Nice Ltd

Yuval Shachaf

Gennadi Lembersky

Eran Roseberg

Yaron Moshe Bialy

Roy Klein

Natan Katz

Hila Kneller

David Geffen

Simone Mimun

Lior Ben Eliezer

Adi Levinshtein

Oz Granit

Omer Kuleski

Gal Tesler

Rami Tibi Abu Rass

A system and method may identify computer-based processes which may be candidates for automation. Embodiments may involve a semi-supervised approach for identifying processes as automation opportunities. Transition probabilities for pairs of routines within a candidate process may be calculated based on a set of instances of the process (e.g., in a dataset of computer actions) using a dynamic time-window optimization procedure, where transition times may be measured for a plurality of instances of a first and second routines of a given pair of routines, and where statistical distributions may be calculated and used for deriving one or more time windows, describing a predetermined percentile (e.g., the 70percentile) of the measured transitions and used for estimating a transition probability for the pair of routines. In some embodiments, the input set of transitions and routines may be generated by a user or business analyst using a graphical user interface (GUI).

Systems and methods for automation discovery recalculation using dynamic time window optimization

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and/or execute database queries and/or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

System and method for artificial intelligence based generation of database queries

A system is provided for an automation tool system that is adapted to analyze data structures for robotic process automation (RPA) solutions. The system includes a processor and a computer readable medium operably coupled thereto, to perform operations which include receiving a data structure for an automated computing task for the RPA solutions of a computing system, wherein the data structure comprises one or more computing assets for one or more data processing workflows, parsing the data structure, accessing rule scoring parameters for determining a set of best practices identified for construction of the automated computing task for the RPA solutions of the computing system, scoring the automated computing task for the RPA solutions for the computing system based on the parsing and the rule scoring parameters, and outputting, based on scoring the automated computing task, a score of the automated computing task for the data structure.

Analytics to increase performance of robotic process automation solutions

A computerized-method for sensitive data redaction from screenshots, is provided herein. The computerized-method includes retrieving records of a sequence of screenshots from a database. Then, grouping the sequence-of-screenshots by one feature of one or more features to yield one or more groups. Each group includes screenshots having one common feature. Then, calculating a score for each pixel across all similar screenshots in each group. For each group of screenshots, blackening pixels in all screenshots having a score above a preconfigured threshold to yield data redacted screenshots. The score of each pixel above the preconfigured threshold indicates a high variance between screenshots in the group and a presence of sensitive data therein and then storing the data-redacted screenshots in a screenshots-database.

Computerized-method and computerized-system for sensitive data redaction from screenshots

A system to create new automation routines includes a processor to: Over time, store a group of actions taken by a group of agents within applications. From the stored group of actions, identify a repeating pattern of actions in a subset of the applications, and construct a binary vector, each position within the binary vector storing a 1 for an occurrence of any action within any application of the subset, and a 0 otherwise. From the binary vector and the repeating pattern, extract a sentence including at least one action within at least one application. Based on the repeating pattern, an application type, or a business goal, create a constraint. If the sentence meets the constraint, accept the sentence and add it to a pool of accepted sentences. From the pool of accepted sentences, identify a pattern of occurrences of the accepted sentence, and create a new automation routine.

Methods for discovery of new automation routine types

A method and system for analyzing interactions (for example between a person and an organization) may include for a number of series of interactions (which may be termed journeys), each interaction represented by a channel and a reason, iterating over the series of interactions by selecting initial information including an interaction subset including one or more interactions, and if there is a set of interaction series among the series of interactions meeting certain conditions, adding an interaction to the interaction subset and determining in a recursive fashion if for the interaction subset, there is a subset among the set of interaction series meeting the conditions which also meet the conditions. Conditions may include a threshold average rating for the set of interaction series, and a threshold number of interaction series in which the interaction subset is found.

Systems and methods for finding an interaction subset within a set of interactions

A method and system for dynamically determining a minimum support for automation mining is provided. The method and system include modifying the minimum support pattern such that the minimum support can result pattern mining algorithms finding a sufficient number of patterns in a practical duration.

Systems and methods automatically adjusting minimum support for routing mining

A method and system for analyzing and connecting computer-based actions into sentences may include for a series of computer-based actions, determining the case ID for the action for each action where an identifier or case ID can be determined, creating sequences of subsets of the series of computer-based actions using the case ID, and merging sequences having computer-based actions having the same case ID. A set of case IDs may be extracted from the actions using a clustering algorithm based on features of potential case IDs such as gaps in appearance of potential case IDs in a sequence of actions and consecutive appearances of potential case IDs in a sequence of actions. The extracted case IDs may be used when creating sequences.

Systems and methods for analyzing and connecting automation sequences

A system and method for creating input data to be used to train a conversational bot may include receiving a set of conversations, each conversation including sentences, classifying each sentence into a dialog act taken from a number of dialog acts, for each set of sentences classified into a dialog act, clustering the set of sentences into clusters based on the content (e.g. text) of the sentences, each cluster having a cluster name or label, and generating a language model based on the cluster labels. Slots may be identified in the sentences based in part on the dialog act classifications. A bot may be trained using data such as the slots, language model, and clusters.

System and method for creating data to train a conversational bot

A computerized-method for automatically documenting automatically-discovered actions of a business-process for automation thereof is provided herein. The computerized-method includes operating a build-automation-document module. The build-automation-document module includes: (i) retrieving the set of automatically-discovered actions of the business process for automation thereof. Each action is of a related type and related to an application to complete the business process via the applications portal. (ii) for each action of the set of automatically-discovered actions, extracting an associated routine-name of a routine; (iii) grouping similar actions by related window in related application and type of action into clusters of actions; (iv) removing clusters of actions which are not necessary to an automated business process; (v) sorting the clusters of actions in an order for automatically performing the business process; and (vi) saving the sorted automatically-discovered actions into a file to be presented on a display unit that is associated to the computerized system.

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Systems and methods for measuring the effectiveness of an agent coaching program calculate a rate of change in a first Key Performance Indicator for a first agent in a first coaching program during a period of time; select a control group of agents in which agents in the control group of agents were not exposed to the first coaching program; calculate an average rate of change in the first Key Performance Indicator for the control group of agents during the period of time; and calculate a first coaching impact of the first coaching program on the first Key Performance Indicator for the first agent relative to the average rate of change in the first Key Performance Indicator for the control group of agents during the period of time.

Systems and methods for measuring the effectiveness of an agent coaching program

Systems and methods for mapping a customer journey in an interactive voice response (IVR) system to a category from a categories list, build a directed graph based on one or more sample IVR customer journeys; generate a black list based on the directed graph; for a given customer journey: filter one or more non-informative menus from the plurality of available menus based on the black list; concatenate the respective associated menu prompt of each menu that was not filtered, and one or more user responses to one or more menu prompts from the series of interactions in the given customer journey, into a concatenated word string; calculate a similarity score between the concatenated word string and a category name of each category from the categories list; and map the given customer journey to the category whose category name produces the highest similarity score.

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Netanya, Israel

Yuval Shachaf