8797results about "Program control" patented technology

A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.

A server computer system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide a plug-in to an interface design software application executing on a computing device, the plug-in allowing the server computer system to communicate with the computing device to monitor design of an interface within the interface design software application; generate at least one recommendation for tagging at least one element of the interface; and send, for display on a display screen of the computing device within the interface design software application, the at least one recommendation for tagging the at least one element.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and / or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) using a gateway router that dynamically coordinates the agents based on prompt characteristics, user context, and / or real-time operational factors. Received inputs (e.g., prompts) are segmented into subcomponents (e.g., sub-queries), which are routed / mapped to candidate agents based on the output parameters of the subcomponent (e.g., performance thresholds, cost thresholds) and operational parameters (e.g., cost, performance metric values, user access restrictions, timing restrictions) of each agent. The gateway router maintains dynamic routing data structures for each agent that are continuously updated based on environmental stimuli (e.g., geo-political stimuli, sensor stimuli, agent stimuli). For example, the gateway router causes agents to dynamically switch between rule engines identified by the routing tables in response to detecting environmental stimuli. Responses from the candidate agents are aggregated into an output that is responsive to the input.

Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and / or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).

A method for creating and deploying plugins for use with one or more web-hosted large language model applications includes: displaying a user interface comprising a code template for creation of a plugin, wherein the code template comprises a context template, a native function template, and a semantic prompt template; receiving modifications to the code template via the user interface to form a modified code template, the modified code template comprising: custom context based on the context template; custom native code comprising one or more computer executable functions based on the native function template; and a custom prompt configured to prompt a large language model based on the semantic prompt template; generating a plugin based on the modified code template; and storing the plugin within a database of a plugin registry.

Systems and methods for generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.

Systems, methods, and devices that relate to routing requests to large language models (LLMs) are disclosed. In one example aspect, the system receives session-specific data elements in response to a request to generate an output using LLMs. The system determines a hierarchy of operational constraints including privacy protocols and performance requirements. Weights for a multi-variable optimization are dynamically updated using the session-specific data elements. The system executes the multi-variable optimization across candidate LLMs that satisfy privacy constraints and optimize performance constraints. Based on the optimization, at least one candidate LLM is selected and the request is routed to it. In response to performance feedback, the system automatically selects a different LLM to improve one constraint, resulting in degradation of another constraint.

Serverless containerization of a collaboration application improves deployment and security of the collaboration application. A system may generate a container within a private cloud. The container may execute the collaboration application. The container is assigned an Internet Protocol (IP) address and is associated with a security group that controls network traffic to and from the container. The system may include a network load balancer configured with the IP address assigned to the container that allocates computational resources for executing the collaboration application. The system may include serverless resources such as a database and filesystem that is accessible to the container within the private cloud. The system may include a private cloud (PC) endpoint configured to establish a private connection with an end user interface, wherein the private connection provides secure network traffic between the PC endpoint the end user interface within a private network of the PC endpoint.

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining a learned control flow directed graph for executable code of an application by observing executions of transitions during an observation period and determining destinations of indirect transfers based on the learned control flow directed graph. Next a disassembly of the executable code is determined based on the learned control flow directed graph, the destinations of the transfers, and the executable code.

A federated distributed computational system enables secure collaboration across institutions for unified biological and multiomics data analysis. It comprises interconnected computational nodes managed by a central federation manager. Each node includes specialized components: a local computational engine for biological data processing, a privacy-preservation system, a knowledge integration component leveraging dynamic knowledge graphs, and a secure communication interface. The federation manager coordinates computational activities while ensuring security, privacy, legality, and contractual adherence. This architecture allows institutions, citizen scientists, and patients to collaborate on complex biological analyses without compromising sensitive data. By enabling shared computational resources and expertise, the system facilitates breakthrough discoveries while maintaining confidentiality. Additionally, it supports pro-rata or contractually defined participation in resultant benefits or knowledge, ensuring equitable collaboration.

A network environment comprises a plurality of host machines that are communicatively coupled to each other via a network fabric comprising a plurality of switches that in turn include a plurality of ports. Each host machine comprises one or more GPUs that execute customer workloads. Described herein are different approaches that provide for addressing the problem of handling network overlay encapsulation without causing adverse impact to the performance of workloads executed on the GPU clusters.

