136 results about "Computerized system" patented technology

A computerized system receives an original prompt that a querying user sends to a Large Language Model (LLM) that is operably connected to organizational data sources of an organization. Instead of executing the original prompt by the LLM, the system obtains user-related organizational context that pertains to characteristics of the querying user, obtains data-related organizational context that pertains to data from which the LLM is expected to obtain information for responding to the original query, and obtains pre-defined organizational policy rules, that indicate which type of users are authorized to access which type of organizational data. Based on the obtained data, the system modifies the original prompt into an adapted prompt. The system sends the adapted prompt, and not the original prompt, to the LLM for processing. The system obtains LLM-generated output from the LLM in response to the adapted prompt, and provides that LLM-generated output to the querying user.

Computerized systems and methods for automated Access Permissions Review in an organization. A method includes: (a) receiving a textual description of an Access Review Policy; (b) constructing a trigger for automatically initiating an Access Review process; (c) automatically collecting organizational data that pertains to: (i) roles and positions of users, (ii) user-specific characteristics, (iii) email messages sent and received by users, (iv) description of events in which users had accessed elements of the Organizational Resource. Upon a triggering of the trigger, the method provides to a fine-tuned Large Language Model (LLM), the Access Review Policy and augmentation data from the organizational data, and a prompt instructing the LLM to generate a proposal to revoke, modify, add or maintain the Access Permissions of a particular user, and further instructing the LLM to generate output that describes reasoning and supporting evidence for the proposal.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

Automated multi-phase investigation of security incident alerts using a Large Language Model (LLM) with converging dialogue. A computerized system receives a Security Alert Message pertaining to a possible security-related incident pertaining to an organization. The system automatically evaluates whether the Security Alert Message is either (I) a False Positive security alert message or (II) a True Positive security alert message, by performing an iterative multi-phase converging process in which the LLM evaluates at least: (i) the content of that Security Alert Message, and (ii) the meta-data of that Security Alert Message, and (iii) organizational context that is related to that Security Alert Message. An iterative process is performed by the LLM, which utilizes an Agent Module to fetch additional context information from organizational sources. The LLM re-updates the Risk Score and re-evaluates the Risk Score until convergence to a decision.

Provided herein are computerized system and method for full patient flow management through an emergency department process, integrating multi-agents LLM. The system and method utilize a plurality of LLM-based agents configured to perform one or more of: extract relevant medical information and generate a summarized medical history; interactively questioning the subject regarding its medical history and regarding the main complaint(s); provide recommendations regarding various medical examinations; process the summarized medical history and the results of the recommended medical examination(s) and; provide further summary, recommendations or instructions, regarding diagnosis and a decision regarding discharge or admission of the subject.

The invention is notably directed to a computer-implemented method of clustering anomalies detected in a computerized system. The proposed method makes use of an unsupervised cognitive model, executed based on input datasets to obtain clusters of anomalies. The method accesses input datasets, which correspond to detected anomalies of the computerized system. These anomalies span respective time windows. Each input dataset comprises a set of timeseries of key performance indicators. The key performance indicators of each input dataset extend over a respective time window. That is, each anomaly corresponds to a respective time window. This model includes a first stage, which includes an encoder designed to learn fixed-size representations of input datasets, and a second stage, which is a clustering stage. The model is executed based on the input datasets accessed, the first stage learning fixed-size representations of the input datasets and the second stage clustering the learned representations.

Systems and methods for improving interaction with computers in content delivery, search, and / or hosting systems supported or configured by devices, servers, and / or platforms, as well as interactions between computers, are disclosed. The disclosed systems and methods provide a novel framework that automatically generates and dynamically updates consistent instances of multi-display dashboards across disparate device and / or network locations. The disclosed framework generates and displays interactive dashboards comprising electronic tiles representing data from multiple processes and operations. The framework includes functionality for maintaining a consistent look and feel for the dashboard and the display characteristics of the tiles contained therein across platforms with different devices, operating environments, and / or display capabilities.

