Abstract: Implementations for mitigating redundant commands issued by an autonomic platform (AP) to a robotic process automation (RPA) platform can include receiving user input indicating user selection of a resource and a process from a user interface of the AP, adding a resource/process pair to a command table of the AP, the resource/process pair representing the resource and the process selected from the user interface, determining that a redundant resource/process pair is included in the command table, the redundant resource/process pair being duplicative of the resource/process pair, and, in response, deleting the redundant resource/process pair, and transmitting one or more requests for respective commands of the command table to a platform-specific adapter, at least one command including a command to execute the process using the resource within the RPA platform.

Abstract: A device may receive an image stream of an environment of an electronic display. The device may detect that a target is in the environment, wherein the target is detected based on the image stream including an image associated with the target. The device may identify that a first object is associated with the target, wherein the first object is identified based on the object recognition model being trained to recognize the first object. The device may determine a characteristic of the first object and may determine that a second object is in the environment. The device may generate content that is to be displayed via the electronic display, wherein the content is generated based on the characteristic of the first object and a characteristic of the second object. The device may cause the content to be displayed via the electronic display to permit the target to access the content.

Abstract: Implementations include providing a baseline multi-dimensional model of a cultivar, determining an encoding based on the baseline multi-dimensional model, and a target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar after a period of time, selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact, and providing the set of actions as output for executing on the cultivar.

Abstract: A centralized data reconciliation system processes at least two data streams transmitting data related to one of a plurality of processes and executes a data reconciliation procedure. Unmatched data records identified during the data reconciliation procedure are further categorized into categorized records based on various reason categories and irreconcilable records which could not be categorized into the reason categories. The irreconcilable records are flagged for user input. The user input is recorded to further train the data reconciliation system. The at least two data streams are initially converted into self-describing data streams from which the entities and entity attributes are extracted using the data models received from the data streams. The data records from the first and second self-describing data streams are mapped. The matched pairs and unmatched pairs are selected from the mappings based on respective confidence scores that are estimated in accordance with the rules of data reconciliation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated testing script generation system are disclosed. In one aspect, a method includes the actions of receiving a context file, a test scenario, and a selected automation tool selected through a user interface, the context file including an object map comprising objects that correlate to respective components of a display page, the test scenario describing a test case for the application involving an intended interaction with a component on the display page, correlating the intended interaction with the component with the corresponding object in the object map, processing the intended interaction and the corresponding object through an Artificial Intelligence (AI) model, determining a script template based, applying the script template to generate an automated testing script for the selected automating tool, and executing the automated testing script to test the function of the display page.

Abstract: In some examples, incident prediction and prevention may include ascertaining a plurality of past incidents, clustering the plurality of past incidents to generate a plurality of incidents clusters, and identifying, for each past incident of the plurality of past incidents that is in a respective incidents cluster of the plurality of incidents clusters, a time of occurrence. Incident prediction and prevention may include ascertaining a new incident, assigning the new incident to an incidents cluster of the plurality of incidents clusters, and determining, for the assigned incidents cluster, at least one further predicted incident associated with at least one further corresponding incidents cluster. Further, incident prediction and prevention may include determining a resolution to the at least one further predicted incident, and preventing occurrence of the at least one further predicted incident by executing the determined resolution to the at least one further predicted incident.

Abstract: According to examples, multiplatform based experience generation may include determining a set of connected devices that provide services related to virtual reality, and determining a platform type of each of the connected devices. Multiplatform based experience generation may further include receiving an indication of a modification to an intrinsic property of a virtual object on one of the set of connected devices, and generating, based on the received indication of the modification to the intrinsic property of the virtual object, a modification to a master version of the virtual object. Based on the platform type of another one of the set of connected devices and based on the modification to the master version of the virtual object, an indication of a modification to an intrinsic property of a virtual object on the another one of the set of connected devices may be generated.

Abstract: According to an example, intent and bot based query guidance may include receiving a query associated with a domain, and identifying, based on an analysis of the query, an intent of the query by extracting an action associated with the query and an entity associated with the query. An intent model associated with the query may be generated based on a mapping of the action and the entity with a domain model of the domain. An intent domain specific language representation of the intent model associated with the query may be generated. Based on an analysis of the intent domain specific language representation, a plurality of bots may be identified, and a parameterized bot chain may be generated to respond to the query. A runtime binding of bots of the parameterized bot chain may be performed, and invoked to generate the response to the query.

Abstract: A device may receive information related to connections between two or more attendees of an event or subsequent interactions between the two or more attendees. The device may process the information using a set of techniques to permit an analysis of the connections and the subsequent interactions. The device may determine a score related to the event based on a result of analyzing the connections and the subsequent interactions. The score may indicate a level of success of the event with respect to the connections or the subsequent interactions. The device may perform an action based on the score.

Abstract: Implementations are directed to methods, systems, apparatus, and computer programs for generation of a three-dimensional (3D) animation by receiving a user input defining a two-dimensional (2D) representation of a plurality of elements, processing, by the one or more processors, the 2D representation to classify the plurality of elements in symbolic elements and action elements, generating, by the one or more processors, based on the symbolic elements, the action elements, and a set of rules a 3D animation corresponding to the 2D representation, and transmitting, by the one or more processors, the 3D animation to an extended reality device for display.

