Abstract: In some examples, artificial intelligence and machine learning based product development may include ascertaining an inquiry, by a user, related to a product that is to be developed or that is under development, and ascertaining an attribute associated with the user. The inquiry may be analyzed to determine at least one virtual assistant from a set of virtual assistants to respond to the inquiry. The determined at least one virtual assistant may be invoked based on an authorization by the user. Further, development of the product may be controlled based on the invocation of the determined at least one virtual assistant.

Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: A document processing system processes documents including typewritten and/or handwritten data by converting them to document images for entity extraction. A received document is initially processed to generate a deep document data structured and for classification as one of a structured or an unstructured document. If the document is classified as a structured document, it is processed for entity extraction based on a matching template and image alignment of the document image with the matching template. If the document is classified as an unstructured document, entities are extracted by obtaining nodes and providing the nodes to a self-supervised masked visual language model.

Abstract: A system may support multiple tier serverless data foundation creation to support large data set processing. At a data ingestion tier, data ingestion serverless tasks may receive source data for processing. The data integration serverless tasks may filter and group the source data into file-object stored items. Further, data integration serverless tasks may capture metadata that, when paired with the file-object stored items, establishes the data foundation. The data foundation facilitates database-like performance in data operations in a database-less system. At the processing tier, the processing serverless tasks access the data foundation by iterating across the file-object stored items to generate output-object stored items. At the directed storage tier, directed storage serverless tasks capture metadata for the output-object stored items to establish an output data foundation or prepare the output data for storage in a data warehouse.

Abstract: A multi-layer consumption unit estimation (CUE) stack may generate a consumption preview for prescribing cloud computing resource utilization. An input layer of the CUE stack may obtain computing resource utilization tracking data, consumption metric data, application execution tracking data, and computing resource reservation data for a set of computing resources. A configuration layer of the CUE stack may determine a CUE interval and determine consumption metric modifiers for a selected identity associated with the set of computing resources. A CUE engine layer may generate a consumption preview by advancing a dynamic consumption credit input/output flow analysis and executing a direct utilization consumption determination.

Abstract: A device may receive API specifications associated with APIs, and may store the API specifications in a data structure. The device may process the API specifications to identify a system level set of API specifications that includes API specifications utilized by multiple systems, a release level set of API specifications that includes API specifications with different release dates, and a version level set of API specifications that includes API specifications with different versions. The device may process the system level set of API specifications, with a machine learning model, to determine system level differences in the system level set of API specifications, and may correct the system level set of API specifications, based on the system level differences, to generate a corrected system level set of API specifications. The device may cause the corrected system level set of API specifications to be implemented in the multiple systems.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media facilitating automated apparel design using deep learning techniques. For example, user instructions may be received as text data (or converted to text data from audio data representing user speech), and natural language processing (NLP) may be performed on the text data to interpret the user instructions. An apparel design may be generated in real-time/substantially real-time based on the user instructions. For example, the interpreted user instructions may be provided as input to at least one machine learning (ML) model that is configured to determine one or more visual apparel elements based on the user instructions and to generate the apparel design based on the visual apparel elements. One or more operations may be initiated based on the apparel design.

Abstract: A system for providing secure access to digital resources is provided that utilizes a blockchain platform. Using this blockchain platform, digital resource vendors create new digital tracking ledgers for their digital resource products such that updates to the digital resource products are accessible directly from a blockchain. Accordingly, these updates are deliverable in a protected and secure manner to consumers of the digital resources.

Abstract: A system may monitor transaction data pertaining to a plurality of transaction types received by a network order fulfillment system. The system may classify the transaction data into a plurality of alarm types based on pre-defined impact of an alarm type to a given transaction type. The system may analyze a plurality of performance parameters influencing a performance of the network order fulfillment system, and identify a performance parameter exhibiting an anomaly based on historical data, a current status of the plurality of the performance parameters and a predefined prediction model. The system may ascertain whether the identified performance parameter negatively impacts the performance of the network order fulfillment system, based on evaluation rules. The system may proactively implement a remediation action to remediate a potential fault caused by the identified performance parameter when the identified performance parameter negatively impacts the performance of the network order fulfillment system.

Abstract: Systems and methods for facilitating an automated customization of user interface are disclosed. The system may include a processor including an AI engine and a UI engine. The AI engine may receive an input data in the form of a pre-defined template that may facilitate the input data to be received in user-readable format. The AI engine may process the pre-defined template to extract input attributes corresponding to the input data. The AI engine may dynamically map the input attributes with sample attributes of a pre-stored file. Based on the dynamic mapping, the AI engine may obtain a form pertaining to an expected visual representation of the web page. The AI engine may facilitate responsive scaling of the form depending on size attributes of the user interface. The UI engine may include a form engine that may customize the form for generation of the web page.

