Abstract: This disclosure relates to a method and a system for managing anonymized data across computing devices in a blockchain network. The method includes generating the blockchain network including a blockchain ledger based on a dataset associated with each of a plurality of end users. The method of generating the blockchain network includes constructing the blockchain ledger corresponding to the dataset associated with each of the plurality of end users; generating a unique ledger key corresponding to the dataset associated with each of the plurality of end users; and storing the dataset associated with each of the plurality of end users in the blockchain ledger based on the unique ledger key. The method further includes communicating a joining request to a second computing device for joining the blockchain network. The method further includes granting access to the second computing device to the dataset associated with the plurality of end users.

Abstract: This disclosure relates to method and system for generation and presentation of User Experience (UX) recommendations. The method includes retrieving data corresponding to one or more users of an application from a database. The data includes one or more of user device screen recording data, application usage data, or user feedback data. The method further includes determining a set of UX parameter values and a set of UX recommendations based on the data using a first Artificial Intelligence (AI) model; generating a set of User Interface (UI) screen templates based on one or more of the set of UX parameter values, the set of UX recommendations, and the data, using a second AI model; and displaying a report through a Graphical User Interface (GUI) on a display of an administrator device.

Abstract: This disclosure relates to method and system for calculation of test automation feasibility indices. The method includes receiving input data comprising user responses to a questionnaire associated with a test case. The questionnaire includes a first set of questions associated with an ease of test case automation, and a second set of questions associated with an impact of test case automation, and each of the plurality of questions comprises a corresponding weightage. The method further includes computing an ease of automation index based on the user responses and a first set of weightages corresponding to the first set of questions, and an impact of automation index based on the user responses and a second set of weightages corresponding to the second set of questions.

Abstract: This disclosure relates to method and system for determination of personality traits of agents in a contact center. The method includes retrieving textual data corresponding to a conversation between a first agent and a first customer. The method further includes generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model. The natural language justification may include one or more sentences. The one or more sentences may include a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits. The method further includes determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.

Abstract: A method for maintaining ownership integrity of templated executable graph-based models is provided. A node template that comprises a predetermined node structure and rules governing generation of node instances is obtained. A bounded executable run-time node is generated. The bounded executable run-time node comprises the node template, a node instance, and an overlay structure. The overlay structure comprises a run-time overlay and an ownership overlay. The run-time overlay comprises an overlay template and an overlay instance that includes processing logic for interaction with the node template and/or the node instance during execution. The overlay ownership overlay defines an ownership rule associated with the bounded executable run-time node. A stimulus and an associated context are received and, in response to the stimulus being received, execution of the processing logic of the run-time overlay is caused in accordance with the ownership rule of the ownership overlay.

Abstract: An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various executable nodes that communicate with each other by way of messages. The executable graph-based model further includes a message node for each message associated with the overlay system. Each message node is associated with one or more time-series analytics overlay nodes that execute a corresponding set of time-series computation functions on the corresponding message node. A publisher overlay node associated with each time-series analytics overlay node may generate and publish a statistical insight based on an output of each corresponding time-series computation function. The processing circuitry may use the statistical insight to generate an analytics outcome.

Abstract: This disclosure relates to a method and a system for managing extensions of a product. The method includes determining a set of extensions associated with the product. The set of extensions includes a set of existing extensions and a set of potential extensions. The method further includes receiving a user selection corresponding to an extension from the set of extensions. The method further includes comparing the extension with the set of existing extensions. The method further includes generating a boilerplate code assembly corresponding to the extension in response to comparing. The boilerplate code assembly is generated based on a definition language template, and a business logic.

