700 results about "User engagement" 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.

Systems and methods for contextual inference and response generation in natural language processing are disclosed. A data structure containing Knowledge Records, terms, and Relationship Types is accessed to facilitate interaction and inference. Knowledge states and uses relationship properties are employed for inference, classifying terms to generate responses. Logic and inference mechanism link Knowledge Records and Terms for response generation. Responses are tailored based on Criteria-Value Rating Pairs and user Traits, with the system adapting through learnings from user interactions and external data. The method includes storing interaction graphs and features modules for natural language understanding, interaction, response generation, and user engagement, executed on a processing unit. The system dynamically updates its Knowledgebase and refines response mechanisms, offering personalized and contextually relevant interactions in natural language. Additionally, the system provides structured contextual and criteria-value data to third-party systems such as Large Language Models (LLM) s via APIs, enhancing contextual understanding and interaction.

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 present disclosure relates generally to computer-implemented systems and techniques for leveraging a virtual decentralized database to enable user identity authentication and verification of user activity and associated behaviors on open internet and public web-based and native applications. Techniques are disclosed for user identification profile onboarding using biometrics, frictionless CAPTCHA (Completely Automatic Public Turing Tests To Tell Humans And Computers Apart) processes, passive data capture of physiological and behaviors using device sensors, and cryptographic methods of storing user engagement activity as immutable data files for secure distribution on a decentralized virtual private network. Such technologies operate to establish a high confidence level for user authenticity, communication origin, engagement outcome transparency and clarity of intent for users active on social, commercial, e-commerce and information sharing platforms. More specifically, an unobtrusive passive data capture method of authentication and verification on a secure VPN supports the organic, spontaneous and original experience audiences desire while simultaneously eliminating opportunities for unscrupulous actors to exploit open internet exchanges by inserting fraudulent user accounts, automated bots and other measures that mimic or impersonate live humans, which ultimately undermines user confidence in data safety and erodes mutual trust between people, companies and institutions.

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.

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.

A patient support apparatus for transporting a patient over a floor surface is described herein. The patient support apparatus includes a drive system with a drive member, a user interface arranged for selective user engagement by a user to operate the drive system, and a control system for operating the drive system. The control system includes a processor configured to determine that the patient transport apparatus is traveling on an inclined floor surface, monitor the user interface for changes in user engagement by a user, and operate the drive system to decelerate the drive member to one of a plurality of positions based on the changes in user engagement.

The present invention relates to systems and methods for generating personalized music marketing strategies and recommendations based on user interaction data, engagement metrics, and predictive analytics. The system collects data from various sources, applies temporal weighting with exponential decay, global historical engagement metrics, and generates recommendation and predictive scores. These scores are used to dynamically adjust marketing campaigns in real-time, enhancing user engagement and optimizing marketing strategies.

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.

Computerized systems and methods are described for executing personalized bundling processes. Methods include receiving user inputs specifying preferences for product bundles and utilizing a Real-Time Data Mesh (RTDM) to retrieve relevant real-time data. An Advanced Analytics and Machine Learning (AAML) Module analyzes these inputs alongside market data to generate personalized bundle recommendations. Recommendations are then displayed to the user via a Single Pane of Glass User Interface (SPoG UI) and, upon user confirmation, transferred as orders to a vendor system. Validation steps use algorithms within the AAML Module to ensure accuracy and relevance of the bundles. Real-time reports on user engagement and bundle success rates are generated. The system, accessible on multiple devices, integrates machine learning models that continually refine the bundling process based on evolving data patterns and user feedback, enhancing personalization and efficiency.