A system and method of securing and virtualizing firmware trusted platform modules (TPMs) for virtualizing a hardware security module (HSM) for a host within a network fabric is provided. The system and method include operably coupling a set of components associated with a host comprising a data processing unit (DPU) secure vault and a firmware trusted platform module (TPM). The firmware TPM is configured as a virtual firmware TPM. The DPU secure vault secures the virtual firmware TPM into one or more virtual firmware TPMs based on one or more partitions configured with the DPU secure vault. A virtual TPM manager communicatively coupled to the DPU secure vault manages one or more virtual TPMs for hosting a set of Virtual Machines (VMs) or Containers.

Systems for explainable large language model routing with immutable audit trails are disclosed. The system receives a query and determines its characteristics including complexity, domain, regulatory constraints, and performance requirements. It retrieves profiles for multiple LLMs from a model matrix containing performance attributes, resource consumption, and compliance parameters. The system selects a particular LLM by balancing resource consumption with performance requirements, evaluating regulatory compliance, ranking LLMs based on these factors, and prioritizing models with successful processing history. The system generates a human-readable explanation of the selection including decision factors, rationale, and alternatives considered. Finally, it records the selection and explanation in a tamper-evident, immutable audit trail data structure.

The invention relates to a method and device for generating a recommendation for correcting a configuration file of an item of infrastructure of a computing environment within which services run. The infrastructure is deployed automatically from said configuration file. The method includes obtaining a runtime report that includes a compliance anomaly detected in the configuration file while said service is being run, and presenting an anomaly correction request as input to a previously trained generative artificial intelligence model. The correction request includes a question formulated in natural language and anomaly context information that includes at least the runtime report. The method also includes obtaining as output a configuration file correction recommendation, validating the recommendation for correcting the configuration file, determining a similarity measurement with respect to the context information, comparing with an acceptability threshold, and when the similarity measurement is validated, submitting the configuration file correction recommendation for user evaluation.

An apparatus to facilitate lossless and lossy automatic hardware compression in graphics-to-graphics network links is disclosed. The apparatus includes compressor / decompressor circuitry (CDC) integrated with physical layer (PHY) intellectual property (IP) hardware circuitry for a graphics processor unit (GPU)-to-GPU communication link communicably coupling a first GPU to one or more other GPUs, the CDC to: receive a data message from the first GPU, wherein the data message is in an uncompressed format; determine that a compression process is to be applied to the data message; apply the compression process to the data message to generate a compressed data message; and cause a GPU link IP hardware circuitry that comprises the PHY IP hardware circuitry to transmit the compressed data message over the GPU-to-GPU communication link.

The present invention relates to the technical field of computers, and provides a log processing method and system, a log management platform, and an electronic device. The method comprises: acquiring real-time log data of each service system, and storing the log data in a message queue; according to a preset time window, writing the log data in the message queue into a target file of a specified storage directory; and in response to completion of data writing of a current time window, on the basis of the storage directory of the target file, calling a log analysis interface of a log analysis service to perform log analysis on the target file. The log processing method in the present invention reduces the risk incident rate of service systems, and improves the localization accuracy of an anomaly condition of a service log.

A system may receive a first user input requesting to provide an evaluator agent configuration for an evaluator agent. A system may receive a second user input specifying information associated with an agent to be evaluated. A system may receive a third user input specifying an evaluation tool, wherein the evaluation tool is configurable to evaluate the information associated with the agent. A system may receive a fourth user input specifying an evaluation tool configuration associated with the evaluation tool. A system may create the evaluator agent based on the evaluator agent configuration, wherein the evaluator agent configuration comprises an indication of the information associated with the agent to be evaluated, an indication of the evaluation tool, and an indication of the evaluation tool configuration. A system may include evaluating, using the evaluator agent, the information associated with the agent.

Systems and methods are disclosed for preserving patient privacy while allowing health data to be analyzed, managed, and stored in different geographical areas. One method for managing cross-border health data while preserving patient privacy includes: receiving a DICOM object from a hospital computing device for analysis; generating a unique case identifier for the DICOM object; validating the received DICOM object; if, based on the validation, the received DICOM object is valid, anonymizing the received DICOM object; updating the anonymous DICOM object to include the unique case identifier; compressing the updated DICOM object; and sending the compressed DICOM object to at least one data analysis web service(s).

