Abstract: The invention provides a multi-tenant cloud-based system. The system includes a memory storing one or more processor-executable routines and a processor communicatively coupled to the memory. The processor is configured to execute one or more processor-executable routines to access customer data and machine learning (ML) computational models for a plurality of customers. The processor is further configured to train each of the ML computational models using the customer data. The processor is configured to define an async service having training parameters and one or more training routines for the ML computational models for each of the plurality of customers. The processor is configured to develop one or more nanoservices corresponding to each async service. Each of the one or more nanoservices comprises a customer-specific instance of the respective async service.

Abstract: A system for establishing a connection of a client system with an external system is presented. The system includes a snap-in subsystem configured to identify address details of the external system based on an authorization function, and transmit the address details to an authorization subsystem, and perform a handshake with the external system by providing an authorization code and a state of the client system, and receiving an access token, a refresh token and an expiration time from the external system.

Abstract: The present subject matter discloses techniques to identify linkable items and accordingly generating recommendations indicating such linkages. One or more work data objects, each linked with a corresponding work item, may be obtained. Further, one or more support data objects, each linked with a corresponding support item, may be obtained. The one or more work data objects and the one or more support data items may be encoded into work vectors and support vectors, respectively. A similarity score may then be estimated based on a quantitative comparative assessment of the work and support vectors. The similarity score may indicate a contextual similarity between each of the one or more work data objects and the one or more support data objects. Based on the similarity score, the one or more support items may be linked with the one or more work items.

Abstract: Techniques for notifying users of events for objects in a system of record are described. A request to generate the event is received. The event is indicative of a modification of an object. The event is recorded in a timeline table for a timeline of the object. Based on the modified object in the timeline table and a user table, one or more users may be identified. Further, the event is classified to determine a notification type of the one or more users from the user table. All interested users are selected from the one or more users and notified of the event. In an example, the system automatically subscribes users to receive notification of the occurrence of an event for the object. In an example, the one or more users may request to stop receiving the notification of the occurrence of the event for the object.

Abstract: A method and system for upgrading objects is provided. The approach comprises creating one or more fragments using an application programming interface (API); wherein each fragment comprises a fragment identifier and one or more fields. The approach further comprises generating an object by referencing the one or more fragments and storing a value associated with each field. During operations, the approach comprises upgrading the object upon encountering modified fragments wherein upgrading comprises updating the object from the first fragment to the latest. Further the approach comprises memorizing the upgrade instructions in a upgrade database.

Abstract: Techniques for managing entity activity in interactive platforms through processing of session replay files are disclosed. The system receives an assessment request for an event, extracts an electronic page and a snippet surrounding an interacted element, and generates an annotated screenshot. A computational model analyses the annotated screenshot and the snippet to produce instrumentation data, including event attributes and identifiers. The instrumentation data is stored for future use, enabling efficient processing of similar events in subsequent sessions. The system employs validation mechanisms to ensure accurate event identification and can re-execute the model to generate modified identifiers, if needed. The system increases the efficiency and accuracy of session replay analysis, reducing manual effort and enabling more comprehensive understanding of user journeys. The present subject matter offers a scalable solution for processing large volumes of session replay data in web applications.

Abstract: Techniques for collaborative management of data transformation processes are disclosed. The techniques include triggering data transformation upon receiving extracted data from an external source, retrieving a recipe blueprint representing the transformation, and associating an active blueprint with the transformation. Multiple entities can collaboratively modify the blueprint. Modifications are recorded in a draft blueprint, which is compared to the active blueprint to determine customization differences. A remigration report is generated indicating changes if the draft becomes active. The draft blueprint transitions to active and is processed by applying customization differences. Ongoing synchronizations are performed using the transitioned active blueprint, transferring only changes and new data. The techniques allow flexible updates to transformation rules without disrupting operations, facilitate collaborative configuration, and increase efficiency through incremental synchronization approaches.

Abstract: Techniques for identification and recommendation of resources are provided. A resource interaction record is obtained based on an entity profile descriptor indicating attributes linked with an entity. Resource identifiers from this record are used to transmit interaction signals to accessed resources, for receiving response payloads in return. The operational status of each resource is determined from these payloads. Based on the operational status, a payload evaluation workflow may be triggered, executing a computational model to evaluate contextual similarity between response payloads and resource information assets linked to candidate resources. The model identifies candidate resources with logical relationships to resources accessed by the entity. A recommendation generation signal is then generated for indicating the candidate resources identified for the entity.

Abstract: Techniques for managing entity activity across interactive portals are disclosed. The techniques include assigning a unique identifier to a request for accessing an interactive portal, capturing events and recording attributes indicating contextual data about interactions. An entity identifier is associated with the unique identifier upon registration. The attributes and data linked to the entity identifier are then analyzed to generate metrics and to create a representation of the entity's interactions and engagement patterns. The techniques enable tracking activity across multiple devices by associating multiple unique identifiers with the same user identifier. It performs retroactive deanonymization to determine complete historical entity journeys. The comprehensive approach to entity activity management provides valuable insights into user behavior, enables personalized experiences, and supports data-driven decision making for organizations across various digital platforms.

Abstract: Techniques for collaborative management of data transformation processes are disclosed. The techniques include triggering data transformation upon receiving extracted data from an external source, retrieving a recipe blueprint representing the transformation, and associating an active blueprint with the transformation. Multiple entities can collaboratively modify the blueprint. Modifications are recorded in a draft blueprint, which is compared to the active blueprint to determine customization differences. A remigration report is generated indicating changes if the draft becomes active. The draft blueprint transitions to active and is processed by applying customization differences. Ongoing synchronizations are performed using the transitioned active blueprint, transferring only changes and new data. The techniques allow flexible updates to transformation rules without disrupting operations, facilitate collaborative configuration, and increase efficiency through incremental synchronization approaches.

