Abstract: Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation system with data structure processing are provided herein. An example computer-implemented method includes processing, into one or more data structures comprising resource-related task allocation data, information pertaining to at least one resource-related task request for at least one given user; generating filtered data by processing at least a portion of the one or more data structures using one or more machine learning-based filtering techniques; generating at least one system recommendation for allocating the at least one resource-related task request by processing at least a portion of the filtered data; and performing one or more automated actions based at least in part on the at least one generated system recommendation.

Abstract: In some implementations, a model system may train a deep learning model to determine baseline figures and stretch figures and may train a shallow learning model to determine likelihoods associated with achieving the baseline figures and the stretch figures. The model system may receive indicators associated with a current entity and may provide the indicators to the deep learning model to generate a baseline figure and a stretch figure. The model system may receive progress data associated with the current entity. The model system may provide output based on the deep learning model and the progress data to the shallow learning model to generate a likelihood associated with the current entity achieving at least one of the baseline figure or the stretch figure. The model system may selectively provide an alert to a user based on the likelihood.

Abstract: Methods, apparatus, and processor-readable storage media for artificial intelligence systems with machine learning-based processing of data structures are provided herein. An example computer-implemented method includes processing data, pertaining to at least one task to be executed, into one or more task-related data structures; predicting one or more classifications for one or more of at least one user and at least one system by processing at least a portion of the one or more task-related data structures using one or more machine learning techniques trained using one or more user and system performance-related data structures, the one or more classifications being associated with likelihood of executing the at least one task; and performing one or more automated actions related to executing the at least one task based at least in part on the one or more predicted classifications.

Abstract: An apparatus comprises at least one processing device configured to identify a plurality of backup operations to be performed in a backup infrastructure environment comprising two or more backup servers, to generate a first data structure characterizing a prioritization of at least a subset of the plurality of backup operations, and to generate a second data structure characterizing status of the two or more backup servers in the backup infrastructure environment. The at least one processing device is also configured to determine, utilizing at least one machine learning model that is implemented by the at least one processing device and that takes as input the first data structure and the second data structure, an execution schedule for the subset of the plurality of backup operations, and to execute the subset of the plurality of backup operations in the backup infrastructure environment in accordance with the determined execution schedule.

Abstract: Methods, apparatus, and processor-readable storage media for predicting resource-related values using multi-dimensional machine learning-based techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one resource-related activity involving at least one resource; predicting one or more values associated with the at least one resource by processing at least a portion of the obtained data using one or more machine learning techniques; predicting one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data using the one or more machine learning techniques; and performing one or more automated actions based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity.

Abstract: Methods, apparatus, and processor-readable storage media for predicting resource-related failures using multi-dimensional-based machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one resource-related activity involving at least one resource and one or more users; predicting one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques; predicting one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques; and performing one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons.

Abstract: Methods, apparatus, and processor-readable storage media for predicting task execution efforts using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining intent information associated with at least a portion of a task by processing data related to the task using at least a first set of artificial intelligence techniques; determining task execution workflow data based at least in part on the intent information associated with at least a portion of the task; predicting one or more efforts associated with executing the task by processing at least a portion of the task execution workflow data using at least a second set of artificial intelligence techniques; and performing one or more automated actions based at least in part on at least one of the one or more predicted efforts associated with executing the task.

Abstract: A method comprises collecting parameters corresponding to processing by a first application programming interface of at least one application programming interface transaction, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, whether the at least one application programming interface transaction is anomalous. In the method, the first application programming interface is designated as being in an anomalous state responsive to predicting that the at least one application programming interface transaction is anomalous. One or more application programming interface requests for the first application programming interface are routed to a second application programming interface responsive to the anomalous state designation.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving information regarding a new sales opportunity from another computing device and determining one or more relevant features from the information regarding the new sales opportunity, the one or more relevant features influencing predictions of an opportunity outcome and an opportunity duration. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of an opportunity outcome of the new sales opportunity and a second prediction of an opportunity duration of the new sales opportunity based on the determined one or more relevant features. The method may also include, by the computing device, sending the first and second predictions to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a new application programming interface (API) specification and extracting one or more keywords from the new API specification. The method also includes identifying, using a trained machine learning (ML) model, one or more existing API specifications that are similar to the new API specification based on the one or more keywords from the new API specification and, responsive to the identification, outputting information regarding the one or more existing API specifications that are similar to the new API specification.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving information regarding a new lead from another computing device and determining one or more relevant features from the information regarding the new lead, the one or more relevant features influencing prediction of a lead conversion. The method also includes, by the computing device, generating, using a machine learning (ML) model, a prediction of a likelihood of the new lead converting to a sales opportunity based on the determined one or more relevant features based on the determined one or more relevant features and sending the prediction to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a new application programming interface (API) specification and extracting one or more keywords from the new API specification. The method also includes identifying, using a trained machine learning (ML) model, one or more existing API specifications that are similar to the new API specification based on the one or more keywords from the new API specification and, responsive to the identification, outputting information regarding the one or more existing API specifications that are similar to the new API specification.

Abstract: A method comprises collecting parameters corresponding to processing by a first application programming interface of at least one application programming interface transaction, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, whether the at least one application programming interface transaction is anomalous. In the method, the first application programming interface is designated as being in an anomalous state responsive to predicting that the at least one application programming interface transaction is anomalous. One or more application programming interface requests for the first application programming interface are routed to a second application programming interface responsive to the anomalous state designation.

Abstract: A display control apparatus includes one or more processors control display content in the display section, according to an interrupt request or a manipulation input, wherein the one or more processors display a first screen corresponding to the interrupt request in a first region in a display region of the display section when the interrupt request is received, and delete the first screen and display a second screen corresponding to a response to the interrupt request in a second region in the display region when the response is received by performing manipulation input to the first screen with the finger of the user, and a distance in a vehicle width direction between a driver's seat provided in a vehicle that includes the display section and the second region is greater than a distance in the vehicle width direction between the driver's seat and the first region.

Abstract: A system and method which dynamically generate proxy connections in a communication system. A client computer system has a processor that is coupled by a communications path including at least one proxy connection to a first target server. An algorithm for dynamically generating proxy connections is stored in machine readable storage. The algorithm is implemented by the processor causing the client computer system to execute generating an additional one of the proxy connections in response to receiving a request and to generate a program identifier which identifies the additional proxy connection. The processor determines if the program identifier is a unique program identifier. If the program identifier is not unique, the processor changes the identifier to render it a modified program identifier so that it is unique and stores the program identifier or the modified program identifier to a configuration file at the client computer system.

Abstract: A system and method which dynamically generate proxy connections in a communication system. A client computer system has a processor that is coupled by a communications path including at least one proxy connection to a first target server. An algorithm for dynamically generating proxy connections is stored in machine readable storage. The algorithm is implemented by the processor causing the client computer system to execute generating an additional one of the proxy connections in response to receiving a request and to generate a program identifier which identifies the additional proxy connection. The processor determines if the program identifier is a unique program identifier. If the program identifier is not unique, the processor changes the identifier to render it a modified program identifier so that it is unique and stores the program identifier or the modified program identifier to a configuration file at the client computer system.