Abstract: Object automated determination of monitors associated with a component (i.e., application/software, middleware, hardware or the like) that has yet to be implemented within a computing environment infrastructure. Such a determination of the monitors relies on deploying one or more probes that scan through various, if not all, layers of the computing environment infrastructure, such as the hardware, the network, storage, operating systems, virtual layer, middleware, database, application layer, and/or the application to identify possible monitoring requirements. In this regard, the probes scan the environment to determine downstream services that effect the component and the upstream services effected by the component and the set-up/configurations associated with those services. Once the services and corresponding configurations have been identified, the appropriate monitors can be determined.

Abstract: A system for remediating computing-related events by taking into account the past performance of remediation processes and the current run-time environment in which the remedy will be deployed. Robotic Process Automation is implemented to identify computing-related events requiring automation and to record actions that are taken to remediate the events. Robotic scripts (bots) are created based on the recorded actions and are implemented in a learning-mode, in which run-time conditions and level of performance are recorded, such that, run-time conditions and performance are determinative of which bot or combination of bots are implemented for subsequent occurrence of computing-related events. Additionally, remedial process associated with the bot(s) may be executed in a simulated environment prior to the production run-time computing environment to validate the bot and insure the likelihood of effective performance of the remedial process in the run-time production environment.

Abstract: Methods, systems, and apparatuses for blockchain-based automated user matching are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive match parameters from one or more computing devices associated with supply side entities and demand side entities. The node may execute one or more artificial intelligence algorithms to match the supply side entities with the demand side entities based on the received match parameters. In some instances, the artificial intelligence algorithms may be stored in a smart contract on a blockchain and the matching may be performed through execution of the smart contract by nodes of the decentralized P2P network.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine, a tagging engine, and a resource allocation engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to modify metadata for the audio file to include AI-defined tags. The resource allocation engine is configured to identify a storage location from among the plurality of storage devices based on tags associated with the audio file and send the audio file to the identified storage location.

Abstract: Embodiments of the present invention provide an automated network security system for dynamically managing network security rules. The system uses a cognitive engine to capture network traffic and analyze behavioral data about said network traffic. Based on analysis of the behavioral data, the system may identify one or more vulnerabilities in the network security system and determine one or more changes to the network security rules to remedy the one or more vulnerabilities. The system further uses a robotic process automation system to test, simulate, and implement the one or more changes to the network security rules for the network.

Abstract: Systems and methods for dynamically allocating a plurality of servers for use in a server build process is provided. The system may include a receiver module configured to receive at least one server build request including a requested time interval. The system may include an analysis module configured to determine if the requested time interval is greater than or less than a pre-determined threshold value and an availability status of each of the servers. The system may include a selection module configured to select a server configuration type. The server configuration type may be based on the determination of the requested time interval being greater than or less than the pre-determined threshold value. The configuration types may be selected from a group of pre-determined configuration types being one of a minimal server type, a tolerant server type and an optimal server type.

Abstract: A system for remediating computing-related events by taking into account the past performance of remediation processes and the current run-time environment in which the remedy will be deployed. Robotic Process Automation is implemented to identify computing-related events requiring automation and to record actions that are taken to remediate the events. Robotic scripts (bots) are created based on the recorded actions and are implemented in a learning-mode, in which run-time conditions and level of performance are recorded, such that, run-time conditions and performance are determinative of which bot or combination of bots are implemented for subsequent occurrence of computing-related events. Additionally, remedial process associated with the bot(s) may be executed in a simulated environment prior to the production run-time computing environment to validate the bot and insure the likelihood of effective performance of the remedial process in the run-time production environment.

Abstract: Aspects of the disclosure relate to parsing page content to determine tag sequences and synthesizing content associated with the determined tag sequences to produce audio and/or braille output relative to user preferences and input. In a first embodiment, a user computing device may receive a page document corresponding to a uniform resource locator (URL) of the third party computing platform, identify one or more tag sequences of the page document, calculate an expected reading time for each of the one or more tag sequences, generate a summary associated with each of the one or more tag sequences of the page document, and produce an output of the summary. In a second embodiment, a server infrastructure may activate an interface with the user computing device and may perform the aforementioned processes in order to increase processing efficiency and decrease computing load at the user computing device.

Abstract: Object automated determination of monitors associated with a component (i.e., application/software, middleware, hardware or the like) that has yet to be implemented within a computing environment infrastructure. Such a determination of the monitors relies on deploying one or more probes that scan through various, if not all, layers of the computing environment infrastructure, such as the hardware, the network, storage, operating systems, virtual layer, middleware, database, application layer, and/or the application to identify possible monitoring requirements. In this regard, the probes scan the environment to determine downstream services that effect the component and the upstream services effected by the component and the set-up/configurations associated with those services. Once the services and corresponding configurations have been identified, the appropriate monitors can be determined.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine and a tag management engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tag management engine is configured to receive an audio file, identify tags linked with the audio file, to determine an access frequency for the audio file within a predetermined time period, and to adjust the activity level of the tags based on the access frequency. The tag management engine is further configured to remove tags from the set of tags with an activity level that is less than a purge threshold.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine and a tagging engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to identify observed concepts within the audio file. The tagging engine is further configured to compare the observed concepts to the first set of concepts, to determine one or more observed concepts matches concepts linked with AI-defined tags, and to modify metadata for the audio file to include AI-defined tags.