The present application relates to the technical field of data storage. Disclosed are a multi-source heterogeneous distributed system, a memory access method and a nonvolatile readable storage medium. The method comprises: a first unified interconnection bus unit receiving, by means of a protocol adaptation interface module, a read request sent by a first device, and setting the cache state of a first memory unit to be an invalid state; sending to a second unified interconnection bus unit of a second device a request message obtained by packaging the read request, the second unified interconnection bus unit reading data from a second memory unit; setting the cache state of the second memory unit to be a shared state, and sending to the first unified interconnection bus unit a response message, the first unified interconnection bus unit storing in the first memory unit data carried by the response message; and setting the cache state of the first memory unit to be a shared state. Thus, deploying a unified interconnection bus unit on each device can achieve a consistent memory communication between the devices, thereby achieving memory sharing, and further improving the performance of the multi-source heterogeneous distributed system.

Systems and methods for artificial intelligence (Al) assisted workflow integration for software development in a digital model platform are provided. In one embodiment, the methods receive a user selection of a target digital tool from a plurality of digital tools that are not directly interoperable with each other. Receive a user request comprising a description of a function script executable by the digital model platform on a target digital model type associated with the target digital tool. Fine-tune a scripting Al agent on prior user actions involving the target digital tool and on a resource-capability mapping of the digital model platform. Generate the function script and generate a unit test script, where the unit test script when interpreted tests the function script against the description. Finally, interpret the unit test script to generate a verification result for the function script; and, generate a test report based on the verification result.

A QoS-aware service migration method in an IoV system based on CVX deep reinforcement learning is provided. Firstly, the optimization problem of service migration and resource allocation is decoupled into two sub-problems; then, a new actor-critic-based asynchronous-update deep reinforcement learning method is designed to explore the optimal service migration, where a delayed-update actor makes decisions of service migration and a one-step-update critic evaluates the decisions to guide the direction of the policy update; and on this basis, an optimal resource allocation method for an MEC server based on convex optimization is proposed to achieve the optimal resource allocation. According to the QoS-aware service migration method, extensive experiments are conducted based on a real-world testbed and a dataset of city vehicle trajectories, thereby verifying the feasibility and effectiveness of SeMiR. Compared with benchmark methods, the SeMiR has the best performance of service migration under various scenarios.

A cooling system running method includes obtaining an open source data set of a cooling system including components each being configured with parameter(s). The open source data set includes a parameter value corresponding to each parameter, and the parameter(s) configured for one component include a controllable running parameter. The method further includes determining an initial model for predicting power consumption of the cooling system, selecting a target algorithm using an open toolbox, training the initial model using the open source data set and the target algorithm to obtain a target model, deploying the target model to a controller of the cooling system, and causing the controller to at least control operation of the one component according to a parameter value corresponding to the controllable running parameter that is defined in a power consumption optimization policy determined by the controller using the target model.

Systems and methods are disclosed for preserving patient privacy while allowing health data to be analyzed, managed, and stored in different geographical areas. One method for managing cross-border health data while preserving patient privacy includes: receiving a DICOM object from a hospital computing device for analysis; generating a unique case identifier for the DICOM object; validating the received DICOM object; if, based on the validation, the received DICOM object is valid, anonymizing the received DICOM object; updating the anonymous DICOM object to include the unique case identifier; compressing the updated DICOM object; and sending the compressed DICOM object to at least one data analysis web service(s).

An apparatus is provided that includes a network interface to transmit and receive data packets over a network; a memory including one or more buffers; an arithmetic logic unit to perform arithmetic operations for organizing and combining the data packets; and a circuitry to receive, via the network interface, data packets from the network; aggregate, via the arithmetic logic unit, the received data packets in the one or more buffers at a network rate; and transmit, via the network interface, the aggregated data packets to one or more compute nodes in the network, thereby optimizing latency incurred in combining the received data packets and transmitting the aggregated data packets, and hence accelerating a bulk data allreduce operation. One embodiment provides a system and method for performing the allreduce operation. During operation, the system performs the allreduce operation by pacing network operations for enhancing performance of the allreduce operation.

The present disclosure relates to secure deployment of model weights from a generative artificial intelligence (GenAI) platform to a cloud service. The method includes accessing the model metadata and a set of weights of a GenAI model associated with a GenAI platform. These model weights may be encrypted using a first encryption key that may be provided in the model metadata. These encrypted model weights may be decrypted based on the model metadata by utilizing the first encryption key from the model metadata. Each key may be associated with the specific type of GenAI model. Before storing the model weights from the GenAI platform cloud tenancy to a cloud storage in GenAI home region, the model weights may be encrypted again by utilizing a second encryption key. This encryption by the cloud may enable independent control over the sensitive information during transit and storing.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.