A computerized system and method may process and predict stress levels for audio data using a machine learning based framework. A computerized system including a processor and a memory may calculate a buffer length based on a plurality of audio attributes (e.g., of a given audio input or data item), extract an audio buffer from an audio data item based on the calculated length, and predict, using a machine learning model, a stress level for the audio buffer or data item. Some embodiments of the invention may include extracting a buffer of a length determined dynamically for different audio inputs, e.g., to ensure coherency between audio attributes or features extracted from different audio inputs having different audio characteristics. In some embodiments, audio features which may be considered by the model may include, e.g., a plurality of gradients between mel-frequency cepstrum coefficients computed for relevant audio buffers or inputs.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

Computerized systems and methods for automated AI-driven customer and vendor subdivision and personalized insight delivery are described. The method involves collecting real-time data including purchase behavior and market trends, analyzing it using an AI / ML algorithm for subdivision, and delivering personalized insights through a user-friendly single glass pane user interface (SPoG UI). Transaction details are recorded for continuous enhancement. Based on user feedback and evolved market dynamics, the subdivision effectiveness is monitored and refined. A plurality of data sources and analysis tools are integrated for comprehensive analysis. The user may customize the tessellation parameters, and the delivery options include push notifications and email alerts. The system facilitates continuous optimization and adaptation, thereby enhancing correlation and accuracy.

An electronic device is disclosed. The device receives retinal images and patient data corresponding to the retinal images. The device can train a first machine learning model (“model”) based on a first group of the retinal images and patient data corresponding to the first group and a second model based on a second group of the retinal images and patient data corresponding to the second group. The electronic device can generate a first prediction based on the first subset of a third group of the retinal images and a second prediction based on the second subset of the third group. After training the first model and the second model, the device can train a third model to predict a geographic atrophy progression in an eye of a patient based on the first and second predictions, the first and second subsets, and patient data corresponding to the first and second subset.

Computerized systems and methods are described for managing vendor-independent configuration-on-order (CTO) and offer-on-order (QTO) processes. A real-time data grid (RTDM) is provided for aggregating, normalizing, and normalizing real-time data from different sources. A single pane view user interface (SPoG UI) facilitates dynamic interaction and visibility to vendor performance. An advanced analysis and machine learning (AAML) module analyzes product compatibility, optimizes a pricing strategy and predicts a market trend. A vendor independent CTO / QTO integration module (VACIM) includes a process standardization engine and a vendor data conversion gateway to ensure consistency of different vendors. According to the method, automatic data processing and data conversion gateway integration are carried out to ensure data consistency, and decision making is carried out by adopting a rule engine driven by machine learning, so that a retailer process is simplified in an extensible and high-adaptability framework, the expandability is enhanced, and a pricing strategy is optimized.

Computerized systems and methods are provided for managing alerts and notifications within a technology distribution platform. A Single Pane of Glass User Interface (SPoG UI) presents notifications to users enabling interaction and customization. A Real-Time Data Mesh (RTDM) collects, filters, enriches, and standardizes event data from multiple sources into a uniform format. An Event Adapter formats data to be processed by a Notification Engine, configured to determine one or more notification triggers and generate alert content based on established rules and algorithms. A logging and user interaction module tracks user interactions with notifications. User feedback is processed by an Advanced Analytics and Machine Learning (AAML) Module configured to dynamically adapt notification logic. Notification content and delivery mechanisms are refined by a Distribution Module to ensure effective dissemination across various communication channels.

Disclosed are computerized systems and methods for a highly scalable, cloud-based AI / ML execution platform. The disclosed systems and methods provide a computerized framework that can generate and execute AI / ML models that provide agile, real-time predictions that can facilitate, cause and / or provide instructions for high-fidelity, real-time management of a multitude of cloud-based WiFi network locations, inclusive of the access points and / or user equipment operating therefrom / therein. The framework can cause a ML model to be trained that is then executed to generate a location-specific Al model that can then be executed to predict how a network can and / or should be configured based on current characteristics at the location, which can then be managed and put into place on at the location via the framework.

