Abstract: A device receives documents from various sources, and processes the documents, with an optical character recognition engine, to generate digitized documents. The device processes the digitized documents, with a first machine learning model, to detect languages utilized in the digitized documents, and processes the digitized documents, in other languages that are different than a common language and with a second machine learning model, to translate the digitized documents, in the other languages, into the common language and to generate translated digitized documents. The device processes the translated digitized documents and untranslated digitized documents, with a classification model, to generate classified documents, and processes the classified documents, with a third machine learning model, to generate extracted information from the classified documents.

Abstract: An Artificial Intelligence (AI) based virtual automated assistance system provides services pertaining to component processes of a task that is to be automatically executed. The virtual automated assistance system includes a pipeline studio that enables generating the services. Historical data pertaining to a service is accessed for training and validating various ML models. The ML models are scored and a selected ML model is registered as a service on the virtual automated assistance system. The services thus registered are represented as process blocks within the pipeline studio wherein the process blocks pertaining to the component processes of the task are arranged in order to form a pipeline. The pipeline thus constructed enables automatic execution of the task by receiving and processing a request pertaining to the task via the services that form the pipeline.

Abstract: A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.

Abstract: Electronically received data is validated based on a digital data image that is scanned from a paper document. Known paper document source entities, paper document types and associated paper document configuration information are stored in a database. The paper documents are converted to digital data images and optically processed to identify respective source entity and document type information represented within the digital data images. Appropriate document configuration information is retrieved based on association with the detected type of document. Validation target data is extracted from the digital data images based on the configuration information and processed. The electronically received data is validated based on the extracted and processed validation target data.

Abstract: A device may determine one or more trends by using one or more machine learning techniques to process historical transactional information included in a set of historical transactional documents. The device may receive a set of transactional documents associated with transactions between a client organization and a vendor organization. The device may generate, based on the one or more trends, a first set of exceptions indicating that one or more transactional documents are problematic transactional documents. The device may generate, using a similarity analysis technique, a second set of exceptions indicating that one or more additional transactional documents are duplicate transactional documents. The device may generate a set of claims based on one or more exceptions. The device may perform, based on the set of claims, one or more actions associated with correction or prevention of transaction processing errors relating to the set of transactional documents.

Abstract: A device extracts data files from different data sources, and processes the data files to generate data from the data files. The device trains a first artificial intelligence model and a second artificial intelligence model based on historical data parameters, wherein the historical data parameters are associated with historical data previously processed by the device. The device utilizes the trained first artificial intelligence model and the data to generate data parameters for the data, and utilizes the trained second artificial intelligence model, the data, and the data parameters to generate a data structure configuration. The device generates a data structure for the data based on the data parameters and the data structure configuration, stores the data in the data structure, and provides a user interface for accessing the data structure.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconciliation data.

Abstract: Data extraction and automatic validation from digitized documents in non-editable formats is disclosed. Paper documents are digitized or converted into formats suitable for storage on computers or other digital devices. The digitized documents are classified into one of a plurality of document types and based on the document type, document processing rules are selected for analyzing the digitized documents to enable data extraction and automatic validation. The positions and values of the data fields in the digitized documents are obtained using machine learning techniques. The data field values are automatically validated and assigned confidence scores. Data fields with low confidence scores are flagged for manual review.

Abstract: A system for reconciling transactions includes an interface, a processor and non-transitory computer readable media. The interface receives information related to a plurality of organizations. Each of the plurality of organizations includes an enterprise resource planning (ERP) system for maintaining account information associated with the organization, and a document management system (DMS) for maintaining documents for the organization. The non-transitory computer readable media is in communication with the processor. The non-transitory computer readable media that stores instruction code that causes the processor to determine, based on the information, a group of organizations of the plurality of organizations that are structurally related to one another.

Abstract: A device may receive, from a user device, a request to access a virtual reality (VR) learning environment that includes an identifier associated with a program that supports the VR learning environment. The device may identify a set of objects to use within the VR learning environment by searching a data structure using the identifier associated with the program that supports the VR learning environment. The device may provide the VR learning environment to the user device. The device may receive, from the user device, information associated with interactions within the VR learning environment. The device may identify one or more additional objects to use within the VR learning environment by using one or more natural language processing techniques to analyze the information associated with the interactions within the VR learning environment. The device may provide the one or more additional objects to the user device.

