical field of visual content generation, and the method comprises the following steps: S1, user intention analysis and data input; s2, dynamically constructing a cognitive logic chain; s3, intelligent scheduling of the multi-modal generation module; s4, cross-modal content collaborative generation is carried out; s5, collaborative editing and real-time feedback are carried out; s6, iterative optimization of logic chain driving; s7, multi-dimensional quality evaluation: constructing an evaluation matrix containing semantic consistency, visual attraction and user participation degree, predicting a content propagation effect in combination with a deep learning model, and generating a quantitative improvement suggestion report; and S8, updating the self-adaptive knowledge reversely marking the cognitive logic chain according to the finally adopted content version, extracting a new association rule, and injecting the new association rule into the rule base. Through deep semantic analysis and dynamic logic chain construction, the system accurately captures a core creation target of a user and converts the core creation target into an executable visual strategy.

The invention discloses a hierarchical collaborative management method and system for a virtual power plant based on multi-modal deep learning, and the method comprises the steps: constructing a four-dimensional data collection system, and achieving privacy enhancement preprocessing through federated learning and a differential privacy technology; a Bi-LSTM and a heterogeneous graph neural network are adopted to construct a three-mode deep fusion model, the weight is dynamically adjusted in combination with an environment-user dual-drive attention mechanism, and the load prediction precision and the space resource utilization rate are improved; a multi-target scheduling strategy is generated based on a five-dimensional target function and an improved DDPG algorithm, and physical feasibility is ensured through digital twinborn pre-verification; efficient execution and excitation transparency are realized through edge layer FPGA + NPU hardware acceleration and block chain evidence storage; and constructing a user participation ecology by using a natural language interaction strategy engine and a stepped incentive mechanism. The power grid economy, the equipment reliability and the user participation degree are remarkably improved, and intelligent upgrading of the virtual power plant is promoted.

A system and method for optimizing automated textual prompts in artificial intelligence (AI) conversational systems is disclosed. The system comprises a network interface, processors, and memory-storing instructions for performing operations to optimize prompts. These operations include receiving and preprocessing input data, tokenizing the data, verifying data authenticity, performing temporal analysis, calculating prompt complexity scores, and selectively expanding or refining prompts based on complexity thresholds. The system further incorporates context-aware optimization, multi-faceted prompt refinement, variation generation, and evaluation using machine learning models. Additional features include a technological hub with advanced processing capabilities, sensor-augmented input apparatus, device-specific prompt optimization, AI model selection, multimodal context integration, and an AI-driven creativity booster. The system provides interactive prompt visualization, certification, and uniqueness verification modules. This comprehensive approach ensures the generation of optimized, contextually relevant, and creative prompts for various AI applications while maintaining data integrity and user engagement.

The invention discloses a non-perpetual culture immersive interactive experience device and system based on a virtual digital human, and relates to the technical field of intangible cultural heritage intelligent interaction, and the device comprises a multi-mode sensing module which collects data such as user actions and voices through a depth camera and the like; the non-perpetual knowledge base module stores a non-perpetual knowledge graph and a case library; the digital human modeling engine fuses the inheritor characteristics and the user data to generate a virtual digital human; the immersion interaction engine constructs a cross-platform rendering environment to realize five-sense fusion experience; and the adaptive learning module analyzes the interaction sequence to optimize the digital human behavior, and all the modules cooperate to realize the intelligent interaction experience of non-genetic culture. Through the multi-modal perception module, the digital human modeling module and the like, the immersive interactive experience of the non-perpetual culture is realized, the user behavior can be accurately captured, the characteristic virtual digital human is generated, the five-sense fusion experience is provided, the content can be optimized based on the user interaction, the inheritance and propagation of the non-perpetual culture are promoted, and the participation degree and the sense of identity of the user are improved.

