480 results about "Computerized system" patented technology

Automatically investigating security incidents and generating security incident reports using a Large Language Model (LLM). A computerized system receives an incoming Security Alert Message pertaining to a possible security-related incident. The system automatically feeds into the LLM at least: the content of the Security Alert Message; the metadata of the Security Alert Message; context information describing a security domain; and organization context information pertaining to users and machines of that organization. The system automatically prompts the LLM to automatically investigate the Security Alert Message and to automatically generate a detailed Incident Report pertaining to the Security Alert Message.

Computerized systems and methods are described for converting traditional technology products into an “As a Service” (AaS) model, facilitating the transition from capital expenses (CapEx) to operational expenses (OpEx). Methods include receiving user inputs for technology product selections and accessing a Real-Time Data Mesh (RTDM) to retrieve data. An Advanced Analytics and Machine Learning (AAML) Module analyzes user inputs and market data, optimizing the conversion into subscription-based services. Process results are displayed to the user through a Single Pane of Glass User Interface (SPOG UI). An AaS Conversion Module performs transition of products into customizable subscription packages. This method emphasizes dynamic pricing based on usage, flexibility, and / or scalability of services. Methods are provided for real-time reporting, subscription management, and vendor system integration, enabling a comprehensive AaS conversion process suitable for modern technology products and services.

Computerized systems and methods for training a trajectory prediction model for autonomous driving vehicles. The systems and methods receive agent dynamics data, high-dimensional (HD) map data; a training data set; and a plurality of meta paths through the HIN representing lane transitions, each meta path comprising a sequence of lane nodes and edges. Scene encoding is performed on the agent dynamics data and the HD map data to produce a directed Heterogeneous Information Network (HIN) as a graph comprising nodes and edges. The trajectory prediction model is trained based on a comparison between a prediction, by the trajectory prediction model, of a positive or negative presence of the meta paths between nodes and meta path ground truth obtained from the training data set and between motion trajectory predictions, by the trajectory prediction model, and motion trajectory ground truth obtained from the training data set.

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 are described for integrating real-time insights across various entities involved in distribution processes. The system includes a Real-Time Data Mesh module for ingesting and harmonizing data from multiple sources, a Data Lake for storing harmonized data, and an Advanced Analytics and Machine Learning (AAML) module for generating insights using predictive analytics, anomaly detection, and recommendation engines. A Single Pane of Glass User Interface (SPoG UI) provides visualizations of these insights through interactive dashboards. The system supports customer, vendor, reseller, and associate systems, enabling efficient data exchange and synchronization. It employs natural language processing (NLP) for sentiment analysis, topic modeling for key theme identification, and clustering algorithms for customer segmentation. Continuous learning mechanisms ensure the system adapts to new data in real-time, enhancing decision-making and operational efficiency.

A computerized system and method may provide a robust, automated clustering procedure, including handling of outlier points, which may involve measuring and / or quantifying degrees of relevance and / or generality for a plurality of input entities. In some embodiments, a clustering procedure may be used, e.g., to generate a hierarchical, multi-tiered taxonomy of such entities. In some embodiments, a computerized system comprising a processor, and a memory, may be used for calculating a distance between nodes for each of a plurality of pairs of nodes, where the pairs may comprise a plurality of input entities and / or initial clusters; selecting one or more of the pairs based on the calculated distances; and merging one or more of the selected pairs, which may include a common node, into one or more final clusters. Some embodiments of the invention may allow routing interactions between remotely connected computer systems based on an automatically generated taxonomy.

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.

Computerized systems and methods are described for generating and optimizing vendor product roadmaps using predictive insights. Leveraging a Real-Time Data Mesh (RTDM) module, data from diverse sources including market trends, customer feedback, and technological advancements is aggregated and standardized. An Analytics and Machine-Learning (AAML) module analyzes this data to generate predictive insights, facilitating adjustments to existing product roadmaps. Dynamic adjustments are made using a roadmap optimization module, with communication facilitated through a Single Pane of Glass (SPoG) user interface (UI). Scenario analysis, decision-support systems, and continuous monitoring improve strategic decision-making.

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.

