34 results about "Interactive computing" patented technology

Systems, methods, apparatuses, and computer program products of a user interface configured for automated generation and optimization of room layout designs. One method may include determining a relative position token for each of a plurality of elements according to a function of at least one relative position token of at least one other element. Each relative position token comprises at least one discrete non-Cartesian value of each of the elements. The method further comprises determining a Cartesian position for each of a plurality of elements according to a function of at least one of the relative position tokens to generate a room layout; displaying, in an interactive computerized user interface, at least one generated room layout; and based upon input from the interactive computerized user interface, transmitting at least one of the generated plurality of room layouts and a request for a selection of the transmitted room layouts.

The invention discloses an AIAgent trigger execution mechanism of a linkage Notebook or a visual front end, and relates to the technical field of artificial intelligence and interactive computing. The mechanism comprises an event monitoring module, a trigger condition configuration module, a condition matching module, an AIAgent scheduling module and a result feedback module. The event monitoring module captures a front-end user interaction event in real time and outputs standardized data; the trigger condition configuration module stores a trigger rule preset by a user and supports dynamic adjustment; the condition matching module compares the standardized data with a trigger rule, and generates a trigger signal when a condition is met; the AIAgent scheduling module calls a corresponding Agent according to the trigger signal and transmits parameters; and the result feedback module converts the execution result into a front-end displayable form and updates the execution result to the front end. According to the method and the device, dynamic collaboration of the AIAgent and the front end is realized, the problems of rigid triggering mode and low interaction efficiency in the prior art are solved, the automation and convenience of intelligent task execution are improved, and the method and the device are suitable for scenes of data analysis, program development and the like.

In some aspects, a computing system can train a risk assessment model, using a training process, for determining a risk indicator. The training process can include: accessing a set of features; determining, using a selector network, a set of selected features and an indicator vector; and training the risk assessment model using the set of selected features and the indicator vector. The computing system can determine the risk indicator for a target entity using the trained risk assessment model. The computing system can transmit, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.

Systems, computer program products, and methods are described herein for digital resource allocation via an interactive computational framework. The present disclosure includes receiving credentials at a first endpoint device, authenticating the credentials, receiving a digital resource allocation request from the first endpoint device, the digital resource allocation request comprising parameters of a user associated with the authenticated credentials and item parameters of an item associated with the digital resource allocation request, retrieving user data associated with the user, determining, using a machine learning model, a digital resource allocation proposal based on the user data, generating, using a generative AI model, based on the digital resource allocation proposal, a smart contract comprising ownership, appending the smart contract to a distributed ledger, and transferring digital resources according to the smart contract.

A system can generate a risk assessment associated with a target entity. The system can access a request for a risk indicator associated with a target entity. The system can also access a model in a test environment and run an ATP tool on the model. The ATP tool can execute a set of deployment steps using the model and determine a completion status of each of the set of deployment steps. The system can deploy the model to a production environment when the set of deployment steps is successful. The system can determine the risk indicator using the model. The system can transmit, to a remote computing device, a responsive message comprising at least the risk indicator to control access of the target entity to one or more interactive computing environments.

An electronic device includes one or more processors, a user interface, and a memory. The device operates an electronic shopping interactive computing environment. Upon initiation of an interactive session, the processors determine a fraudulent return propensity score based on various input parameters, including device-level activities such as subscriber identification module card swaps, factory resets, and application reinstalls. When the propensity score exceeds a predefined threshold, the processors preclude one or more user interaction events, such as shopping cart interactions or product returns, from occurring in the shopping environment. The system can employ a machine learning algorithm to generate a normalized propensity score, enhancing fraud detection accuracy. This proactive approach mitigates the risk of fraudulent returns, protecting retailers from financial losses and maintaining the integrity of the e-commerce platform.

Interactive Computer Device for Real-Time Machine Learning Driven Data Interpretation

A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.