A patient transport apparatus includes a base, a patient support deck, a plurality of wheels, a plurality of brakes, and an electro-mechanical braking system. The electro-mechanical braking system includes a linkage and an electrical braking assembly. The linkage is operatively coupled to the brakes to place the brakes in a braked state, a released state, or other state. The electrical braking assembly includes an actuator assembly that moves the linkage via a driving member. A user interface includes an input control for user engagement. A brake control circuit includes a hold circuit to generate an enable signal with a predetermined voltage in response to the user engagement with the input control, maintain the enable signal for a predetermined period following user disengagement with the input control, and operate the actuator assembly with the enable signal to move the driving member within the predetermined period.

This invention relates to a system and method that enhances user interaction and marketing effectiveness through advanced data integration and personalized algorithms. The system and method collects detailed user data through body scanning and questionnaires, creating comprehensive profiles using machine learning. These profiles facilitate precise matching of influencers with businesses based on physical attributes, audience demographics, and content preferences. The system generates AI-driven marketing personas tailored to specific demographics and enables live shopping integration with social platforms. A novel double-blind review mechanism ensures transparent feedback between businesses and influencers. The platform supports group profiles that aggregate multiple users' data, enabling family-based shopping with privacy controls and preference weighting. The system continuously refines its algorithms using campaign performance data and engagement metrics, optimizing future matches and marketing strategies. This innovative approach significantly improves marketing ROI, user engagement, and personalization for digital social interaction and targeted marketing.

An apparatus and method for data generation for user engagement are disclosed. The apparatus includes an application programming interface, an interactive user interface, at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive user data from the interactive user interface, identify first user engagement data as a function of the user data, label the first user engagement data to at least an engagement label, generate action data as a function of the first user engagement data containing the at least an engagement label, trigger the action data using the application programming interface, receive second user engagement data as a function of the triggered action data from the interactive user interface and update the action data as a function of the second user engagement data.

Systems and methods for facilitating user engagement with units of work assigned within a collaboration environment are presented herein. Implementations may: manage environment state information maintaining a collaboration environment, the environment state information the work unit records including values of an approval state parameter; effectuate presentation of a user interface of the collaboration environment, the user interface displaying individual work unit pages for individual units of work that include a set of user interface elements available for selection by the users, individual user interface elements in the set corresponding to different ones of the values of the approval state parameter; obtain user input information conveying user input characterizing the selection of the individual user interface elements; update the work unit records based on the user input information; and / or other perform other operations.

An online system receives a request to access an interactive geographical map of sources from a client device associated with a user. The system retrieves data describing a geographical location associated with the user. The system identifies one or more sources within a threshold distance of the location and retrieves data including information describing items available at each source. For each source, the system accesses and applies a machine-learning model to predict a user engagement score indicating a likelihood of one or more interactions by the user with a set of items available at the source if the source is included in the map. Based on the score for each source, the system selects a set of sources and generates the map, in which the map indicates the geographical location of each selected source. The map is then sent to the client device, causing the device to display the map.

Determining content items promote long-term user engagement with a digital content platform is not trivial. Online learning systems or simulations that aim to learn and predict such content items are expensive to implement and may not always converge. One approach can involve modeling long-term engagement by examining user trajectories extracted from offline user session data. User trajectories may include user interactions with the platform over a long period of time. Rewards for a particular content item can be calculated using the user trajectories, where a reward is based on a window of user interactions that follows a user interaction with the particular content item. An estimate for the engagement score for the particular content item can be determined from the rewards. The engagement scores of various content items can be used as training data to train a model that can make inferences on long-term engagement potential of a content item.

The invention discloses a user participation degree prediction method based on distillation multi-modal retrieval enhancement, and belongs to the technical field of social media data mining and user behavior analysis. According to the method, the correlation of the UGC is evaluated by introducing the self-enhanced distillation module, and the top-K related UGC is selected in combination with the selected retriever, so that the interference of irrelevant information on the related UGC is effectively avoided, and the noise caused by irrelevant artifacts can be filtered out when the related UGC is retrieved, thereby keeping the original feature representation of the related UGC. The heterogeneous graph construction module enhances the interactive representation capability between UGCs through multi-relation modeling, and can more accurately optimize a prediction result in user participation prediction. According to the method, information retrieval between the related UGC and the unrelated UGC can be better balanced, excessive diffusion of unrelated information is avoided, and the performance of the model in user participation degree prediction is improved. Particularly, when complex multi-modal data containing a large number of irrelevant UGCs is processed, the mechanism can effectively enhance the distinction degree of the relevant UGCs, and the prediction accuracy is improved.