Disclosed are computerized systems and methods for a decision intelligence (Dl)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real -world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

Disclosed are systems and methods that provide a computerized network management framework that adaptively configures a network at a location to ensure connectivity and / or network services are maintained. The disclosed framework enables the implementation of multi-link operation (MLO) functionality within WiFi 7 enabled networks to implement non-disruptive off-channel scanning. The framework can utilize MLO backup / redundant links that can mitigate network disruptions in service / connectivity for client devices that are WiFi 7 capable during the performance of off-channel scanning of access point (AP) devices for a network by diverting network traffic to an activated MLO link while an available channel / radio (e.g., 5 GHZ) is used to perform such off-channel scanning.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides mechanisms for managing, optimizing and configuring a WiFi network at a location. The framework provides network management utilizing edge processing capabilities to bridge local WiFi and cloud systems. The framework implements comprehensive device typing through multi-layered analysis combining passive monitoring, deep packet inspection and hybrid deterministic-probabilistic classification methods. State synchronization between local and cloud networks can be achieved through hierarchical data modeling and differential synchronization algorithms. The framework can implement advanced features that include automated channel optimization, QoS management, and security monitoring. The framework incorporates self-healing capabilities using reinforcement learning techniques and maintains operational efficiency through intelligent resource management and workload distribution. The framework can operate autonomously while requiring minimal cloud connectivity, featuring extensible architecture through a plugin system that enables adaptation to evolving network requirements while maintaining stable operation of existing capabilities.

Computerized systems and methods for generating and optimizing vendor product roadmaps using predictive insights are described. A real-time data grid (RTDM) module is utilized to summarize and standardize data (including market trend, customer feedback and technical progress) from different sources. An analysis and machine learning (AAML) module analyzes the data to generate predictive insights, facilitating adjustments to existing product roadmaps. Dynamic adjustments are made using a roadmap optimization module, where communications are facilitated through a single glass pane (SPoG) user interface (UI). The scenario analysis, decision support system and continuous monitoring improve strategic decision making.

Computerized systems and methods are described for managing multi-tiered subscription-based models of technology products, including hardware, software, and cloud services. The system performs automated optimizing of subscription models and pricing using a Real-Time Data Mesh (RTDM) and Advanced Analytics and Machine Learning (AAML) Module. The system employs a Single Pane of Glass User Interface (SPoG UI) to enhance user interaction and subscription management. Automated processes facilitate dynamic adjustment of subscription terms, pricing, and configurations based on real-time data and analytics. API integrations for perform data exchange for comprehensive management of multi-tiered subscriptions within a unified ecosystem.

The present invention is a computerized system for grading an object, comprising: an enclosure; a platform designed to receive an object; a track system integrated into the enclosure and wherein the platform is attached to the track system; a scanning system integrated into the enclosure relative to the track system, wherein the scanning system is configured to capture at least one image of the object; and a computer system connected to the scanning system and the track system, comprising at least one processor.

A method, a computer program, or a computerized system for a first software program to remove private user information from a collection of data items communicated by a second software program to a third software program. The first software program may receive the data collection from the second software program, detect in the data collection a first indicator associated with the particular private user information, detect in the data collection the private user information within a proximity threshold associated with the first indicator, remove the private user information from the data collection to form cleaned data collection, and provide the cleaned data collection to the second software program.

Disclosed are computerized systems and methods for a decision intelligence (DI)-based framework that automatically and / or dynamically provides an interactive content viewing and content discovery experience to users. The framework includes functionality for real-time video content analysis and interactive entity identification that, inter alia, provides novel capabilities to viewing users related to the extraction, analysis and subsequent interaction with content depicted within video frames during playback of such content. The framework implements artificial intelligence / machine learning (AI / ML) approaches for video processing, user interaction and information delivery through a series of interconnected processes and subsystems. In some implementations, rendered content can be parsed and mined for real-world and / or digital content depicted therein that relate to real-world entities and / or digital resources, whereby interaction with such entities is provided in the form of a provided interface, electronic message and / or recommendations for further information discovery, or some combination thereof.