Embodiments are described herein for establishing, in an interactive computing environment operating via at least one server device, a first user with a first user access type for managing communications between a plurality of users. The first user access type may allow the first user to communicate directly via the interactive computing environment with a second user having a second user access type and a plurality of third users having a third user access type in. The second user access type may permit the second user to identify the third users based on identification parameters and a relationship between the third users and the first user in the interactive computing environment. The third user access type may prevent the third users from directly communicating with the second user without an approval of the first user to protect a privacy associated with the third user.

Systems and methods for predicting future risk for a target entity are provided. A risk assessment system receives historical risk assessment data of the target entity and identifies a target cluster that matches the historical risk assessment data. The target cluster is identified from a group of clusters determined using high dimensional clustering based on risk assessment data of a set of entities. The risk assessment system identifies a set of nearest neighbors of the target cluster and determines a prediction of future risk for the target entity based on the target cluster and the set of nearest neighbors. The risk assessment system transmits a responsive message, which can include the prediction of future risk, to a remote computing device for use in controlling access of the target entity to one or more interactive computing environments.

Systems, methods, apparatuses, and computer program products of a user interface configured for automated generation and optimization of floorplan designs. One method may include automatically generating a plurality of floorplans. At least one of the room's corner offsets is determined after at least one other room's position has been determined. The method further includes scoring a plurality of generated floorplans according to a scoring function; calculating, for at least one generated floorplan, whether it has a path with width at least as large as a number from a door of the respective generated floorplan to an exit; displaying, in an interactive computerized user interface, a subset of the generated plurality of floorplans that has been selected based on the scores of the floorplans; and based upon input from the interactive computerized user interface, transmitting at least one of the generated plurality of floorplans.

Abstract In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions. Abstract 20 26 20 53 15 06 J ul 2 02 6 A b s t r a c t 2 0 2 6 2 0 5 3 1 5 0 6 J u l 2 0 2 6

An electronic device and method preclude user interaction events in an electronic shopping interactive computing environment. The method involves comparing a shopping cart interaction event to at least one previous user interaction event associated with the user. When a metric associated with the previous user interaction event deviates from a corresponding metric associated with the shopping cart interaction event, the method presents a prompt on a user interface indicating that additional shopping cart interaction events will be unavailable for product return user interaction events. The electronic device includes a user interface, memory, and processors that compare the price of an item with metrics associated with past purchases and returns. If the price exceeds predefined thresholds, the device precludes product return user interaction events, thereby mitigating potential return fraud.

Systems, methods, apparatuses, and computer program products of a user interface configured for automated generation and optimization of room layout designs. One method may include determining a relative position token for each of a plurality of elements according to a function of at least one relative position token of at least one other element. Each relative position token comprises at least one discrete non-Cartesian value of each of the elements. The method further comprises determining a Cartesian position for each of a plurality of elements according to a function of at least one of the relative position tokens to generate a room layout; displaying, in an interactive computerized user interface, at least one generated room layout; and based upon input from the interactive computerized user interface, transmitting at least one of the generated plurality of room layouts and a request for a selection of the transmitted room layouts.

In some aspects, a computing system can train a machine-learning model to analyze a graph database for risk assessment. The computing system can use the machine-learning model to identify a risk indicator for a target component of one or more interactive computing environments. The graph database can include a set of nodes where each node represents a respective infrastructure service of one or more infrastructure services and a set of edges connecting individual nodes of the set of nodes. The computing system can generate the risk indicator for the target component based on an output of the machine-learning model The computing system additionally can output a graphical user interface including at least the risk indicator for use in controlling access to the one or more infrastructure services.

An electronic device and method for managing user interactions in an electronic shopping environment. The system determines a fraudulent return propensity score based on shopping cart interaction events across multiple sessions. When the score exceeds a predefined threshold, the system precludes additional user interactions, such as shopping cart completion or product returns. The method involves analyzing factors like delivery address, location, and time taken for interactions. The device includes processors and memory to execute these functions, enhancing fraud detection and maintaining the integrity of return policies. The system provides prompts to users when certain interactions are restricted.