The invention relates to the technical field of three-dimensional modeling simulation, in particular to a three-dimensional scene generation method and system based on a large language model multi-agent cooperative system, which decomposes a three-dimensional scene generation task into a plurality of sub-tasks and distributes an agent for executing the corresponding sub-task for each sub-task. The plurality of sub-tasks comprise a text analysis sub-task, a planar graph design sub-task, a three-dimensional resource object selection sub-task and a scene combination sub-task, and the intelligent agents are all constructed based on a large language model; and the intelligent agent sequentially executes the corresponding sub-tasks according to the execution sequence of the sub-tasks in the three-dimensional scene generation task so as to generate a three-dimensional scene meeting the task requirements. According to the method, the 3D game scene can be effectively generated from the demand text, human and artificial intelligence interaction can be enhanced in the scene creation process, the real 3D scene creation process can be captured, roles of human team members in 3D scene creation can be simulated, the user participation degree can be improved, and the controllability in the 3D scene creation process can be improved.

In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user / content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.

An education platform has systems and associated methods for using generative artificial intelligence to generate and present individualized education curriculums based on tracked user engagement with the presented content. The education platform receives a request to provide individualized instruction to a user on a particular subject, and presents first content for a first part of the particular subject to the user. The education platform tracks user engagement with each of the first content using a set of sensors, and determines the user's learning preferences in response to tracking positive engagement that the user has with a subset of the first content of a first type, format, or presentation. The education platform customizes second content for a second part of the particular subject to have more content of the first type, format, or presentation than the first content based on the learning preferences.

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.

The invention provides a design method and system of a touch interaction interface. Wherein user behavior habits are collected, key elements are extracted, a user participation degree evaluation system is constructed, and a behavior habit analysis report is generated; carrying out situation modeling processing by using a situation awareness layout optimization algorithm, carrying out adaptability analysis by adopting an environment adaptation technology, and generating environment adaptation interface configuration; a long-term preference tracking algorithm is used to record and learn personalized preference settings to add personalized components, a progressive interface evolution method is used to carry out progressive evolution processing, an interactive feedback mechanism is introduced step by step, an interactive module is added, and an optimized layout scheme is generated; and constructing a learning library according to the user behavior habits, importing interaction behavior data to perform behavior pre-judgment, adjusting interface elements and a response mode, and generating a touch interaction interface optimization strategy. According to the technical scheme provided by the invention, the man-machine interaction fluency and the user experience of the touch interaction interface are improved.

The invention discloses an intelligent power distribution energy-saving power supply system and method, relates to the technical field of power systems, and solves the technical problems that the user experience is poor due to the lack of consideration of the user participation degree in the prior art, and the efficiency of intelligent power distribution energy-saving power supply is low due to the fact that the electric quantity generated by renewable energy cannot meet the user demand. Obtaining predicted electricity consumption of the plurality of areas through an electric power prediction model; calculating a regional power utilization priority coefficient according to the regional data; the power station state is adjusted according to the predicted electricity consumption and the regional electricity consumption priority coefficient, the user participation degree is considered, whether the generating capacity meets the electricity consumption requirement or not is predicted, regulation and control are conducted in advance, the situation of insufficient power supply is reduced, and the user experience and the power supply efficiency are improved.

A system and method for generating contextually relevant reply suggestions for media posts is disclosed. The system analyzes media content to identify visual objects, scenes, text, and metadata attributes using computer vision techniques. Identified objects and attributes are incorporated into structured prompt templates to construct detailed natural language descriptions of the media context. The prompts are provided to a text generation artificial intelligence (AI) that outputs a plurality of contextual reply suggestions based on the media analysis. Suggestions are displayed as selectable options adjacent to the media post. Users can cycle through suggestions and select a reply to send. Selections are logged to improve the AI model. Feedback on suggestion quality can also be collected. By integrating computer vision and AI generation driven by engineered prompts, the system produces highly relevant, personalized responses tailored to media content. The techniques enhance user engagement with media posts through intelligent AI reply suggestions.

One embodiment of a method for training a machine learning model includes generating a graph based on one or more semantic concepts associated with a plurality of entities and user engagement with the plurality of entities, and performing one or more operations to train an untrained machine learning model based on the graph to generate a trained machine learning model.