Computerized systems and methods are disclosed to generate a document from one or more first and second text prompts, generating one or more context-sensitive text suggestions using a transformer with an encoder on the text prompts and a decoder that produces a text expansion to provide the context-sensitive text suggestions based on the one or more first and second text prompts by applying generative artificial intelligence with token biased weights for zero-shot, one-shot or some-shot generation of the artificial intelligence context-sensitive text suggestions from the one or more first and second text prompts.

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 computer system and method for financial management and estate planning. The system provides data aggregation for both financial and non-financial assets to create a “virtual vault” from which the owner can view all information about his / her portfolio. In some embodiments, the system provides a tree visualization of the portfolio, which allows a structured organization that provides both a high level view and the ability to drill down to an in-depth analysis. In some cases, the system provides segmented permissions in which restricted access could be granted based on module and / or asset in the tree visualization.

Cross-channel user authentication is provided, to increase resilience of a computerized system to cyber-attacks and to fraudulent transactions. In a demonstrative scenario, a user utilizes his web browser on his laptop computer to log-in to a web server; which in turn extracts the IP address of the laptop computer. The web server triggers an application server to send a Push Notification to a Mobile App that had been installed on a smartphone of that user who just logged in; and that Push Notification causes that smartphone to respond to the application server with the IP address of the smartphone. If the IP address of the computer is not identical to the IP address of the smartphone, then the servers determine that the log-in attempt on the computer was possibly fraudulent, and fraud mitigation operations are initiated. Optionally, geo-location data, device orientation data, device motion data, or other parameters are used as part of the cross-channel authentication process.

Examples of the presently disclosed technology provide computerized systems and methods that automatically update digital maps in response to detected traffic sign updates in a rapid and efficient manner. Examples achieve these advantages by cascading traffic sign updates through multi-layered digital maps using specific methodologies tailored to different types of traffic sign updates. By providing rapid and accurate digital map updates, examples can improve performance for autonomous driving systems that rely on digital maps for determining vehicle navigation paths—thereby improving traffic safety and efficiency.

A computerized system and method to allow a safe, secure and efficient real-time access to the patient's private health records (PHR) stored in the encrypted format in a remote Private Health Vault (PHV) database. The system uses patient's private encryption key for encrypting and decrypting PHR stored in the PHV, and the patient controls access to the PHR and authorizes by electronic communications with the PHV server to allow doctors to have limited in duration access to the patient PHR. The patient's private keys may be stored in a remote Key Bank database, separately form the PHV database, and the location of the patient's PHV data may also require transmission of the location id from a separate Mapping server. Additional security is also provided by determining digital proximity of the doctor's and patient's mobile devices to the node device in the doctor's office, and terminating access when patient leaves the facilities. It also utilizes 2-way digital token exchange and confirmation of the exchange between the patent and doctor, as well as digital fingerprinting for the confirmation of identity.

A computer program product, computerized method, and system for generating a Hybrid Non-Fungible Token and using a Hybrid Non-Fungible Token to confirm an identity during an electronic transaction. The Hybrid Non-Fungible Token containing Forensic Data which will include at least one of the following; unique biometric data, unique state issued identifying documents or specific unique machine attribute data tied to a specific computerized system. The plurality of authenticating servers further utilizing the Zero Knowledge Proofs to ensure that the servers in a group are authorized to have access to the Blockchain containing the Hybrid Non-Fungible Tokens. The invention further includes an Ethereum Blockchain distributed on the plurality of authenticating servers.

A computerized system and method may provide automated clustering procedures where each clustered entity or node may be included in a plurality of clusters (e.g., more than a single cluster). Clustering procedures provided by some embodiments of the invention may involve measuring and / or quantifying degrees of relevance and / or generality for a plurality of entities or nodes. In some embodiments, a clustering procedure may be used, e.g., to generate a hierarchical, multi-tiered taxonomy of such entities. A computerized system comprising a processor, and a memory, may be used for ranking a plurality of nodes; select nodes based on the ranking; cluster selected nodes into intermediate clusters; calculate distances between unselected nodes and intermediate clusters; and cluster unselected nodes and intermediate clusters into final clusters based on the calculated distances. Some embodiments of the invention may allow routing interactions between remotely connected computer systems based on an automatically generated taxonomy.