Abstract: An executable inference hypergraph representing a rule-based model, the executable inference hypergraph comprising a first hyperedge associated with a first inference rule of the rule-based model and encapsulating a plurality of value nodes storing a plurality of values such that the plurality of values form a part of a set of terms used to evaluate the first inference rule, wherein the plurality of value nodes include at least one of the one or more value nodes of a graph-based model. The executable inference hypergraph further comprising a rule overlay node coupled to the first hyperedge thereby forming a first executable inference rule, wherein the rule overlay node comprises processing logic operable to evaluate the first inference rule using the set of terms encapsulated by the first hyperedge. The executable inference hypergraph is executed to determine an inference outcome.

Abstract: A graph-based model comprises a plurality of entity nodes indicative of a plurality of entities within a dataset and a hierarchical structure of nodes. The hierarchical structure of nodes include a plurality of data nodes indicative of a plurality of data values associated with the plurality of entities and a plurality of context nodes coupled between the plurality of entity nodes and the plurality of data nodes. The plurality of context nodes define contextual relationships between the plurality of entity nodes and the plurality of data nodes. A query comprising a query value is received and a node within the hierarchical structure of nodes is identified based on the query value. A traversal path is determined from the node to a first entity node related to the node and a response to the query is generated based on the traversal path.

Abstract: An overlay system provided, includes processing circuitry and storage circuitry that stores primary executable graph-based models and auxiliary executable graph-based models. Each primary executable graph-based model is mapped to one or more auxiliary executable graph-based models based on various rules. The processing circuitry receives a stimulus associated with the overlay system and identifies, based on the stimulus, a primary executable graph-based model and one or more rules. Further, one or more values associated with an auxiliary executable graph-based model that is mapped to the primary executable graph-based model are retrieved based on the one or more rules. One or more values associated with the primary executable graph-based model are populated based on the retrieved one or more values. The processing circuitry further executes an operation associated with the stimulus based on the primary executable graph-based model that is populated with the one or more values.

Abstract: An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes node templates, node instances, and index nodes. Each node template is associated with one or more node instances and one or more index nodes. Further, each index node includes index records. The processing circuitry receives a contextualized stimulus associated with the overlay system and identifies one or more index records that include an index value indicated by the contextualized stimulus. Based on the identified one or more index records, the processing circuitry further identifies one or more node instances required for stimulus processing. Further, the processing circuitry executes an operation associated with the stimulus based on the identified one or more node instances and each node template associated with the identified one or more node instances.

Abstract: An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The storage element stores an executable graph-based model including a plurality of nodes, a plurality of rule overlay nodes, and a plurality of data analysis overlay nodes. The processing circuitry receives a stimulus indicative of a data analysis operation and identifies a first node, a rule overlay node associated with the first node, and a data analysis overlay node associated with the first node. The processing circuitry executes a set of rules associated with the rule overlay node on a composition of the first node to generate a set of outputs. The data analysis overlay node uses the set of outputs to determine whether a data analysis score associated with the first node exceeds a data analysis score threshold.

Abstract: An overlay system is provided that includes a plurality of storage elements and processing circuitry coupled thereto. The plurality of storage elements store a plurality of executable graph-based models such that first and second storage elements store first and second executable graph-based models, respectively. Each executable graph-based model includes a plurality of nodes. The processing circuitry receives a stimulus to share a first node of the first executable graph-based model with a second node of the second executable graph-based model. The processing circuitry instantiates a tenant overlay node that is associated with the second node and includes a set of constraints to be adhered to by the second node while sharing the first node. The processing circuitry creates a sharing channel as a medium between the first and second storage elements. The sharing channel and the tenant overlay node enable sharing of the first node with the second node.

Abstract: An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various active nodes, various history nodes, and various history overlay nodes. Each active node is associated with one or more history nodes and one or more history overlay nodes. The one or more history overlay nodes facilitate the generation and maintenance of one or more history nodes (e.g., one or more historical versions of the corresponding active node). The processing circuitry receives a contextualized stimulus associated with the overlay system and identifies an active node and one or more associated history nodes, in the executable graph-based model based on the context. The processing circuitry further executes an operation associated with the stimulus based on the identified active node, the associated one or more history overlay nodes, and the identified one or more history nodes.