Abstract: The invention provides a guided interactive user-support system for a platform is provided. The system includes a user interface that is configured to receive a user query corresponding to a desired task to be performed on the platform. The system further includes an artificial intelligence (AI) driven module that is configured to provide guided support to the user for the desired task. The AI-driven module is further configured to analyze the user query and identify one or more actions corresponding to the user query. The AI-driven module is further configured to generate one or more videos and/or texts based on the identified actions to address the user query. The AI-driven module is further configured to receive feedback from the user on the generated videos and/or texts via the user interface. The AI-driven module is further configured to generate modified videos and/or texts based on the received feedback.

Abstract: Disclosed is an artificial intelligence (AI)-driven user support system for a platform. The system includes at least one user interface configured to facilitate user interaction of a user with the system. The user interface is further configured to receive user inputs corresponding to a desired task to be performed on the platform. The system also includes an AI-driven support module configured to provide support to the user in response to the user inputs. The system also includes an AI training module configured to track real-time user interaction data of the user with the AI-driven support module. The AI training module is also configured to track the analytics and application usage data of the platform. The AI training module is also configured to facilitate real-time training of the AI-driven module based upon the tracked user interaction data and analytics and application usage data to provide multi-modal support to the user.

Abstract: Techniques are disclosed to generate relevant responses for requests received in a connected environment. A user communicating with the connected environment may request, in natural language, an operation to be performed. The request may be augmented with the information related to the requesting user to generate an augmented query. The augmented query may be processed by a large language model to generate a response to the augmented query. The response is returned in natural language.

Abstract: Techniques for vector database indexing are described. In an example, a plurality of unstructured data is obtained. Vector embeddings are performed on each unstructured data from amongst the plurality of unstructured data to generate a plurality of vectors. Each vector from amongst the plurality of vectors is indicative of attributes of the unstructured data from amongst the plurality of unstructured data. The plurality of unstructured data and the plurality of vectors is stored in the vector database. A vector index corresponding to the plurality of vectors based on a first indexing mechanism is generated where the vector index includes at least one cluster of vectors with similar attributes. An index mapping is performed to store the vector index into a relational database.

Abstract: Techniques for orchestrating field-level access control in distributed microservices environments are disclosed. An interceptor module embedded within each microservice intercepts incoming requests before they reach the application logic. The interceptor identifies the requesting actor, retrieves relevant field-level access control policies, and evaluates them to determine granular field-level permissions. Requests are only authorized to proceed if the actor has appropriate field-level access. The interceptor also filters outgoing responses to remove any fields the actor lacks permission to view. The system utilizes a distributed cache to store policies and permissions, improving performance. Policies can include tenant identifiers, caveats expressed in a domain-specific language, target resource types, and granular field privileges.

Abstract: A clustering system for asynchronously clustering work items is provided. The clustering system comprises a user interface configured to enable a user to define a schedule. The clustering system further includes a clustering module communicatively coupled to the user interface and configured to retrieve a plurality of work items based on the schedule, retrieve a plurality of embeddings corresponding to the plurality of work items and asynchronously generate a first hierarchy of clusters by clustering embeddings based on the defined schedule.

Abstract: The present subject matter discloses techniques for managing resources for microservices. The system includes a Resource Configuration-enabling Framework (RCeF) with interactive input receptors for selecting resources and providing microservice information. Upon receiving a resource orchestration request, the system generates a configuration initiation signal. For each selected resource, a logical schema with dynamically modifiable portions is retrieved and updated based on provided attributes. A configuration file is then built using the updated logical schemas. The system may create a new data repository instance for the configuration file or modify an existing one. The configuration file is executable by a resource orchestration framework to commission resources for the microservice. This approach streamlines resource management for microservices, reducing manual intervention, minimizing errors, and enhancing flexibility.

Abstract: The invention provides a system for automating business process workflows. The system includes a user interface that is configured to receive natural language inputs corresponding to a business process from a user. The system further includes a large language model (LLM) based module that is configured to generate a generic automation structure for the business process in response to the received natural language inputs. The automation structure comprises a plurality of atomic operations. The system further includes an automation generation module that is configured to identify events and actions corresponding to each atomic operation of the automation structure using a pre-determined catalogue of available events and actions and to generate a deterministic workflow using the identified events and actions.

Abstract: Techniques for end-to-end testing of data migration from source platforms to a destination platform are described. A plurality of test data objects is generated based on a plurality of configuration files that defines rules by which the plurality of test data objects is generated. A predefined operation is applied to each of the plurality of test data objects to simulate data manipulation in a source platform. The first plurality of test data objects is mapped to corresponding data objects in the source platform and in a destination platform. The expected data for validation is computed. The expected data for validation includes expected data to be extracted from the source platform and the destination platform upon the data migration from the source platform to the destination platform. The data migration is validated by comparing data extracted from the first source platform and the destination platform against the computed data for validation.

Abstract: Techniques for object matching and ontology alignment for data migration between different domain models are described. A plurality of objects from a plurality of source domains is received. Schema of the plurality of objects from the plurality of source domains corresponds to the plurality of source domain models. The schema of objects of the destination domain corresponds to a destination domain model. The plurality of source domain models is different from the destination domain model. The plurality of objects is converted into a predetermined data representation format. The converted plurality of objects is input to a machine learning model. The converted plurality of objects is transformed, using the machine learning model, to align with a destination domain model irrespective of quantity of object, object type, object attributes. The transformed plurality of objects is matched with a plurality of objects corresponding to the destination domain using the machine learning model.