Abstract: The disclosed system and method focus on automatically generating questions from input of written text and/or audio transcripts (e.g., learning materials) to aid in teaching people through testing their knowledge about information they have previously been presented with. These questions may be presented to an end user via a conversational system (e.g., virtual agent or chatbot). The user can iterate through each question, provide feedback for the question, attempt to answer the question, and/or get an answer score for each answer. The disclosed system and method can generate questions tailored to a particular subject by using teaching materials as input. The disclosed system and method can further curate the questions based on various conditions to ensure that the questions are automatically selected and arranged in an order that best suits the subject taught and the learner answering the questions.

Abstract: The present disclosure relates to a system and a method for model control platform stack. The method includes, at an input layer of a model control platform stack, receiving input data. At a governance layer of the model control platform stack, the method includes maintaining a probe and model inventories; selecting a model, a monitoring location point, and a probe; and deploying, based on the selections of the probe and the model, a container to an orchestration layer of the model control platform stack. At the orchestration layer of the model control platform stack, the method includes accessing the container; using the container to deploy the probe and the model; scheduling an execution of the model to determine inference associated with the input data; during the execution, extracting probe data, using the probe, from the monitoring location point; and adjusting, based on the probe data and the inference, the model.

Abstract: Examples of a system for intelligent communication management for content summarization are provided. In an example, the system receives a summary generation requirement. The system establishes a question database for generation a set of questions for a user corresponding to the summary generation requirement. The system implements an artificial intelligence component to sort the answers obtained from the question and generates a user-specific knowledge database. The system may use the user-specific knowledge model for generation of further questions for the user. The system may assist a user with collecting information in a conversational manner mode and to automatically produce intelligible deliverables. The system may accept multiple modes of input while collecting information from a user. The system may be used for an automatic summary generation for customer service conversations, interviews, conferences and presentations, a person's holiday chronicles, and the like.

Abstract: Implementations of the present disclosure include providing, by a security platform, graph data defining a graph that is representative of an enterprise network, the graph including nodes and edges between nodes, a set of nodes representing respective assets within the enterprise network, and a node representing a process executed within a system of the enterprise, each edge representing at least a portion of one or more lateral paths between assets in the enterprise network, determining, for each asset, a contribution value indicating a contribution of a respective asset to operation of the process, determining, for each asset, an impact value based on a total value of the process and a respective contribution value of the asset, and implementing one or more remediations based on a set of impact values determined for the assets, each remediation mitigating a cyber-security risk within the enterprise network.

Abstract: Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Abstract: A system and method for automatically detecting misinformation is disclosed. The misinformation detection system is implemented using a cross-stitch based semi-supervised end-to-end neural attention model which is configured to leverage the large amount of unlabeled data that is available. In one embodiment, the model can at least partially generalize and identify emerging misinformation as it learns from an array of relevant external knowledge. Embodiments of the proposed system rely on heterogeneous information such as a social media post's text content, user details, and activity around the post, as well as external knowledge from the web, to identify whether the content includes misinformation. The results of the model are produced via an attention mechanism.

Abstract: An automatic artwork review system validates an artwork or a product label based on a received label specification document. Text extracted from the product label is chunked into sentences and words. Character-wise comparison is executed to identify the best match text from the label specification document for the sentence chunks from the product label. If the corresponding best match texts bears a similarity higher than a predetermined threshold to selected text including one or more sentence chunks, no errors are raised. If the similarity of the best match text to the selected text is not higher than the predetermined threshold, the specific errors occurring in the selected text and the particular portions where such errors are present are identified. The information regarding the errors can be output via one or more of an output user interface or a label compliance report.

Abstract: Call preparation engine for customer relationship management (“CRM”) is presented. Example embodiments of the present invention include invoking an intelligence assistant to retrieve lead details, customer information, and insights for use during a call between a tele-agent and a customer; administering tele-agent call preparation notes for use during the call between the tele-agent and the customer; and displaying, through a call preparation cockpit, the lead details, customer information, insights and tele-agent call preparation notes.

Abstract: Implementations of the present disclosure include providing a graph that is representative of an enterprise network and includes nodes and edges, a set of nodes representing assets within the enterprise network, each edge representing a lateral movement path between assets, determining, for each asset, a contribution value indicating a contribution of an asset, determining lateral movements paths between a first asset and a second asset, providing a lateral movement path value representative of a difficulty in traversing a respective lateral movement path, identifying a set of remediations based on remediations defined for one or more vulnerabilities associated with issues identified for assets, each remediation mitigating a cyber-security risk within the enterprise network, and prioritizing the two or more remediations based on contribution values of assets, lateral movement path values of paths, and one of lateral movement complexity values of respective segments of paths and costs of respective remediation