Provided herein is an engagement and care support platform (“ECSP”) computer system including at least one processor in communication with at least one memory device for facilitating senior user engagement. The processor is programmed to: (i) register a user through an application, (ii) register a caregiver associated with the user through the application, (iii) generate a senior profile based upon user personal and scheduling data, (iv) build a daily interactive user interface that reflects the senior profile, (v) display the daily interactive user interface at a first client device associated with the user, (vi) cause the first client device to initiate a daily interaction prompt to the user, (vii) determine whether any user interaction was received in response to the daily interaction prompt, and (viii) transmit a daily update message to a second client device associated with the caregiver, including an indication of whether any user interaction was received.

The invention belongs to the technical field of virtual reality, and discloses a virtual rich real environment training system based on benign stress, comprising: a database module for storing cognitive tasks and virtual scenes; the acceptance testing module is used for testing a preset cognitive task of the user and generating a scene acceptance index; and the initial training task generation module is used for generating a training task comprising two groups of scene parameters and one cognitive task according to the index. And the training execution module is used for constructing a dynamically changing virtual training scene according to the two groups of parameters and executing a cognitive task in the virtual training scene. And the training task updating module is used for generating a new training task based on the execution result and the acceptability index. And the training planning module formulates an overall training plan and controls an execution process. Personalized training content is provided by evaluating individual differences of the users, it is ensured that the training difficulty is matched with the capabilities of the users, and the training effect is optimized while the user participation degree and the possibility of insisting in training for a long time are improved.

The invention relates to the technical field of digital marketing, in particular to a digital advertisement putting method based on big data, and the method comprises the following steps: initializing an advertisement model, importing advertisement request time sequence data, carrying out time sequence analysis, adjusting an enqueue rule and a processing rate, calculating request distribution in a differentiated time period, and generating model parameter configuration. According to the invention, through a queuing theory and time sequence data analysis, advertisement time sequence and processing efficiency optimization, time sequence analysis and parameter adjustment, advertisement timeliness is enhanced, user activity peak synchronization is ensured, user participation degree and advertisement effect are improved, and recalculation of service time and estimation of waiting time support real-time monitoring of service efficiency. Accurate matching of advertisement resources and user requirements is achieved, real-time adjustment of strategies and simulation testing rapidly adapt to market changes, the resource utilization efficiency and ROI are improved, and precision and economic benefits of advertisement putting are remarkably improved through fine management and data driving optimization.

Systems and methods are presented herein for increasing user engagement with an interface by suggesting commands or queries for the user. A plurality of content items available for consumption are identified and metadata for each of the plurality of content items is retrieved. One or more candidate voice commands are generated based on a plurality of voice command templates based on a target verb and a subset of the metadata corresponding to the plurality of the content items available for consumption. A recall score is generated for each candidate voice command based at least in part on a detection of phonetic features that match between clauses of each candidate voice command. At least the candidate voice command with the highest recall score is selected and output using a suggestion system.

The invention discloses a concentration training method and system based on multi-modal fusion, and the method comprises the steps: collecting an electroencephalogram signal, an eye movement signal and a head movement signal through a wearable device in a concentration training process, and calculating an electroencephalogram frequency characteristic parameter, an eye movement parameter and a head movement modal parameter in a one-to-one correspondence manner; calculating a concentration score according to the electroencephalogram frequency characteristic parameters, the eye movement parameters and the head movement modal parameters so as to evaluate the concentration; and adjusting a concentration training strategy according to a concentration evaluation result. According to the method, the electroencephalogram, eye movement and head movement signals are collected at the same time through the wearable device, the corresponding characteristic parameters are calculated, the concentration score is comprehensively evaluated, and a comprehensive, accurate and stable evaluation result is provided. On the basis of real-time evaluation, task difficulty and types are dynamically adjusted, challenging and interestingness are ensured, the user participation degree and the training effect are improved, and concentration research and technical progress are promoted.

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.