AUTOMATION ANYWHERE, INC.

Described herein are methods, systems, and media for supporting multi-language user sessions and fulfillments comprising: maintaining a repository of fulfillment objects each comprising a language and a region; establishing a user session with a user; determining a user region for the user in association with establishing the user session; identifying one or more fulfillment objects in the repository available for the user region; processing the user session, the user session comprising one or more user requests; applying a language detection model to each request to determine a user request spoken language; applying an understanding module to each request to recommend one or more of the fulfillment objects matching the region for the user; and rendering a response to each request to the user, utilizing the one or more of the fulfillment objects matching the region for the user, in the user request spoken language.

Described herein are methods, systems, and media for deriving operational insights about customer infrastructure comprising (a) obtaining customer incident data reported by one or more customers, wherein the customer incident data is pre-encoded using one or more encoding techniques that are specific or unique to the one or more customers; and (b) using a decoder module to directly process the customer incident data to generate an output, wherein the output comprises a plurality of configuration item(s) (CIs) discovered, derived or extracted from the customer incident data.

Embodiments disclosed herein concern improved techniques for recognition of tabularized data within an image and construction of a computer-readable electronic table representing the recognized tabularized data. The image can be a screen image produced by a software application program operating on a computing device. The tabular data, such as pertaining to a table, can be recognized by recognizing rows, columns, text, controls and lines.

Systems and methods for locating target user interface (UI) elements that automation programs intend to interact when performing automation programs. The automation programs can, for example, operate to perform actions as part of a workflow process to complete tasks. Advantageously, when UI elements referenced in automation programs cannot be located based on information recorded about the UI elements during the phase of designing the automation programs. One approach can involve creating candidate fallback element locators (e.g., XPaths) for UI elements during the automation program design phase. If an automation program fails during playback, one or more of these candidate fallback element locators can be used to locate a target UI element. In another approach, when an automation program playback fails, portions of a software application's user interface code can be identified and used as inputs to machine learning model(s), which can generate candidate fallback element locators that can used to locate target UI elements.

Robotic process automation (RPA) systems with improved playback capabilities. Certain embodiments can provide resilient playback of software automation processes by providing the capability to record, compute and store parameters for user interface controls detected from a screen image of a user interface. These parameters can be used to assist in locating correct corresponding user interface controls within a screen image presented at playback of a software automation process. Other embodiments can, additionally or alternatively, provide resilient playback of software automation processes by providing enhanced capability to locate user interface controls within a screen image of a user interface. In some embodiments, one or more of the user interface controls located within the screen image of the user interface can be used to manipulate the user interface so that other user interface controls become visible within the screen image. Advantageously, embodiments disclosed herein allow software automation processes to operate with greater reliability and flexibility.

Systems and methods for producing programs and processes for performing tasks based on event data contained within event logs are disclosed. The systems and methods can involve processing event data such that relevant portions of the event data are provided to machine learning models to allow such systems to produce automation programs. The systems and methods can also involve providing instructions to a command package machine learning model, such as a large language model, that operates to propose command packages that contain automation actions, and providing instructions to an action machine learning model that selects automation actions, associated with the proposed command packages, to be incorporated within automation programs that can be used to automate the tasks.

A system, method or computer readable medium for generating next suggested actions using generative Artificial Intelligence (AI). The system, method or computer readable medium can leverage the power of artificial intelligence algorithms to generate personalized, context-aware suggestions for user actions based on input data. By employing generative AI models, the system, method or computer readable medium can effectively anticipate user needs and provide intelligent recommendations to enhance user experiences across various domains.

Systems and methods for producing automation programs that are suitable for performing business and personal tasks using software application programs. The methods and systems involve can identify or receive a user request for the production of an automation program and then utilizing one or more machine learning models, where each of the machine learning models can produce an aspect of the requested automation program. Each of the machine learning models are provided with inputs such as a specific user's request for an automation program to automate tasks, the definition of a role that the model should take on, domain knowledge specific to an aspect of the automation program being requested, and functional instructions for each of the machine learning models to produce a desired output. The outputs of each of the machine learning models can be combined to form the user-requested automation program.

A robotic process automation (RPA) system provides bots that interact with and provide user credentials to applications, such as for multi-factor authentication (MFA). First user credentials associated with MFA are retrieved by the bots from credential storage. Second user credentials that correspond to questions posed to a user of an application are retrieved from credential storage. Second user credentials that correspond to a one-time password are generated by the RPA system. The second user credentials may also be generated by a third-party authentication service that provides the credentials via a secondary channel such as email or SMS, which are then retrieved for presentation to the application.

Improved techniques to access content from documents in an automated fashion. The improved techniques permit content within documents to be retrieved and then used by computer systems operating various software programs (e.g., application programs), such as an extraction program. Documents, especially business transaction documents, often have various descriptors (or tables) and values that form key-value pairs. The improved techniques permit key-value pairs within documents to be recognized and extracted from documents. Consequently, RPA systems are able to accurately understand the content of tables within documents so that users and/or software robots can operate on the documents with increased reliability and flexibility.