323 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 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 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.

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 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 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.

There is provided a computerized system comprising a processing unit and associated memory configured to obtain a three-dimensional dataset informative of at least part of a tread of a tire, and determine, using the three-dimensional dataset, data informative of tread depth of the tire.

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.

An indication of a known condition of a user is received in a computerized system. The system is operable to monitor the user for one or more physical indications related to the known condition, and provide counseling for the known condition to the user responsive to observation of the one or more monitored physical indications via a networked responsive personal guidance system trained on the condition. The networked responsive personal guidance system may be adapted based on the received input from the user, such as to remember a state of prior interactions with the user to provide consistent conversational guidance across multiple login sessions for the user.

The invention is directed to a computer-implemented method of controlling a given machine (3), in particular a hydraulic machine (3). The method first comprises loading a control module (12) and an adaptation module (14). The control module (12) relies on a machine learning computational model trained to produce control signals to control each actuator of each machine of a set of machines, based on dynamics (21) of said each actuator and a latent variable for said each actuator. The adaptation module (14) relies on a machine learning computational model trained to estimate a latent variable for said each actuator based on dynamics (21) of said each actuator and control signals for said each actuator. The method further comprises concurrently running the control module (12) and the adaptation module (14) during an adaptation phase, whereby the control module (12) produces initial control signals based on initial dynamics (31) of one or more actuators of the given machine (3) and initial estimates of one or more latent variables for the one or more actuators, respectively, and the adaptation module (14) estimates one or more latent variables for the one or more actuators, respectively, based on the initial control signals and initial dynamics (31) of the one or more actuators as observed in response to applying the initial control signals to the one or more actuators. Finally, the method comprises operating, during an operation phase, the one or more actuators according to further control signals as produced by the control module (12) based on dynamics (31) of the one or more actuators and one or more latent variables as estimated by the adaptation module (14) for the one or more actuators, respectively. The invention is further directed to related computerized systems and other systems, as well as computer program products.

Computerized systems and methods for digital histopathology analysis are disclosed. In one embodiment, a series of deep learning networks are used that train, in succession, on datasets of successively increasing relevance. In some examples, learned parameters from at least a portion of one deep learning network are transferred to a next deep learning network in a succession of deep learning networks. In some examples, at least one of the deep learning networks includes a self-supervised learning network. In some examples, at least one of the deep learning networks includes an attention-based learning network. These and other examples and details are disclosed herein in various contexts including, for example evaluating genotypes of cancer tissue (e.g., bladder, prostate, or lung cancer) using histopathology images. In some examples, the context is to assist in predicting presence or absence of certain cancer genotypes and / or predicting patient responses to a new treatment.

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.

A computerized system and method for securing a cryptocurrency wallet is provided. A transaction proposed by a user of a cryptocurrency wallet is intercepted and an address of counterparty is extracted from the transaction. If the address of the counterparty matches with an address in an allow list, a risk score is displayed for the proposed transaction. In response to displaying the risk score, an indication whether to submit or abort the proposed transaction may be received. If the indication is to submit the proposed transaction, the transaction is submitted. If the indication is to abort the proposed transaction, the transaction is aborted. In some examples, the indication to submit or abort the proposed transaction may be automatically generated based on machine learning from user behavior with earlier transactions with similar risk scores.

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.

A method for providing an architectural component, the method includes (i) receiving, by a computerized system, a request to design a manufacturing process of an architectural component; (ii) determining, by the computerized process, the manufacturing process by applying an interactive design process that takes into account interactions between (a) material composition parameters, (b) architectural design parameters, and (c) manufacturing parameters; wherein the manufacturing process is an additive three dimensional printing process; and (iii) responding to the designed manufacturing process.

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.

Computerized systems and methods facilitate searches by identifying instances where the relative positional relationships of multiple search inputs exist within a corpus of text containing pre-compiled code. The search system identifies a presence or absence of the relative positional patterns by unidirectional sub searches anchored at a location that a primary search input occurs within the code. The directionality of the sub searches may be determined by the system based on the input field in which the search input was provided. The system may provide search results that facilitate review of the code at each location the presence or absence of the relative positional pattern exists within the corpus.