A system may receive a request to perform machine learning inferences, the request including a specification, according to which the machine learning inferences are to be performed on K input records based on N decision trees. The system may determine, based on the specification and a configuration of a computerized system, an optimal number k of the K input records and an optimal number n of the N decision trees to be processed in parallel by the system for performing the machine learning inferences, where 1≤k≤K and 1≤n≤N. The system may perform the machine learning inferences by executing parallel operations, whereby up to k input records and up to n decision trees are repeatedly processed in parallel by the system, to obtain inferences for each of the K input records based on the N decision trees.

Computerized systems and methods are described for managing vendor-agnostic configure-to-order (CTO) and quote-to-order (QTO) processes. A Real-Time Data Mesh (RTDM) is provided for aggregating, standardizing, and normalizing real-time data from various sources. A Single Pane of Glass User Interface (SPoG UI) facilitates dynamic interaction and visibility into vendor performance. An Advanced Analytics and Machine-Learning (AAML) Module analyzes product compatibility, optimizes pricing strategies, and predicts market trends. A Vendor-Agnostic CTO / QTO Integration Module (VACIM) includes a Process Standardization Engine and a Vendor Data Transformation Gateway to ensure uniformity across vendors. Methodologies within the invention automate data processing, integrate transformation gateways for data consistency, and employ rule engines driven by machine learning for decision-making, thereby streamlining vendor processes, enhancing scalability, and optimizing pricing strategies in a scalable, adaptable framework.

In some examples, a computerized system for analyzing images comprises at least one programmable processor and a machine-readable medium having instructions stored thereon which, when executed by the at least one programmable processor, cause the at least one programmable processor to execute operations comprising training an autoencoder using a plurality of image model training samples, the autoencoder comprising a plurality of interconnected layers and combined instances of neural networks, passing input data into a trained autoencoder model, the input data including at least one pixel image, encoding the input data into a compressed version of the input data, and decoding the compressed version of the input data to generate to create an output, the output including a sparse reconstruction of the input data, the output including a predicted pixel image label or score.

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.

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

A computerized system and method may provide automated clustering procedures where each clustered entity or node may be included in a plurality of clusters (e.g., more than a single cluster). Clustering procedures provided by some embodiments of the invention may involve measuring and / or quantifying degrees of relevance and / or generality for a plurality of entities or nodes. In some embodiments, a clustering procedure may be used, e.g., to generate a hierarchical, multi-tiered taxonomy of such entities. A computerized system comprising a processor, and a memory, may be used for ranking a plurality of nodes; select nodes based on the ranking; cluster selected nodes into intermediate clusters; calculate distances between unselected nodes and intermediate clusters; and cluster unselected nodes and intermediate clusters into final clusters based on the calculated distances. Some embodiments of the invention may allow routing interactions between remotely connected computer systems based on an automatically generated taxonomy.

Disclosed are systems, servers and methods for providing a novel framework that enables the unique cataloging and organization of audio files, upon which audio rendering experiences can be created and provided to requesting users, whether the users are individuals or third-party partners. The disclosed framework enables audio files to be stripped down, uniquely stored, and then stitched together in a novel manner that previously did not exist within the computing arts. The disclosed systems and methods, therefore, provide a novel platform where audio is not just provided to consumers, but audio experiences are compiled from various types of audio formats and types in a unique, dynamically determined manner for a listening user.

A process operable using a computerized system for providing an Environmental, Social and Governance (ESG) Report for a company of an industry sector, the computerized system including an input module, a pre-trained neural network in communication with the input module, and an output module in communication with the pre-trained neural network said process including the steps of (i) receiving an input dataset of the company via the input module, wherein said input dataset includes information of the company that is associated with ESG topics of said company including environment, social and governance; (ii) in a pre-trained neural network, generating ESG reporting data based on the input data of step (i); wherein the pre-trained neural network has been pre-trained utilising a plurality of datasets that are associated with ESG topics with an associated ESG Report including ESG Ratings corresponding, and corresponding to a plurality of companies from a plurality of industry sectors; and (iii) from the output module, providing ESG output data based on said ESG reporting data from (ii) for generation of an ESG Report for said company.

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.