Methods, systems, and computer-readable media for user engagement modeling for engagement optimization are disclosed. Based (at least in part) on one or more user engagement models, a user engagement modeling system determines an uplift score for a user of an Internet-accessible service. The uplift score comprises an estimated effect on one or more user engagement metrics of an incentive to interact with the service. The uplift score is determined based (at least in part) on values of the user engagement metric(s) for the user in a treatment group and values of the metric(s) for the user in a control group, in view of propensity score to be in either group. The treatment group is offered the incentive, and the control group is not offered the incentive. The system determines that the user is or is not offered the incentive based at least in part on the uplift score.

A system and method for facilitating training of large language model based recommender systems are provided. The system may utilize one or more LLMs to create probability distributions for binary classification tasks associated with specific user-item pairs. The probabilities may be utilized to rank one or more tasks directly. The training of the one or more LLMs may involve the use of Knowledge Distillation methods and may be based on incorporating a dual-label system such as, for example, hard labels and soft labels. The one or more LLMs training data may consist of user-item pairs and their corresponding features. The labels used in the training process may include binary classification labels and their respective probabilities. The system may further implement the trained one or more LLMs to determine rankings or recommendations associated with user engagement of one or more content items.

The invention discloses an AI-based child story picture book generation method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating a narrative framework through employing a BPOT bidirectional pre-training model, carrying out the filling optimization of detailed text paragraphs through combining reinforcement learning and semantic mutual information, extracting dynamic features based on DCNN and SENet, and generating a child story picture book. And an interactive dynamic effect sequence is generated in combination with the preprocessed corpus, and finally, typesetting is optimized through a Wasserstein distance and a variational auto-encoder to generate a child story picture book. By using a BPOT bidirectional pre-training model, reinforcement learning and semantic mutual information to optimize a narrative framework, semantic age suitability and educational target integrating degree are remarkably improved, a DCNN is combined with SENet to generate an age-suitable interaction dynamic effect, interaction trigger point design is fused, immersion and user participation degree of the picture book are improved, and it is ensured that the picture book integrally presents high-quality narrative consistency.

An emerging service in the private and non-profit sectors is to electronically facilitate engagement of employees and volunteers for various causes. The instant systems and methods provide a software as a service platform that provides user engagement via machine learning and artificial intelligence.

The invention belongs to the technical field of intelligent marketing, and particularly relates to an intelligent customer obtaining system and method based on multi-source data fusion. The intelligent customer obtaining method based on multi-source data fusion comprises the following steps that S10, geographical location information, e-commerce data information and social data information of a user terminal are collected under the condition that authorization of the user terminal is obtained, and the e-commerce data information comprises transaction information, browsing information and search information; the social data information comprises collection clues, collection shops and browsing information; and S20, analyzing the interest preference of the user according to the social data information. According to the scheme, through comprehensive data collection, dynamic weight distribution and a real-time feedback mechanism, the pushing accuracy, the user participation degree and the conversion rate are remarkably improved, distribution of customer resource clues is optimized, the user experience is enhanced, the problem that in the prior art, the conversion rate is low when marketing users are converted into consumption users is effectively solved, and the user experience is improved. And the customer obtaining efficiency and the user satisfaction are remarkably improved.

Apparatus for predicting an outcome of user engagement and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive user data that include a plurality of data attributes, populate a data retrieval module using the user data, the data retrieval module including assessment metrics, wherein populating the data retrieval module includes assigning a weight to each data attribute of the data attributes as a function of at least an assessment metric of the assessment metrics, rank the data attributes as a function of the weight, and generate an outcome prediction, as a function of the ranked data attributes.

