209 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.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

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.

A method disclosed herein is employed in a computerized system. Initially, categories are defined in the memory, encompassing both identifier categories for direct identifiers of personally identifiable information and enumerated categories for objects related to such information but distinct from direct identifiers. Medical facts for numerous patients are acquired and processed into fact vectors based on these categories using processors within the system. A de-identification process is conducted to remove direct identifiers from the fact vectors, and a fictionalization process is carried out to alter enumerated objects. The resultant de-identified and fictionalized medical facts are stored in memory as a training dataset. Utilizing this dataset, the system trains an untrained artificial intelligence (AI) model, transforming it into a trained AI model through processor-based training procedures.

A computerized system for circuit design review and analysis can include an automated machine analysis engine running in a processor and operable to execute a BOM evaluation of a PCB design, a schematic analysis engine running in the processor and operable to analyze a schematic diagram associated with the PCB design, a PCB layout analysis engine running in the processor and operable to analyze a PCB layout associated with the PCB design, a PCB manufacturability analysis engine running in the processor and operable to analyze the PCB layout of the PCB design in combination with the BOM to validate manufacturability of the PCB design, and a design modification engine running in the processor and configured to generate a screen display object on a human-machine interface illustrating the data and / or a change to the BOM, the schematic diagram, and / or the PCB layout.

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.

Computerized systems and methods useful for resource-constraint scheduling using visual representations on a graphical user interface to build one or more schedules across a business network, where the schedules are dynamically reconfigured based off real-time changes to resource availability, constraints and requirements that must be satisfied, where interactions by the user with the schedule at the user interface cause the dynamic, real-time recalculation of possible appointments meeting the constraints, after which a schedule is reconfigured and displayed at the user interface.

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.

A dynamically operated concave threshing bar system, method, and apparatus wherein one or more threshing bars within a concave can dynamically move to various positions in real-time based on one or more conditions such as the type crop being harvested and on a determination by a combine harvester's computerized system, artificial intelligence (AI) system, or upon the operators' input, among others. The concave can include a concave frame having a pair of arcuate side members, a threshing bar, and an actuator coupled to the threshing bar, wherein the actuator can be configured to move the threshing bar along the arcuate side members of the concave frame.

In one aspect, a computerized system of an Energy Yield Management Software (EYMS) framework includes an Industrial Grade Solar Microgrids (IGSM) deployment comprising a control unit configured to communicate with a power generating source, collect data from the power generating source, and issue instructions to the power generating resource. The control unit is further configured to communicate with a sensor, an automation module, a local load, an energy storage systems, or a generation resource within a customer IGSM deployment. The EYMS is configured to communicate through a communication network to the IGSM deployment. A Digital Twin configured to actively use real time and historical data to learn how each component in the IGSM performs under a plurality of operational conditions and characteristics, wherein a predictive model is employed by the Digital Twin for a prediction operation, an optimization operation and a prescriptive maintenance operation of the IGSM.

The disclosure relates to systems, methods and computer readable media for enabling distributed secure remote access from a device via a wide area network (WAN), to a designated physical site covered by local area network (LAN). Specifically, the disclosure relates to a computerized systems, methods and computer-readable media using hardware-based, virtual private network pairs having preshared encryption / decryption keys, operable to transmit data over WAN from a physical computing device to an exclusively designated site in a physical area covered by a LAN.

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.

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 AI 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.

In one aspect, a computerized system comprising: a user-interface configured to: display a skin color selection, wherein the skin color selection comprises a plurality of skin pigments, receive a user skin color selection, display a skin cancer familial history input field, receive a skin cancer familial history input value, display a sun protection factor (SPF) input field, receive a SPF sunscreen input value selection for sunscreen being used; a sun exposure monitoring application operating in the computer system configured to: translate the skin color selection to a maxim sun exposure value, and computer a maximum sun exposure time using the user skin color selection, the skin cancer familial history input value, and the SPF selection for a sunscreen being used, monitor a sun exposure of the computer system, implement an alert signal when the maximum sun exposure time is reached.

A method for authorizing an entity (9) in a computerized system to access an object (12) includes the following steps: providing (S1) an access control list (ACL) that specifies access permissions for each object (12) to the object (12) in the computerized system; assigning (S2) capability requirement information to at least one of the objects in the access control list (ACL); assigning the capability information to at least one entity (9) in the computerized system; requesting (S11) access to the object (12) by the entity (9); checking whether the requesting entity (9) has access permissions according to the ACL; and authorizing (S5) access to the requested object (12) by the requesting entity (9) only if the capability information assigned to the requesting entity (9) matches the capability requirement information assigned to the requested object (12). The combination of ACL-based access to files and capabilities enhances the security of the system.

A control system is presented for use in depth-resolved inspection of multi-layer structures. The control system comprises a computerized system capable of processing input measured data indicative of a light spectrum obtained from the structure by camera pixels of a broadband interferometer during variation of an optical path difference (OPD). A data processor of the computerized system comprises: first processing utility for processing the input measured data and extracting complex reflectivity data of the structure; second processing utility for processing the complex reflectivity data and determining a time-domain impulse response of the structure, for each of N lateral positions corresponding to N of said camera pixels, and obtaining a 3D map of the time-domain impulse responses of the structure for at least a wavelength range of interest from the range used in the broadband interferometer; and layers' alignment data extractor for directly extracting depth-resolved information from said 3D map data.

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, methods, media, and computer program products are provided for improving the computational efficiency of evaluating and selecting a tendency scoring model. In an example method, a computer system accesses first data representing a plurality of characteristics of a test subject and second data representing a plurality of candidate tendency score models that may be used to estimate effectiveness of a treatment. The system selects a set of tendency score models by sequentially evaluating at least some of the candidate tendency score models until one or more stop criteria are met. The effectiveness of the treatment may be estimated by utilizing the selected set of tendency score models. Further, the system stores a data structure representing the set of tendency score models, and outputs the data structure.

A computerized system operable to provide multiple video streams of an event. In an ideal embodiment, the system provides live and dynamic streaming of an event such as a sporting event, concert, march, rally, and the like, to allow viewers to watch video of the event from nearly any angle and vantage point.

Computerized systems, and method and computer readable media. The method may include receiving, by a neural network, input face visual information; wherein the neural network comprises multiple convolutional layers, an embedding layer and one or more conversion layers; generating, by the embedding layer, a face recognition (FR) feature vector that comprises multiple FR feature elements; applying a soft function on the multiple FR feature elements, by the one or more conversion layers, to provide a converted FR feature vector that comprises multiple converted FR feature elements that are non-binary; and generating a binary representation of the face recognition features based on the converted FR feature vector.

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.

Disclosed are systems and methods that provide a computerized electronic and digital asset / resource management framework that provides novel capabilities for users (or fans) to interact with participants of a live-event (e.g., athletes currently playing in a sporting event). According to some embodiments, the disclosed framework provides a blockchain-based platform designed to revolutionize the engagement of sports fans and athletes by providing novel capabilities for fans to interact and / or reward athletes for exceptional plays and highlights in real-time. Leveraging blockchain technology and smart contracts, the disclosed framework provides an innovative application / platform for which NIL rewards in the world of sports can be leveraged, thereby increasing the fan experience while ensuring equity among the participants of the real-time gameplay.

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.