Abstract: A machine learning (ML) based automated search system receives an entity information document including a plurality of entities and identifies new entities that are similar to the plurality of entities which are not included in the entity information document via automated searches. Entity intelligence reports are generated for the plurality of entities which are further used to extract search terms. The search terms are used for executing automatic searches for documents with relevant portions. The documents are further analyzed to identify other, new entities which are not included in the entity information document. Entity intelligence reports including information regarding the new entities are also generated. Significant attributes are determined for the entities. The significant attributes are further employed in ranking the new entities so that top ranked new entities are identified. Alerts can be generated based on the information regarding the new entities.

Abstract: Examples of an intelligent data quality application are defined. In an example, the system receives a data quality requirement from a user. The system obtains target data from a plurality of data sources. The system implements an artificial intelligence component sort the target data into a data cascade. The data cascade may include a plurality of attributes associated with the data quality requirement. The system may evaluate the data cascade to identify a data pattern model for each of the attributes. The system may implement a first cognitive learning operation to determine a mapping context from the data cascade and a conversion rule from the data pattern model. The system may establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation. The system may generate a data cleansing result corresponding to the data quality requirement.

Abstract: Systems and methods for active state synchronization between distributed ledger technology (DLT) platforms are provided. A system may store an origin blockchain compliant with an origin DLT. The system may further store a target blockchain compliant with a target DLT. The target DLT may be different from the origin DLT. The system may include a DLT object synchronizer with access to the origin blockchain and the target blockchain. The DLT object synchronizer may receive, from an exchange node, a request to synchronize an origin instance of a DLT object between the origin blockchain and the target blockchain. The DLT object synchronizer may select a target instance of the DLT object on the target blockchain. The DLT object synchronizer may format origin data from the origin instance for compliance with the target DLT. The DLT object synchronizer may synchronize the origin instance and the target instance.

Abstract: Examples of a data migration system are provided. The system may receive a data migration requirement. The system may sort the data stored in the on-premise data warehouse into a plurality of data domains. The system may map the plurality of data domains to a usage parameter index and a storage parameter index to determine a data usage pattern and a data storage pattern. The system may evaluate the data storage pattern and the data usage pattern to determine a data assessment index. The system may determine a data migration index from mapping a plurality of cloud platforms to the data assessment index. The system may determine a data migration model compatible with the data assessment index. The system may generate a data migration output comprising the layout for transferring data stored in the on-premise data warehouse to a compatible cloud platform to resolve the data migration requirement.

Abstract: A customized industrial graph knowledge base for an industrial operation includes a graph database storing nodes of multiple dimensions predefined according to the nature and characteristics of the industrial operation. The nodes are extracted from baseline, domain-specific, and implementation specific industrial knowledge data sources using various analytics for structured and unstructured data. The customized industrial graph knowledge base further includes edges representing relationships between nodes that are either inter-dimensional or intra-dimensional. The importance of each node to the industrial operation is further quantified using a graph model and is included in the graph database as a composite filtering parameter.

Abstract: In some examples, software CoBot engineering, execution, and monitoring may include extracting a CoBot requirement from a requirement specification for a CoBot that is to be implemented. Based on application of a CoBot description language to the CoBot requirement, a CoBot workflow that specifies a plurality of tasks to be performed by the CoBot may be generated. A determination may be made as to whether a task is to be performed by a bot or by a human. A team that includes a plurality of bots and at least one human may be generated to execute the CoBot workflow. The bots of the team may be prioritized to identify a bot that is a best match to the CoBot requirement. The CoBot that includes configured bots may be deployed in an operational environment to perform the CoBot workflow.

Abstract: An intelligent data ingestion and governance method and system is disclosed. A set of data requirements is received from a user. The set of data requirements includes multiple different formats and corresponding location information indicating a plurality of corresponding different data sources. One or more data policies are also received from the user as part of the set of data requirements. A configuration file is automatically generated using the set of data requirements. A new dataset is retrieved from the plurality of corresponding different sources of data, using the generated configuration file. The retrieved dataset is classified, and metadata for the retrieved dataset is provided. Actionable policies are automatically generated using the metadata and the user defined data policies, and a compliance engine is generated. Security of access to the retrieved input data is controlled using the generated compliance engine.

Abstract: Implementations include actions of receiving consumer-specific data and ID-specific data from an identification presented by a consumer to a vending machine, processing at least a portion of the ID-specific data to determine one or more of whether the identification is unexpired and whether the identification is authentic, and serving the consumer from the vending machine at least partially in response to determining that the identification is unexpired and that the identification is authentic and determining that the consumer is authentic relative to the identification.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: A system includes a multi-layer capacity configuration optimization (CCO) stack to generate a token containing prescriptions for optimize capacity configuration of a database container in a NoSQL database cloud service. The system may aggregate the capacity utilization data; predict, based on the aggregated capacity utilization data, respective prediction-based processing capacity utilizations for the database container; determine a target processing capacity utilization value from the prediction-based processing capacity utilizations; calculate respective provisioned processing capacity utilizations based on the target processing capacity utilization value; evaluate a consumption metric based on the prediction-based processing capacity utilizations and the provisioned processing capacity utilizations; select one of the predetermined capacity modes as a recommended capacity mode for the database container based on the consumption metric.