Computerized systems and methods are described for enhancing search capabilities and user engagement within technology distribution platforms. The system integrates components including Real-Time Data Mesh, Single Pane of Glass User Interface, Advanced Analytics and Machine-Learning Module, and Relevancy Search Engine Module, Embodiments aggregate real-time data from multiple sources, standardize it for efficient processing, and utilize advanced analytics and machine learning models to optimize search results. A Relevancy Search Engine prioritizes search results based on relevancy scores using machine learning models and natural language processing. A Dynamic SKU Search Engine enables dynamic and responsive search functionalities personalized to user needs. A Personalization Engine provides personalized recommendations based on user profiles and interactions. A Real-Time Relevancy Adjustment Module ensures continuous optimization of search relevancy. Methods include initiating enhancement processes, analyzing search queries, processing data, and adjusting relevancy in real-time. Impact is measured using performance indicators.

LLMs use probabilistic methods to create coherent responses, sometimes going beyond the training data. This can result in “LLM hallucination.” Asset metadata is used in constructing prompts for the LLM, enhancing the LLM's understanding of available assets. Including a limited set of candidate assets in an LLM prompt template as part of a chain-of-thought prompt can solve the hallucination issue. Multiple rounds of interaction with the LLM may be used, allowing for more dynamic and responsive user engagement. The LLM-based recommender system for data catalogs may comprise asset metadata, user data, and a recommender model. The output of the LLM-based recommender system is a recommended asset list. The prompts may explicitly instruct the LLM to limit elements of the list to the candidate set of assets. The LLM's advanced context understanding and reasoning abilities enable it to deliver accurate and interpretable personalized recommendations.

The present disclosure provides a search engine optimization (SEO) system comprising a remote server. The remote server comprises a memory with a set of executable routines and a search engine database with multiple fields of applications, each associated with multiple URLs indexed with a user engagement matrix, written data, and a search engine ranking. A processor acquires a web link from a computing device, extracts textual content, analyzes relevancy, and retrieves relevant URLs. The processor evaluates a thematic score, a readability score, and an emotional tone data using NLP techniques, analyzes written data of each URL to determine a topic weight, a legibility weight, and a sentiment tone data, develops a machine learning model, applies the model to recommend content alterations, and renders the alterations at the computing device.

A method including receiving at least one input, detecting at least one parameter associated with a context, generating, by a machine learning model, a first set of data classes enriched with the context, determining if each data class is associated with a data class repository to define a subset of data classes not associated with a data class repository, and generating, by a machine learning model, at least one data class repository for each data class. The method includes generating a display signal to display information associated with at least one data class repository, the display signal associated with a graphical user interface, altering, using a machine learning model, the display signal by altering at least one portion of the graphical user interface associated with the context, and sending the display signal to display the at least one portion of the graphical user interface on the user device.

Correlations between a set of linguistic features identified in an unstructured text recited in a source language and a set of user engagement analytic parameters may be measured by a machine learning model selected based on a set of performance metrics from a set of machine learning models trained by a set of supervised machine learning algorithms on (i) a set of unstructured texts recited in the source language and containing the set of linguistic features and (ii) the set of user engagement analytic parameters measured for the set of unstructured texts. The machine learning model grades the unstructured text recited in the source language to determine whether the unstructured text recited in the source language should be (1) edited in the source language and then translated into the target language or (2) translated from the source language to the target language as is.

Disclosed herein are systems and methods utilizing an application that utilizes the hardware and software capabilities of a mobile device to extend the time of user engagement. The hardware and software capabilities of the mobile device that are utilized can include gaze tracking, requiring user engagement for award redemption, requiring two-device confirmation in a trading system, blockchain tracking, geo-location reminders, Global Positioning System (GPS) reminders, and combinations thereof.

A system can operate a speech-controlled device to perform user engagement detection (UED) processing to detect when speech represented in audio data is directed to the device. For example, the device may extract audio features from the audio data and process these audio features using a classifier to estimate an orientation of the user's head, which may be used as a proxy for user engagement. Thus, if the head orientation is within an engagement zone (which varies based on distance to the user), the device may determine that the user is engaged with the device and perform language processing on input speech. In contrast, if the head orientation is outside of the engagement zone, the device may determine that the user is not engaged and ignore the input speech. To enable additional functionality, the classifier may optionally output a coarse estimate of the head orientation along with the UED determination.