Abstract: An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various executable nodes that communicate with each other by way of messages. The executable graph-based model further includes a message node for each message associated with the overlay system. Each message node is associated with one or more analytics overlay nodes that execute a corresponding set of analytics operations on composition of the corresponding message node to generate one or more analytic insights. The composition includes data and transactional information associated with the corresponding message node. A publisher overlay node associated with the one or more analytics overlay nodes may generate and publish an analytics outcome based on an output of the execution of the set of analytics operations. The analytics outcome is indicative of performance of the overlay system.

Abstract: An overlay system including a storage element and processing circuitry is provided. The storage element stores an executable graph-based model that includes various active nodes and various micro-overlay nodes. Further, each active node includes multiple node elements, where each micro-overlay node is associated with at least one node element, and extends the functionality of the corresponding node element. The processing circuitry receives a contextualized stimulus. Further, the processing circuitry identifies an active node, node elements of the active node associated with the processing of the contextualized stimulus, and one or more micro-overlay nodes associated with each identified node element. The processing circuitry executes an operation associated with the contextualized stimulus based on the identified node elements and one or more micro-overlay nodes associated with each identified node element.

Abstract: An overlay system including processing circuitry and storage element that stores various base nodes and various security overlay nodes, is provided. Each base node is associated with one or more security overlay nodes that control access to the information value contained in the base node. The processing circuitry receives a contextualized stimulus that indicates a requirement for associating two or more nodes. The processing circuitry identifies two base nodes required for processing the contextualized stimulus and executes an operation on the two identified base nodes to create an aggregated node. Thus, the aggregated node contains a higher information value than the two identified base nodes. Consequently, the processing circuitry dynamically associates, with the aggregated node, one or more security overlay nodes that have an equal or higher security level than the security levels of security overlay nodes associated with the two identified base nodes.

Abstract: An overlay system is provided that includes processing circuitry and a storage element that stores an executable graph-based model including various active nodes, various history message nodes, and various history overlay nodes. Each active node is associated with one or more history overlay nodes that facilitate the creation and maintenance of one or more history message nodes associated therewith. The processing circuitry receives a contextualized stimulus and identifies an active node and one or more history message nodes in the executable graph-based model based on the context. The processing circuitry creates one or more history nodes (e.g., one or more historical versions of the corresponding active node) based on the one or more history message nodes. Further, the processing circuitry executes an operation associated with the stimulus based on the identified active node, the one or more history overlay nodes, and the created one or more history nodes.

Abstract: An overlay system is provided that includes a primary storage element, a plurality of auxiliary storage elements, and processing circuitry. The primary storage element stores an executable graph-based model having a plurality of nodes. The processing circuitry receives a first stimulus indicative of a data splintering instruction. Based on the first stimulus, the processing circuitry identifies a first node in the executable graph-based model and executes a data splintering operation on the first node to divide the first node into a plurality of splinters. The processing circuitry, based on the first stimulus, instantiates a plurality of location overlay nodes and associates the plurality of location overlay nodes with the plurality of splinters. Based on the association, the processing circuitry stores each splinter of the plurality of splinters in an auxiliary storage element indicated by a corresponding location overlay node.

Abstract: An artificial neural network (ANN) modelled as an overlay hypergraph comprising a plurality of hyperedges, a plurality of role nodes, and one or more overlay nodes. A hyperedge of the plurality of hyperedges represents an artificial neuron within the ANN and comprises a set of role nodes each of which representing a portion of a connective relationship within the ANN. A role node of the plurality of role nodes represents a connection between layers of the ANN and comprises a first connective relationship associated with a first hyperedge and a second connective relationship associated with a second hyperedge such that the role node functionally connects the first hyperedge and the second hyperedge. The one or more overlay nodes comprise processing logic operable to interact with at least one hyperedge or at least one role node coupled to the one or more overlay nodes.