公开(公告)号：US20240176673A1

公开(公告)日：2024-05-30

申请号：US18072349

申请日：2022-11-30

Applicant: Nice Ltd.

Inventor： Eran ROSEBERG ,  Yuval SHACHAF ,  Oz GRANIT

IPC: G06F9/50

CPC classification number: G06F9/5061

Abstract: A computerized system and method may generate computer automation opportunities based on segmenting action sequences from action data and/or information items. A computerized system including a processor or a plurality of processors, and a memory including a data store of a plurality of data items describing actions input to a computer may be used to receive an input query or a plurality of actions input to a computer; segment action sequences from the stored data items based on the query; and produce automation candidates based on the segmented sequences. Embodiments of the invention may include generating, by a machine learning model, vector embeddings for action sequences, calculating similarity scores for sequences based on the embeddings, and mining a plurality of action subsequences based on, a group or set of similar sequences, as well as additional and/or auxiliary procedures and operations.

公开(公告)号：US20230359659A1

公开(公告)日：2023-11-09

申请号：US17737495

申请日：2022-05-05

Applicant: NICE LTD

Inventor： Oz GRANIT ,  Yuval SHACHAF ,  Eran ROSEBERG

IPC: G06F16/35

CPC classification number: G06F16/355

Abstract: A system and method may identify computer-based processes involving the use of text templates which may be candidates for automation. Using one or more computers, embodiments of the invention may sort low-level user action information for a given process which may be received as input; search for a plurality of strings pasted multiple times in the sorted information; discard one or more of the strings found from the search which correspond to a set of criteria (e.g., found to be shorter, or pasted, or edited fewer times than a predetermined threshold); group the strings according to an identifier of the target app where each string was pasted; iteratively calculate a similarity score for strings or groups of strings, and cluster strings or groups for which the similarity score is below a predetermined threshold, to form final clusters; and suggest the final clusters as automation opportunities to, e.g., a business analyst.

公开(公告)号：US20230113136A1

公开(公告)日：2023-04-13

申请号：US17496624

申请日：2021-10-07

Applicant: Nice Ltd.

Inventor： Eran ROSEBERG ,  Yaron Moshe BIALY ,  Yuval SHACHAF

IPC: G06F16/2458

Abstract: 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.

公开(公告)号：US20230041328A1

公开(公告)日：2023-02-09

申请号：US17392271

申请日：2021-08-03

Applicant: NICE LTD.

Inventor： Pavan LAHOTI ,  Shivdatta MORWADKAR ,  Yuval SHACHAF

IPC: G06Q30/02 ,  H04L29/08 ,  G06N20/00

Abstract: A computerized-method for dynamic digital-survey-channel selection is provided herein. In a computerized system having a processor, a memory to store a database of survey responses and a database of customers details, and a Voice of the Customer (VOC) platform having an outbound-message Application Programming Interface (API) to send a digital survey to a customer, via a plurality of digital survey channel types, when a customer is nominated for a digital survey, the computerized-method included operating by said processor, a digital-survey-channel-selection module. The digital-survey-channel-selection module includes (i) determining a digital-survey-channel type to elevate customers-response-rate to a digital survey; and (ii) sending the determined digital-survey-channel type to the outbound-message API to trigger the digital survey to a computerized device of the customer, via the determined digital-survey-channel type.

公开(公告)号：US20220318713A1

公开(公告)日：2022-10-06

申请号：US17223537

申请日：2021-04-06

Applicant: Nice Ltd.

Inventor： Yaron Moshe BIALY ,  Yuval SHACHAF ,  Eran ROSEBERG

IPC: G06Q10/06 ,  G06K9/62

Abstract: 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.

公开(公告)号：US20210304103A1

公开(公告)日：2021-09-30

申请号：US16830806

申请日：2020-03-26

Applicant: NICE Ltd.

Inventor： David GEFFEN ,  Yuval SHACHAF ,  Gennadi LEMBERSKY

IPC: G06Q10/06

Abstract: 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.

公开(公告)号：US20210256417A1

公开(公告)日：2021-08-19

申请号：US16791316

申请日：2020-02-14

Applicant: Nice Ltd.

Inventor： Hila KNELLER ,  Lior BEN ELIEZER ,  Yuval SHACHAF ,  Gennadi LEMBERSKY ,  Natan KATZ

IPC: G06N20/00 ,  H04L12/58 ,  G10L15/22 ,  G10L15/18 ,  G10L15/183 ,  G10L15/16 ,  G10L15/06

Abstract: 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.