Abstract: Systems and methods for evaluating attributes in supply chain management is disclosed. The system may receive data from a set of data sources corresponding to a supply chain associated with at least a product, pre-process the data based on integration of the data from each of the set of data sources, generate supply chain data based on the integrated data, analyze, via an orchestration engine, the supply chain data to assess an impact of the supply chain data on the supply chain, predict, via the orchestration engine, a state associated with a purchase event of the product in the supply chain, and generate a resolution flow to be executed in the supply chain for managing the predicted state associated with the purchase event of the product.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support creation and execution of automated sequences of user interface (UI) interactions. To facilitate creation and execution of automated sequences of UI interactions, an automation engine is provided and includes a model configured to capture image data when creating the sequence of UI interactions. The model may also be used during replay of the sequence of UI interactions. For example, the model may be used during replay of the sequence of UI interactions to locate UI elements corresponding to the UI interactions, or to perform pre- and/or post-validation of action execution. The automation engine may also provide processes to enable location of dynamic content, such as UI elements that may be presented in different or unexpected locations, and processes to address complex UI elements, such as data grids, tree views, and drawings (e.g., CAD drawings).

Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: A device may receive training data that includes datasets associated with natural language processing, and may mask the training data to generate masked training data. The device may train a masked event C-BERT model, with the masked training data, to generate pretrained weights and a trained masked event C-BERT model, and may train an event aware C-BERT model, with the training data and the pretrained weights, to generate a trained event aware C-BERT model. The device may receive natural language text data identifying natural language events, and may process the natural language text data, with the trained masked event C-BERT model, to determine weights. The device may process the natural language text data and the weights, with the trained event aware C-BERT model, to predict causality relationships between the natural language events, and may perform actions, based on the causality relationships.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving training data for multiple datasets that include information about a computing process. The training data is received at a computing system that includes a data manager, a data classifier, and a machine learning (ML) system. The data classifier annotates the training data as being associated with a particular dataset and as being descriptive of computing processes executed to perform transactions. The ML system receives the annotated training data and data about a transaction operation of the system, trains a predictive model to generate prediction data that indicates a runtime condition of the system, and provides the prediction data to a process automation module of the system. The module executes process automation scripts to remediate the computing process, where the computing process is executed by the system to perform the real-time transaction operation.

Abstract: A device may receive data stored in one or more data sources associated with an organization based on utilizing one or more data discovery-related application programming interfaces (APIs) to access the data. The device may process, utilizing one or more data feature models via the one or more data discovery-related APIs, the data received from the one or more data sources to identify types of data included in the data based on a contextualization of the data. The one or more data feature models may identify a respective set of attributes expected to be included in the types of data. The device may perform multiple analyses of the data after identifying the types of data. The device may determine, based on a result of the multiple analyses, a score for the data. The device may perform one or more actions based on a respective result of the multiple analyses.

Abstract: A speech enabled product presentation engine for customer relationship management (‘CRM’) comprising: a product recognition module; the product recognition module comprising a module of automated computing machinery stored on one or a non-transitory computer-readable mediums and configured to: identify, in dependence upon digitized speech for recognition, a product articulated in the conversation between a tele-agent and a customer. The presentation engine also includes a product dimension module. comprising a module of automated computing machinery stored on one or a non-transitory computer-readable mediums and configured to: query an internal CRM, in dependence upon the identified product, and receive one or more descriptions of one or more dimensions of the product for presentation to the tele-agent during a call with the customer.

Abstract: The disclosure provides a non-opaque, abstract, unified query language that exposes the query as a first-class citizen of the underlying architecture. The present disclosure thus facilitates the creation of no-code or low-code applications by enabling a level of collaboration between components that may be difficult to achieve if the language employed were opaque to the architecture. The disclosed query language may be considered “SQL-like,” which may allow contributors familiar with structured query language (SQL) to effectively participate in the design of an application. The defined structures of a data objects of the non-opaque query language described herein are not-hidden and inspectable.

Abstract: An Artificial Intelligence (AI)-based data transformation system receives an input package and enables automatic execution of one or more processes in a robotic process automation system (RPA). The input package includes a plurality of documents and metadata required for the execution of the automated processes. The plurality of documents are categorized into a domain. Entities with their corresponding name-value pairs and entity relationships are extracted from the plurality of documents. An ontology is selected based on the domain. The entities are mapped to output fields identified from the selected ontology. The mappings thus generated are transmitted to the RPA system which employs the mappings to automatically execute the one or more processes.

Abstract: Complex computer system architectures are described for utilizing a knowledge data graph comprised of elements, and selecting a discovery element to replace an existing element of a formulation depicted in the knowledge data graph. The substitution process takes advantage of the knowledge data graph structure to improve the computing capabilities of a computing device executing a substitution calculation by translating the knowledge data graph into an embedding space, and determining a discovery element from within the embedding space.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity.