Abstract: A method includes collecting current data and architecture state, collecting future data and architecture state; analyzing the current and/or future data and architecture state to generate deployment options; and causing the summary of options to be displayed. A computing system includes a processor and a memory comprising instructions, that when executed, cause the system to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed.

Abstract: Systems and methods for proactive support of computing assets are presented. In contrast to existing techniques of reactive support, the proactive support techniques disclosed herein automatically collect operating data from a plurality of computing devices, analyze the operating data to identify predictive indicators associated with error conditions, identify a subset of affected computing devices that match the predictive indicators, and execute corrective scripts to remediate or avoid such error conditions before problems are experienced on the affected computing devices. The operating data may be used to train a machine learning model in order to identify the predictive indicators associated with each error condition. In some embodiments, the corrective scripts may be automatically generated to adjust operating parameters or applications of the affected computing devices based upon the identified predictive indicators.

Abstract: Techniques for providing temporary document access for secure file sharing are disclosed. An example method includes receiving a request from a first user to share a file, and generating, by an encryption module, a secure access link corresponding to the file that does not indicate a storage platform or a file name. The example method also includes transmitting the secure access link to a second user, and receiving an input from the second user during the accessibility duration corresponding to the secure access link. Responsive to receiving the input, the example method includes validating the access key to determine whether or not the second user should be granted access to the file; and responsive to determining that the user should be granted access to the file, retrieving an updated version of the file from the storage platform, and transmitting the updated version of the file to the second user.

Abstract: The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.

Abstract: The systems and methods described herein relate to improvements to generative artificial intelligence systems through the use of generative artificial intelligence pipelines to supply external information to pre-trained large language models for use in answering queries. To improve the efficiency and accuracy of large language models in responding to user queries, according to various aspects described herein, such queries may be modified and augmented with additional relevant information and may be divided into multiple queries for parallel handling, the results of which may then be combined into a response. The additional relevant information may include portions of documents or other data sets to be used in generating the response. Additional aspects may further improve resilience and flexibility by managing the generation or implementation of such modified and augmented queries.

Abstract: ML methods and systems for cataloging and making recommendations based on domain-specific knowledge are disclosed. An example method includes: cataloging, using knowledge engines, data to develop knowledge repositories for respective domains; obtain current domain state data; obtain future domain state data; analyze, using first ML models, one or more of (i) data from the knowledge repositories, (ii) the first domain state data, and (iii) the second domain state data to identify a recommended set of one or more regulations, standards, policies and/or rules for a desired second domain state; analyze, using second ML models, (i) the recommended set and (ii) a current data and architecture state for a current computing environment to generate a summary of one or more cloud deployment options for migrating a current computing environment to a future computing environment for a future domain state; and cause the summary to be displayed on a computing device.

Abstract: The systems and methods described herein relate to improvements to generative artificial intelligence systems through the use of generative artificial intelligence pipelines to supply external information to pre-trained large language models for use in answering queries. To improve the efficiency and accuracy of large language models in responding to user queries, according to various aspects described herein, such queries may be modified and augmented with additional relevant information and may be divided into multiple queries for parallel handling, the results of which may then be combined into a response. The additional relevant information may include portions of documents or other data sets to be used in generating the response. Additional aspects may further improve resilience and flexibility by managing the generation or implementation of such modified and augmented queries.

Abstract: Systems and methods for providing a pattern-based cloud architecture facilitating deployment of pattern-based cloud applications are presented. The pattern-based cloud architecture comprises a base layer having one or more bases, a landing zone layer comprising one or more landing zones within each base, and an application pattern layer comprising one or more application patters for each landing zone. Each base provides base services and/or parameters common to all landing zones associated with such base. Each landing zone adds services and/or parameters specific to such landing zone, while inheriting those of the associated base. Application patterns define the requirements and capability of cloud-based applications that can be run within each associated landing zone.

Abstract: The systems and methods described herein relate to generative artificial intelligence systems using retrieval-augmented generation pipelines to supply information to large language models (LLMs). The potential for failures by such LLMs to return responses to prompts significantly increases with system complexity. To improve the resilience of the pipelines in handling such failures, various aspects described herein provide mechanisms for early detection and remediation of such prompt failure events. Thus, prompt failure events may be identified based upon (i) an elapsed time between sending a prompt and receiving a first token from the LLM exceeding a first threshold or (ii) an elapsed time between receiving such first token and receiving a last token exceeding a second threshold. Remediation may be achieved by causing a copy of the failed prompt to be sent to the LLM, without waiting for an error from the LLM provider or a standard network request timeout.