Abstract: A system for orchestrating an operation is disclosed. The system includes an case orchestration engine to identify a discrepancy in the operation, and to generate a plurality of hypotheses for resolving the discrepancy. The case orchestration engine further collects evidence pertaining to the discrepancy in the operation, evaluates each of the plurality of hypotheses based on a dialogue-driven feedback received from a user, and selects one of the plurality of hypotheses for resolving the discrepancy based on the evidence and an expected outcome of the operation. The case orchestration engine provides reasons for the discrepancy along with remedial measures for resolving the discrepancy based on the selected hypothesis, and then generates a plan for performing the operation to achieve the expected outcome based on the remedial measures.

Abstract: Data extraction and automatic validation from digitized documents in non-editable formats is disclosed. Paper documents are digitized or converted into formats suitable for storage on computers or other digital devices. The digitized documents are classified into one of a plurality of document types and based on the document type, document processing rules are selected for analyzing the digitized documents to enable data extraction and automatic validation. The positions and values of the data fields in the digitized documents are obtained using machine learning techniques. The data field values are automatically validated and assigned confidence scores. Data fields with low confidence scores are flagged for manual review.

Abstract: Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Abstract: Electronically received data is validated based on a digital data image that is scanned from a paper document. Known paper document source entities, paper document types and associated paper document configuration information are stored in a database. The paper documents are converted to digital data images and optically processed to identify respective source entity and document type information represented within the digital data images. Appropriate document configuration information is retrieved based on association with the detected type of document. Validation target data is extracted from the digital data images based on the configuration information and processed. The electronically received data is validated based on the extracted and processed validation target data.

Abstract: A device may obtain a document, of a document type, from which specific information is to be captured. The specific information to be captured may depend on a document class associated with the document. The device may identify the document class, associated with the document, based on a characteristic of the document. The document class may identify a category of document in which the document is included, and may be associated with multiple document types. The device may determine field information associated with the document class. The field information may include information that identifies a portion of the document where the specific information is located, or may include information that identifies a manner in which the specific information can be identified within the document. The device may analyze the document, based on the field information, in order to capture the specific information. The device may provide the captured specific information.

Abstract: Implementations for consignment booking include actions of providing a user interface including a plurality of consignment data entry fields that include a pickup location entry field configured to receive data identifying a pickup location for a consignment, a delivery destination entry field configured to receive data identifying a delivery destination for the consignment, and a content entry field configured to receive data identifying a content of the consignment to be delivered, retrieving the data identifying the pickup location, the delivery destination and the content, transmitting a call signal to be transmitted to a mobile computing device of each agent in a plurality of agents, determining a respective location of each agent in the plurality of agents based on a response to each call signal, and selecting an agent from the plurality of agents based at least in part on the respective agent locations with respect to the pickup location.

Abstract: A device may obtain a set of documents, including at least two documents of different document types, for conversion to a uniform and an editable or a searchable format. The set of documents may include at least two documents of a different document type. The device may determine conversion information, associated with converting the set of documents, that includes an editable or a searchable document type to which the set of documents is to be converted. The device may determine zone information, associated with converting the set of documents, that includes information that identifies a portion of the set of documents that is to be converted. The device may generate a set of converted documents based on the conversion information and the zone information. Each document, of the set of converted documents, may be of the document type. The device may provide the set of converted documents.

Abstract: Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.

Abstract: A method may include receiving, from multiple, different sources, documents. The documents may be received in multiple, different file formats. The method may include performing operations on the documents to prepare the documents for processing, to obtain data included in the documents, or to determine information describing the documents. The method may include storing or providing, for storage, status information corresponding to the documents. The status information for a document may identify results of performing the operations with regard to the document. The method may include receiving an instruction that identifies an action to perform with regard to the document. The method may include performing the action. The method may include updating the status information to generate updated status information based on a result of performing the action. The method may include providing the updated status information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconciliation data.