The disclosed systems and methods provide a novel action prediction framework that performs personalized action prediction. According to an embodiment, the disclosed framework is able to dynamically predict which action (if any) a user might perform in response to receiving a given message. In some embodiments, for a given message, the action prediction framework can determine the probability that a user (e.g., sender, recipient) associated with the message may perform an action or set of action actions (e.g., open, forward, delete, reply, archive) related to the message. In some embodiments, the framework may be used to suggest a predicted action to the user. In some embodiments, a computing device may use the predicted actions to automatically perform the action. According to an embodiment, the action prediction framework includes a multi-label or multi-class model using a neural network.

Disclosed herein is a computer implemented method to receive a user defined region of interest selection of a sample image that is used to extract a portion of sample for analysis by an analysis device.

A computerized system for personalized medical education, compliance verification, and clinical workflow automation uses sensor-based identification and multi-modal monitoring to verify user presence, engagement, and comprehension during delivery of educational modules and required disclosures. Artificial intelligence models interpret electronic medical record data to select and sequence patient-specific content, determine procedure-specific and jurisdiction-specific compliance requirements, and adapt presentation in real time through device-level control actions including pausing, prompting, repeating, and module recalibration when noncompliance is detected. The system further supports conducting interactive clinical encounters in lieu of or in conjunction with clinician visits, including structured intake, decision support, and generation of audit-ready, billable encounter documentation under clinician oversight and safety guardrails. The system generates time-stamped interaction logs, medical decision-making and time-on-task summaries, and electronic medical record-ready outputs with provenance and compliance verification, suitable for reimbursement and audit defensibility across multiple specialities.

Disclosed are systems and methods for a computerized framework that leverages artificial intelligence (AI) / machine learning (ML) mechanisms to assign selected individuals to military operations. The disclosed framework comparatively analyzes an ops sheet of a military operation and profile data related to a user(s), and automatically determines user(s) who are optimal for the operation. The determined user or users possess the physical and / or intellectual capabilities to accurately and efficiently, with respect to real-world and electronic resources, perform and complete the operation. The disclosed framework provides a computerized platform that selects users for highly specific tasks based on the users' analyzed skill sets, and based on computerized determinations of how such users are predicted to perform using those skill sets, securely and / or confidentially provides the users access to information related to the operation.

A computerized system of main controller that each node uses to process packets comprising: the main controller that each node uses to process packets, wherein the main controller comprises a plurality of receiving threads while receiving or transmitting a plurality of packets, and wherein when packets are received, a payload of the packet is placed per flow into a payload buffer, which notifies a suitable context queue by identifying a connection and a number of bytes received, and wherein the main controller further comprises: a policy controller configured to handle how and when a request for data and computation is performed, relayed, and cached, based on a localized cost estimation procedure, and a mode selector configured to calibrate a sending rate, a transmission window size, and a payload buffer size, based on a given choice complexity parameter that optimizes for a high throughput, a loss avoidance and a low latency.

A computerized system and method for manufacturing articulated surgical devices. Using surgical device pathway data (often acquired from medical scans), target and entry point position data, and design parameters stored in computer memory, customized devices are automatically designed and subsequently manufactured. The devices typically comprise multiple units, which may have different lengths and widths and are connected to one another by at least one movable joint. The devices are configured to accommodate multiple pull cables, often extending from the distal end to the proximal end. These cables allow the operator to precisely control the orientation of the distal end as the device traverses the patient's internal pathway, often using a proximal control device. The distal end of the device may have an effector unit configured to perform medical operations. Design and manufacturing are facilitated by the use of AI-powered computerized design techniques and computer-controlled (CNC, laser cutting, 3D printing) methods.

In an embodiment, an automated computerized system for providing a surety bond is disclosed. The system includes a plurality of computing devices communicatively coupled to one another over a network. The system further includes an underwriting server communicatively coupled to a client device over the network to receive first electronically transmitted information representing an application for a surety bond from the client device. The system further includes a database electronically storing contractual data from a contract between the principal and obligee, a contractual data aggregator configured to retrieve data from the database and to verify signatures on the contract, and a monitoring module configured to monitor whether one or more conditions for payment of the penal sum are met.