Abstract: Systems and methods for managing software resources such as machine learning/artificial intelligence models. The system includes a number of components that provide versioning services to both software resources and to data sets. A data storage component also addresses storage/retrieval functions for the system. A user interface element is also present to provide users with both a GUI and a command line interface. An assessment element assesses user selected software assets for project inclusion, with the assessment being based on good software development principles and practices. Once a project of selected software assets has passed the assessment stage, the software assets are pipelined for execution by an execution unit. The outputs of the execution unit are sent to a data output module automatically populates predetermined and preconfigured templates with the outputs to produce reports. The outputs may be final outputs or they may be intermediate results produced by the software assets.

Abstract: Techniques for a modeling platform associated with simulating risk models. According to certain aspects, systems and methods include simulating the impact of qualitative assumptions on the risk model. Accordingly, the risk model may include a hierarchical tree formed of component qualitative assumption objects and quantitative assumption objects. Quantitative assumption objects include indications of distribution function parameters associated with the assumption object and qualitative assumption objects include an indication of an impact on a parent quantitative assumption. When simulating the risk model, systems and methods may adjust sampled values quantitative assumption object when a child qualitative assumption object is enabled.

Abstract: Systems and methods for providing a pattern-based cloud architecture facilitating deployment of pattern-based cloud applications are presented. The pattern-based cloud architecture may comprise a base layer having at least a primary base, with each base supporting one or more landing zones of a landing zone layer. Each landing zone further supports one or more application patterns for cloud applications in an application pattern layer. Implementing the base layer includes receiving a set of base services and operating parameters defining a primary base and executing scripts to configure the primary base for providing the set of base services and operating parameters to any landing zones associated with such primary base. A bootstrap isolation entity may be established prior to and for the purpose of executing the scripts to implement the primary base. Additional bases may be implemented in the base layer by executing additional scripts within the primary base layer.

Abstract: Systems and methods for adjusting operating parameters of at least one pipelined software asset. Within a pipelined environment, an agent software asset is inserted immediately preceding a software asset whose operating parameters are to be adjusted. The agent software asset receives data and/or data sets from a user and such data and data sets are inserted/used by the software asset. The agent software asset also allows for a reporting of the output of other software assets to thereby provide users with intermediate outputs from the pipelined environment.

Abstract: Techniques for a modeling platform associated with simulating risk models. According to certain aspects, systems and methods include simulating the risk model over time. The risk model may include a hierarchical tree formed of component assumption objects associated with distribution functions. The systems and methods may apply a time function to generate time-adjusted values for the component assumption objects corresponding to defined distribution functions. The systems and methods may then combine the time-adjusted values to generate time-adjusted values for parent assumption objects.

Abstract: Systems and methods for optimizing asset maintenance protocols by predicting vegetation-driven outages are disclosed. An example method includes determining, by one or more processors, a failure probability for each asset in a set of assets within a designated area. The example method further includes defining, by the one or more processors, an asset risk for each asset in the set of assets based on the failure probability, and clustering, by the one or more processors, vegetation within the designated area to determine a predicted vegetation-driven outage. The example method further includes optimizing, by the one or more processors, a set of asset maintenance protocols corresponding to the set of assets based on the asset risk and the predicted vegetation-driven outage.

Abstract: A computer-implemented method includes receiving user input including codified knowledge management information and/or engine data; and processing the input using one or more trained machine learning models to generate one or more living documents. A computing system includes one or more processors; and a memory having stored thereon instructions that, when executed, cause the computing system to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents.

Abstract: The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.

Abstract: Machine learning methods and systems for developing security governance recommendations are disclosed.

Abstract: Systems and methods for using machine learning to dynamically assess contract parameters are disclosed. According to certain aspects, an electronic device may train a machine learning model using real-world pricing and contract data, access parameters associated with a potential contract for an entity, and analyzing, using the machine learning model, the accessed parameters. Based on the analysis, the machine learning model may output a set of potential terms for the potential contract. Data indicative of this output may be availed to the entity to be used in negotiating and executing the contract, among other uses.

Abstract: Systems and methods for improving an onboarding process of a use case received from a user device using machine learning by automatically evaluating a potential to implement machine learning on the use case and automating the onboarding process, which may include receiving and processing an initial use case data set using a trained tractability machine learning model to generate a first determination whether the use case is machine learning tractable. The trained tractability machine learning model is trained using historical tractability data. Generating an onboarding machine learning model for solving the use case based at least upon the first determination the use case is machine learning tractable. Receiving a feedback data set and processing the initial use case data set and the feedback data set using the trained onboarding machine learning model.