Abstract: A natural language processing system that includes an artificial intelligence (AI) engine, a tagging engine, and a resource allocation engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to modify metadata for the audio file to include AI-defined tags. The resource allocation engine is configured to identify a storage location from among the plurality of storage devices based on tags associated with the audio file and send the audio file to the identified storage location.

Abstract: Systems and methods for managing and repairing errors occurring on a plurality of servers in an automated server build process is provided. Systems may include a first error code from a plurality of error codes. The first error code may be associated with a failure. The system may further include a plurality of sets of action codes. When the failure occurs on a server, included in the plurality of servers, during the server build process, the system may be configured to receive an error code from the server that may correspond to the failure and check the error code against previously recorded error codes recorded on an error code table. When the error code is included in the error code table, the system may retrieve the corresponding action code, assign the action code to the failure and execute the action code on the server.

Abstract: Methods, systems, and apparatuses for blockchain-based automated user matching are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive match parameters from one or more computing devices associated with supply side entities and demand side entities. The node may execute one or more artificial intelligence algorithms to match the supply side entities with the demand side entities based on the received match parameters. In some instances, the artificial intelligence algorithms may be stored in a smart contract on a blockchain and the matching may be performed through execution of the smart contract by nodes of the decentralized P2P network.

Abstract: Methods, systems, and apparatuses for blockchain-based digital token utilization are described herein. Data corresponding to digital tokens may be stored on a blockchain associated with a decentralized peer-to-peer (P2P) network. Smart contracts for collecting digital tokens in exchange for service provider tokens may be deployed to the blockchain. Smart contracts for distributing digital tokens based on credit tokens may be deployed to the blockchain. Through execution of the smart contracts, digital tokens may be utilized by users in various ways.

Abstract: Methods, systems, and apparatuses for blockchain-based property management are described herein. According to a first embodiment of the disclosure, a plurality of computing systems may be established at a plurality of points along a supply chain associated with the manufacture, shipment, and sale of a physical property item. At each of the plurality of points, the plurality of computing systems may identify data corresponding to the physical property item, which may be stored on a blockchain associated with a decentralized peer-to-peer (e.g., P2P) network. According to a second embodiment of the disclosure, content creator computing devices may transmit digital property item upload requests to a digital property management computing device, which may generate smart contracts corresponding to the requests. Through execution of the smart contracts, access may be granted to digital property items and fees may be provided to the content creator computing devices.

Abstract: A dynamic cloud stack testing system comprises a cloud network with cloud components and a cloud stack server coupled to the network. The server includes an interface, a memory, a cloud stack configuration engine, and a cloud stack testing engine. The interface receives a cloud stack request from a user device that includes functionality parameters. The memory stores historic cloud stack combinations. The cloud stack configuration engine identifies cloud components associated with the functionality parameters and determines a cloud stack configuration that incorporates them. The cloud stack testing engine determines a cloud stack configuration test. The cloud stack testing engine executes the test, and stores results and the associated cloud stack configuration in the memory.

Abstract: Methods for machine-learned detection and removal of malicious software within a network are provided. Methods may record environment behavior of an application and a plurality of components. The plurality of components may touch the application. Methods may generate a baseline dataset based on the recorded environment behavior. Methods may schedule snapshots of the application. Methods may take snapshots of the application and the components based on the scheduling. Methods may store the snapshots in a repository. Methods may monitor the application and the components, using the stored snapshots, for any deviation in the environment behavior. Methods may detect a deviation in the behavior of the application or components. Methods may take a snapshot, outside of the scheduling, of the application and components upon detection of the deviation. Methods may determine that the deviation is unwarranted. Methods may revert the application and components back to a previous version.

Abstract: Methods, systems, and apparatuses for blockchain-based property management are described herein. According to a first embodiment of the disclosure, a plurality of computing systems may be established at a plurality of points along a supply chain associated with the manufacture, shipment, and sale of a physical property item. At each of the plurality of points, the plurality of computing systems may identify data corresponding to the physical property item, which may be stored on a blockchain associated with a decentralized peer-to-peer (e.g., P2P) network. According to a second embodiment of the disclosure, content creator computing devices may transmit digital property item upload requests to a digital property management computing device, which may generate smart contracts corresponding to the requests. Through execution of the smart contracts, access may be granted to digital property items and fees may be provided to the content creator computing devices.

Abstract: Systems and methods for dynamically allocating a plurality of servers for use in a server build process is provided. The system may include a receiver module configured to receive at least one server build request including a requested time interval. The system may include an analysis module configured to determine if the requested time interval is greater than or less than a pre-determined threshold value and an availability status of each of the servers. The system may include a selection module configured to select a server configuration type. The server configuration type may be based on the determination of the requested time interval being greater than or less than the pre-determined threshold value. The configuration types may be selected from a group of pre-determined configuration types being one of a minimal server type, a tolerant server type and an optimal server type.