Abstract: This disclosure relates to system and method for optical character recognition. In one embodiment, the method comprises providing an image data to a plurality of customized machine learning algorithms or various customized neural networks, configured to recognize a set of pre-defined characters. The method comprises presenting one or more suggestions for the character to the user in response to negative character recognition, and training a customized machine learning algorithm corresponding to the character if one of the suggestions is identified by the user. If the suggestions are rejected by the user, the method comprises prompting the user to identify the character and determining presence of the character in the set of pre-defined characters. The method further comprises training a customized machine learning algorithm corresponding to the character if the character is present, or dynamically creating a customized machine learning algorithm corresponding to the character if the character is not present.

Abstract: This technology relates generally to data classification, and more particularly to system and method for classifying an input statement with respect to a small dataset. In one embodiment, a method is provided for classifying an input statement with respect to a small dataset. The method comprises receiving the input statement, extracting a plurality of input keywords from the input statement, computing a membership score for each of a plurality of category ontology trees based on the plurality of input keywords, and determining a classification of the input statement based on the membership score. The plurality of category ontology trees are derived from the small dataset and correspond to a plurality of classifications in the small dataset.

Abstract: Disclosed herein is a method and system for dynamically integrating a plurality of BOTs. The method comprises creating the plurality of BOTs offering one or more automated services, wherein each of the plurality of BOTs has a common BOT structure comprising one or more field parameters. One or more predefined functions are assigned to each of the plurality of BOTs, wherein at least one of the one or more predefined functions comprises a function value. A maturity score for each of the plurality of BOTs is determined based on the one or more field parameters and the function value upon assigning the one or more predefined functions to each of the plurality of BOTs. Finally, the plurality of BOTs are integrated by synchronizing data amongst the plurality of BOTs based on the maturity score.

Abstract: Methods, devices, and non-transitory computer readable media that add interoperable BOTs to a stored BOT inventory. Each of the BOTs is associated with a BOT type, has a common structure, and automates at least a portion of an enterprise process. Assembly rules are obtained. Each of the assembly rules includes one or more constraints for inclusion of one or more of the BOTs in one or more of a plurality of workflows based on the one of the BOT types associated with each of the one or more of the BOTs. The workflows are assembled based on one or more the assembly rules. Each of the workflows comprises a subset of the BOTs and each BOT of the subsets of the BOTs is included in one or more of the workflows based on at least one of an associated functionality, performance, or service level.

Abstract: System and methods for creating on-demand robotic process automation are described. In one example hardware processor-implemented method embodiment, an application providing graphical user interfaces is executed, and a monitoring program executing independently of the application. Using the monitoring program, a screen capture image is obtained of the graphical user interfaces. Graphical user interface elements are identified using a computerized image processing technique. Metadata on presence of confirmatory predictors for the elements, and confirmatory predictor vectors for the interfaces, are generated. Confusion indices for the confirmatory predictors and the confirmatory predictor vectors are calculated. Threshold values are generated based on the calculated confusion indices. A decision table is generated storing sequences of user interactions with the graphical user interface elements. A subset of the stored sequences is identified as yielding a successful outcome.

Abstract: This disclosure relates generally to human-machine interaction. In one embodiment, an interaction device for providing the interaction between the user and the ECA is disclosed. The interaction device comprises a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, which, on execution, causes the processor to receive conversation data of a user interacting with the ECA, wherein the ECA is presented on an interface of the interaction device. The processor further determines an emotional state of the user based on one or more behavioral parameters associated with the conversation data of the user. The processor identifies a response state for the ECA corresponding to the emotional state of the user, wherein the response state is identified from a plurality of response states based on a pre-defined probability for each response state. The processor further transitions behavior of the ECA based on the response state.