The invention discloses an English teaching method based on somatosensory interaction, and relates to the field of interactive teaching, and the method comprises the steps: collecting limb behaviors and voice data of a user, and generating a teaching task instruction based on a teaching theme selected by the user; matching actions and voices of a user with a target grammar structure and a vocabulary set by constructing a mapping library of limb behaviors and English communication intentions; analyzing the limb movement data through a space-time diagram convolutional network, recognizing movement features, and generating a voice intention identifier through voice recognition and semantic analysis; comparing the consistency of the limb behavior and the voice intention, and if a conflict exists, generating a correction instruction; and dynamically adjusting the task difficulty according to the historical performance of the user, and increasing visual and voice interference until all teaching objectives are completed. The method has the advantages that by collecting the limb movement and voice data of the user in real time and combining the dynamic task and the intelligent error correction system, the language learning experience with high interactivity is achieved, and the learning effect and the user participation degree are effectively improved.

A gaze detection environment includes an online learning platform and a gaze detection system. A gaze detection module is integrated within the online learning platform and is initialized to monitor user engagement. The gaze detection module is calibrated to track the eye movement of the user in real-time and capture the eye gaze data which includes one or more eye coordinates. The gaze detection system processes the captured eye gaze data for detecting off-screen events by streaming the eye gaze data in real-time. The pre-defined screen edges are defined which includes an outer area of the screen. Further, the extracted gaze details are compared with the pre-defined screen edges to identify the off-gaze event and generate an alert if the user's gaze location is identified within the pre-defined screen edges for a pre-defined time. A notification module displays the generated alert to notify the user about the detected off-screen event.

A system for reward generation on a social media platform is disclosed. The system includes a processing subsystem having a. registration module linking multiple social media accounts for a user. An engagement calculation module identifies key metrics like likes, comments, and shares, calculating individual metrics for each engagement type. The engagement calculation module computes a total engagement value for specific posts or overall accounts. The training module uses historical data, to train machine learning models, fine-tuning hyperparameters for iterative improvement. The reward generation module establishes a conversion rate between total engagement and reward points, dynamically assigning loyalty through trained models. A reward redemption module facilitates the accumulation of points in a virtual wallet, converting them into various assets like money or cryptocurrency using blockchain integration. This comprehensive approach ensures a fair and personalized reward system based on user engagement levels.

In an interactive media system, for example, in a video game or extended reality (XR) environment, interactive media event information may be received. An interactive media satisfaction value may be determined based on one or more interactive media events received. For example, the event may be scoring a point by the user. When the interactive media satisfaction value is determined to be high, supplemental content with more upbeat themes may be selected for display in the interactive media user interface, while supplemental content with more negative themes, or more comfort-oriented or escapist themes may be selected when the satisfaction value is low. A user engagement metric may also be determined and used for timing, locating, and selecting a mode for presenting the supplemental content.

The invention discloses an incentive differentiation configuration and promotion support system based on user hierarchies, and relates to the technical field of big data and intelligent promotion, and the system comprises a multi-dimensional user data collection module which collects various types of user data and carries out desensitization storage; the user level dynamic evaluation module is used for calculating a comprehensive score through multi-dimensional index weighting and dividing five layers of users; the incentive differentiation configuration module formulates gradient incentive strategies according to different hierarchies; the promotion resource intelligent distribution module distributes promotion resources according to levels, targets and scenes; and the promotion effect tracking feedback module is used for monitoring data, generating a report and dynamically adjusting a strategy to realize optimization. According to the method, accurate division of user levels is realized, the user participation degree is improved through differential excitation, the utilization efficiency is improved through intelligent distribution and promotion of resources, continuous optimization is performed through an effect feedback closed loop, different business targets are adapted, and the marketing promotion accuracy and efficiency are improved.

Systems and methods for assessing user engagement with digital materials combining a variety of data sources, including video images, user device activity, and derived data, to estimate user attention recovery time. User engagement can be proactively monitored, analyzed and optimized, thereby improving the learning experience for student users.