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: In some possible implementations, an operations platform may include one or more memories; and one or more processors, communicatively coupled to the one or more memories, to: monitor a client platform without interacting with a user interface of the client platform; detect an issue associated with the client platform based on monitoring the client platform; determine whether the operations platform has access to a bot to resolve the issue; generate the bot when the operations platform does not have access to the bot; and deploy the bot with regard to the client platform to resolve the issue, wherein the bot is configured to resolve the issue without interacting with the user interface of the client platform.

Abstract: Implementations are directed to providing an electronic document, and include receiving text content including a plurality of segments, the text content being received from data sources, determining a set of topics to be included in the electronic document, for each topic in the set of topics, providing a set of contextual words associated with a respective topic, contextual words being determined from a lexical database, each contextual word having a respective frequency, determining a score for each segment and topic pair, the score indicating a relevance of a respective topic to a respective segment, each score being determined based on respective contextual words of the respective topic and frequencies of the respective contextual words, for each topic, providing, by the one or more processors, a summary including at least one segment based on respective score, and providing, to a user device, the electronic document including one or more summaries.

Abstract: A method for controlling access to one or more of a plurality of target systems includes receiving profile data that defines one or more features associated with a plurality of individuals with one or more entitlements of those individuals. Each entitlement is indicative of target system access. The method further includes generating a model that relates the one or more features and the one or more entitlements of the plurality of individuals. Profile data that defines one or more features associated with a target individual is received from a first user management system. A listing that includes one or more entitlements associated with the target individual, and confidence values associated with the one or more entitlements is generated based on the profile data and the model. Each confidence value is indicative of whether the target individual should be granted a corresponding entitlement.

Abstract: A device obtains an image and generates, based on the image and using an image generation model, a set of candidate images for object recognition. The device determines, based on the set of candidate images and using an image evaluation model, that one or more candidate images, of the set of candidate images, are processed during image generation and using the image generation model to improve a resolution of the image. The device determines image embeddings for the one or more candidate images and based on the image, and determines an embeddings error for the one or more candidate images based on the image embeddings and the image. The device determines an identification of a feature of the image based on the image embeddings and the embeddings error, and performs an action relating to the identification of the feature based on determining the identification of the feature.

Abstract: A device may receive information associated with first and second bug reports to be classified as duplicate or non-duplicate bug reports. The device may identify first and second descriptions associated with the first and second bug reports, respectively. The first and second descriptions may be different descriptions having a shared description type. The device may identify a neural network for encoding the first and second descriptions, based on the shared description type. The device may encode the first description into a first vector using the neural network, and may encode the second description into a second vector using the neural network. The device may classify the first and second bug reports as duplicate or non-duplicate bug reports based on the first vector and the second vector. The device may perform an action based on classifying the first and second bug reports as duplicate or non-duplicate bug reports.

Abstract: A device may include one or more processors. The device may receive first data to be processed. The device may receive information identifying a plurality of tasks to be performed to process the first data. The device may determine that one or more first tasks, of the plurality of tasks, are to be performed by an automation device, and that one or more second tasks, of the plurality of tasks, are to be performed by workers. The device may provide, to the automation device and to the workers, the first data. The device may receive, from the automation device and the workers, second data. The second data may be generated based on the automation device performing the one or more first tasks and based on the workers performing the one or more second tasks.

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: A device may identify a tool operating on a first device for integration into a lifecycle management platform operating on a second device. The tool may be associated with providing a functionality not included in the lifecycle management platform. The first device may be external to the second device. The device may determine a set of tool attributes for data events associated with the tool. The data events may include a data input, a data output, a new message, an updated message, a deleted message, or the like. The device may select a message format based on the set of tool attributes. The device may configure adaptation for a tool application programming interface (API) of the tool and a platform API of the lifecycle management platform based on the message format. The device may provide information associated with configuring adaptation for the tool API and the platform API.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for workforce planning.