A customized interactive computing environment may allow for a customized interactive user experience. The interactive computing environment may be defined by session parameters to customize a session of the interactive computing environment. The session parameters may include customized interactive user objectives that may be defined by a user. Each objective may be defined with a number of points awarded to a user for completing the objective. Real-time interactive user data may be received from user devices for completion of the custom objectives. Custom profile information may be defined by a user to customize user interfaces provided to users that log into a session of the interactive computing environment. Data analysis about the session and / or the defined objectives may be generated and displayed to users to indicate progress or other data about the session.

The invention relates to the technical field of man-machine interaction computing devices, and discloses an induction type interactive computing device and method, and the device comprises a housing which is provided with an inner cavity, and an upper plate is used for sensing and judging the interactive input condition; the induction type positioning plate is located below the upper plate of the shell, adopts a magnetic positioning technology or an electromagnetic resonance positioning technology and is used for inducing the position of interactive input, and the induction area of the induction part of the shell is matched with the induction area of the induction part of the induction type positioning plate; the processing control module is electrically connected with the induction part of the shell and the induction part of the induction type positioning plate and is used for receiving induction information and processing, storing and outputting the information; the imaging module is electrically connected with the processing control module and is used for imaging the information output by the processing control module to the upper surface of the shell; and the man-machine interaction module is used for man-machine interaction input. A high-precision positioning technology, real-time information processing and convenient man-machine interaction are adopted, and the defects in the aspects of large size, process complexity and the like are overcome.

Disclosed is a method of efficiently generating a computer-generated forces (CGF) entity that includes a simulated external vehicle within an interactive computer simulation that is displayed within an interactive simulation environment by an interactive computer simulation station. The method entails creating an array of state-space model structures having initially unknown parameters that, once known, will define a linearized approximation of a non-linear model of the simulated external vehicle, generating frequency response matrices by exciting each input-output relationship of the non-linear model of the simulated external vehicle at a plurality of trim conditions using very short Gaussian pulses, wherein the frequency response matrices define a plurality of linear equations, calculating the parameters by solving the linear equations of the frequency response matrices to thereby provide an array of state-space models that defines the linearized approximation of the non-linear model, and using the array of state-space models to generate the CGF entity.

A power graph convolutional network (PGCN) can be used for explainable machine learning. For example, a computing device can determine, using a PGCN, a risk indicator for a target entity from predictor variables associated with the target entity. The PGCN includes a convolutional layer configured to generate modified predictor variables by multiplying each of the predictor variables with a respective interaction factor generated based on an adjacency weight matrix and the predictor variables. The PGCN also includes a dense layer configured to generate the risk indicator. The training process involves adjusting a set of weights in the adjacency weight matrix and a set of weights in a weight vector of the dense layer based on a loss function of the PGCN. The computing device transmits a responsive message including the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.

Systems, methods, apparatuses, and computer program products of a user interface configured for automated generation and optimization of floorplan designs. One method may include automatically generating a plurality of floorplans. At least one of the room's corner offsets is determined after at least one other room's position has been determined. The method further includes scoring a plurality of generated floorplans according to a scoring function; calculating, for at least one generated floorplan, whether it has a path with width at least as large as a number from a door of the respective generated floorplan to an exit; displaying, in an interactive computerized user interface, a subset of the generated plurality of floorplans that has been selected based on the scores of the floorplans; and based upon input from the interactive computerized user interface, transmitting at least one of the generated plurality of floorplans.

A system can generate one or more compliance graphs and a compliance score that can be used at least in risk assessment operations. The system can access a request to visualize target entity data and calculate a compliance score for the target entity. The system can access attribute data. The system can generate one or more compliance graphs and a compliance score using the attribute data. The system can compare the compliance score to a compliance threshold and transmit a message including the results of the comparison to a remote system for controlling access of the target entity to an interactive computing environment.