Abstract: Examples of an intelligent data quality application are defined. In an example, the system receives a data quality requirement from a user. The system obtains target data from a plurality of data sources. The system implements an artificial intelligence component sort the target data into a data cascade. The data cascade may include a plurality of attributes associated with the data quality requirement. The system may evaluate the data cascade to identify a data pattern model for each of the attributes. The system may implement a first cognitive learning operation to determine a mapping context from the data cascade and a conversion rule from the data pattern model. The system may establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation. The system may generate a data cleansing result corresponding to the data quality requirement.

Abstract: An Artificial Intelligence (AI)-based automated process is monitored via a process monitoring system that identifies components used in the execution of the sub-processes of the automated process. Various metrics are selected for collection prior to or during the execution of the AI-based automated process. The values of the metrics are collected as step outputs corresponding to the sub-processes. A final output generated upon the execution of the automated process is also collected. The step outputs can be used to determine the reason why the automated process produced a certain final output.

Abstract: An unmanned vehicle (UV) system provides network data connectivity for terminals. UV nodes in the system may transmit and receive terminal data in a network of UV nodes and other nodes according to a routing protocol. The nodes may include fixed nodes or UV nodes with a wired or wireless network connection to a destination terminal. A UV node may receive data from a source terminal for transmission to the destination terminal, and when the UV node is within range of a node in the network connected the destination terminal, the UV node transmits the received data to the node within range for forwarding to the destination terminal via the network.

Abstract: Examples of the disclosure project resource demand. In some examples, a first factor that corresponds to a task and/or a resource is identified. The first factor is associated with one or more scenarios. A model associated with the scenarios is generated. The model includes the first factor. The model is used to generate a projection associated with an expected resource demand for a first scenario. The projection is compared with historical data associated with an actual resource demand for the first scenario to determine a variance between the projection and the historical data. Based on the variance, a second factor that does not directly correspond to the task and/or the resource is generated. The second factor is configured to increase an accuracy of the model. Aspects of the disclosure enable the model to be generated, maintained, and/or updated in a calculated and systematic manner for increased performance.

Abstract: Examples of medical concierge are provided. In an example, an claim may be received. The claim may include data relating to service provided, by a provider, to multiple patients. The claim may be parsed to determine the provider, the multiple patients and the service provided. Additional information may then be fetched. The additional information may include one of a number of claims filed in the past, status of each claim, number of appeals filed, status of the appeals, and complaints registered by the provider. Thereafter, the claim and the additional information may be analyzed and a category may be determined for the provider. The category may be determined based on a behaviour model that may be computed based on the claim and the additional information. The category may be indicative of an issue in behaviour of the provider.

Abstract: A device may receive information regarding one or more client devices being utilized at a connected learning center. The device may receive information regarding one or more training courses, one or more users of the one or more client devices, and the one or more users. The user interface may provide information regarding a particular training course, of the one or more training courses, that a particular user is attending. The particular user may be determined to be attending the particular training course based on the information regarding the one or more client devices, obtained by a particular sensor of a plurality of sensors, indicating that a particular client device is located within a classroom of the connected learning center in which the particular training course is being provided. The device may provide the user interface for display.

Abstract: A system includes a holographic display that may create multi-dimensional images. The holographic display may be coupled to processing circuitry and display controller circuitry that may generate a multi-dimensional interface. The multi-dimensional interface may accept input from human interface devices that may translate human motion in multi-dimensional space into the multi-dimensional space of the multi-dimensional interface. The multi-dimensional interface may be generated by a holographic interface stack executing on the processing circuitry.

Abstract: A device may receive information that identifies code associated with an application. The device may generate a technical description of the code based on the information that identifies the code associated with the application. The device may generate a set of rules based on the technical description of the code. The set of rules being associated with a semantics of business vocabulary and business rules (SBVR) format. The device may generate a set of microservices based on the set of rules. The device may provide information that identifies the set of microservices to cause an action to be performed.

Abstract: A device obtains a machine readable document, and extracts plain text information from the machine readable document. The device extracts visual structural information from the machine readable document, and processes the plain text information with an artificial intelligence model to generate artificial intelligence-generated annotations. The device aligns the artificial intelligence-generated annotations and the visual structural information to generate a structured document, and provides the structured document for display.

Abstract: Implementations directed to providing a computer-implemented method for automating analytical model building for code snippets, the method comprising receiving software instructions indicating functionality of a software component, analyzing the software instructions through natural language processing to identify tags to characterize the software instructions and the functionality, performing a search, based on the identified tags, of relevant Internet sites to identify code snippets that address the functionality, scoring one or more of the identified code snippets based on a respective relevance to the functionality, based on the scoring of the one or more of the identified code snippets, determining that at least one of the code snippets has a score that meets a relevance threshold, and providing the at least one of the code snippets based on the determination that the at least one of the code snippets has the score that meets the relevance threshold.