Abstract: Implementations directed to identifying variables affecting a cost of healthcare include actions of receiving data from a plurality of data sources, the data relating to cost of providing healthcare, providing a data model based on the data, processing the data model using non-parametric analysis to provide a non-parametric result, the non-parametric result including a first and second variable, automatically processing the data model to correlate at least the first variable of the non-parametric result to the second variable of the non-parametric result to provide a correlation result, processing the correlation result using parametric analysis to provide a parametric result including at least one variable that affects the cost more than one or more other variables of a plurality of variables of the data model, and providing at least one data visualization for display, the at least one data visualization providing at least one graphical representation of the parametric result.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a conversation monitoring system.

Abstract: A disadvantage of existing betting operations is the implementation of fixed odds and pari-mutuel betting on specific and dedicated systems. This results in higher acquisition and maintenance costs as compared with a single system. According to the present invention, a single system allows both fixed odds and pari-mutuel betting to be offered on a single system wherein the system effects a computer-implemented method of wagering, including: receiving, via a communications network, a wager from a customer in respect of a wagered event and a wagered product; storing the wager, in an electronic data store, and associating the wager with the wagered event, the wagered product and a customer account, wherein the computer system manages the electronic data store, having stored therein, a plurality of events, a plurality of products and a plurality of customers, all of which are independently defined.

Abstract: A device may analyze a set of unstructured documents of an organization associated with a domain to identify a first set of entities. The device may analyze a set of semi-structured documents of the organization to determine a second set of entities. The device may filter the first set of entities using the second set of entities. Filtering the first set of entities may include removing, from the first set of entities, one or more entities that do not satisfy a threshold level of similarity with entities included in the second set of entities. The device may consolidate the filtered first set of entities and the second set of entities to identify a set of key entities. The device may provide the set of key entities to a user device to allow the set of key entities to be annotated and used for one or more machine learning models.

Abstract: Implementations directed to direct messaging between one or more robotic process automation (RPA) platforms, and an autonomic platform (AP), and include actions of receiving, by a platform-specific adapter of the AP, data from a RPA platform of a plurality of RPA platforms the AP interacts with, the data indicating occurrence of a scenario, providing, by an application programming interface (API) of the platform-specific adapter, an occurrence message responsive to the occurrence of the scenario, transmitting, by the API, the message to the AP through a first channel, and transmitting, by the platform-specific adapter, a set of messages to the AP through a second channel that is separate from the first channel, the set of messages communicating tagged data records.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, employing a permissioned distributed ledger for the promotion of sustainable agriculture. In one aspect, a method includes receiving, from a purchaser of goods, payment data including a targeted payment amount and a product identifier; locating, on a permissioned distributed ledger, an attestation for a delivery of the purchased goods, the permissioned distributed ledger stores verified transactions within a supply chain for the purchased goods; processing the attestation for a delivery from a producer of the purchased goods to determine the producer of the purchased goods; providing payment information including the targeted payment amount to the producer through a producer application; receiving verification, based on biometric authentication, of an identity of the producer from the producer application; and releasing funds for the targeted payment amount to the producer.

Abstract: A device may obtain, for a set of transactions, a set of transaction values associated with a particular industry. The device may determine one or more sample statistical distributions for a probabilistic transaction model by using one or more machine learning techniques. The one or more sample statistical distributions may be similar to one or more actual statistical distributions that are associated with the set of transaction values. The device may generate simulated transaction information using the probabilistic transaction model. The device may perform one or more actions after generating the simulated transaction information.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: A device may receive one or more data models that have been trained using a first set of values that are in a format capable of being processed by the one or more data models. The first set of values may be associated with a set of historical network performance indicators relating to a set of network devices. The device may receive network data that includes network ticket information and performance statistics for the one or more network devices. The device may determine a set of network performance indicators relating to the one or more network devices. The device may convert the set of network performance indicators into a second set of values that are in the format capable of being processed by the one or more data models. The device may use the second set of values to generate one or more recommendations associated with improving network performance.