A system can generate a risk assessment associated with a target entity. The system can receive a request for a risk indicator. At a first time and in a first environment, the system can train a first model using a first training data set. At the first time and in a second environment, the system can train a second model using a second training data set. At a second time, the system can select the second model based on a comparison of metrics associated with each model. The system can deploy the second model to the first environment and can determine the risk indicator using the second model. The system can transmit, to a remote computing device, a responsive message including the risk indicator to control access of the target entity to one or more interactive computing environments.

The invention discloses an intelligent environment perception and calibration optimization system and a use method, the system comprises a camera internal reference calibration module, an external reference calibration module, a data acquisition and frequency adjustment module, an annotation and training module, a model conversion and reasoning module and a fusion and release module, outputting a high-precision internal reference and a distortion coefficient; the external parameter calibration module interactively calculates a rotation translation matrix between the camera and the laser radar based on the two-dimensional image and the three-dimensional point cloud data; the data acquisition module adjusts topic frequency and supports cloud synchronous storage; the labeling training module is used for training a detection or segmentation model based on models such as YOLO and RTMPose, and realizing efficient reasoning through ONNX conversion and TensorRT acceleration; and finally, the fusion publishing module maps the two-dimensional result to the three-dimensional point cloud, and outputs a sensing result with coordinates. According to the system, through an optimization algorithm and interactive optimization, the calibration precision and adaptability in a dynamic complex environment are effectively improved.

The invention relates to the technical field of cloud computing, and discloses a distributed numerical simulation system implementation method, device and equipment and a storage medium, and the method comprises the following steps: creating a system container template through a cloud computing technology, creating a plurality of system containers according to the system container template, installing basic software components required by interactive computing, and establishing a plurality of distributed numerical simulation systems according to the basic software components; an interactive computing environment is obtained, a multi-physics field numerical simulator is installed in a system container of the interactive computing environment, an encryption protocol is configured through a reverse proxy, and a distributed numerical simulation system is achieved. According to the invention, the system container template is created by using the cloud computing technology, the plurality of system containers are generated, the basic software component is installed to construct the interactive computing environment, and on the basis, the multi-physics field numerical simulator is deployed, and the distributed numerical simulation system is realized through the reverse proxy and encryption protocol. Interaction and safety of the simulation system are enhanced, and numerical simulation processing efficiency is improved.

A computerized system for assessing performance includes an interactive computer simulation station for providing a simulation of a machine to train a student in how to operate the machine and an instructor operating station communicatively connected to the interactive computer simulation station to receive instructor assessment data from an instructor at the instructor operating station. The system includes an automatic rules-based assessment module for automatically assessing a performance of the student during the simulation based on one or more rules to thereby provide automatic assessment data. The system includes an artificial intelligence (AI) module for receiving both the instructor assessment data and the automatic assessment data and for providing a hybrid performance assessment of the student based on an AI assessment model trained using training sets of instructor assessment data and training sets of automatic assessment data.

In some aspects, a computing system can improve a machine learning model for risk assessment by removing or reducing bias in the machine learning model. The training process for the machine learning model can include training the machine learning model using training samples, obtaining data for a protected attribute, and calculating a bias metric using the data for the protected attribute and data obtained from the trained machine learning model. Based on the bias metric, bias associated with the machine learning model can be detected. The machine learning model can be modified based on the detected bias and re-trained. The re-trained machine learning model can be used to predict a risk indicator for a target entity. The predicted risk indicator can be transmitted to a remote computing device and be used for controlling access of the target entity to one or more interactive computing environments.

A computer-implemented method includes accessing data associated with log-in attempts of an interactive computing environment from attempt logs. The method further includes detecting a success percentage of log-in attempts by an entity. Additionally, the method includes identifying the entity as a credential-stuffing attacker based at least in part on the success percentage of log-in attempts by the entity. Moreover, the method includes restricting access to the interactive computing environment by the entity.

INTERACTIVE COMPUTER TERMINAL KIOSK FOR BUSINESS MANAGEMENT