公开(公告)号：US20240176785A1

公开(公告)日：2024-05-30

申请号：US18071008

申请日：2022-11-29

Applicant: Nice Ltd.

Inventor： Darshan AMBHAIKAR ,  Seemit Vijay SHAH ,  Yuvraj Amrutrao SAWANT ,  Nick MARTIN

IPC: G06F16/2457 ,  G06F16/22

CPC classification number: G06F16/24573 ,  G06F16/2272 ,  G06F16/2358

Abstract: A computerized system and method may quantify a level or degree of monotony associated with the execution of repetitive tasks involving a plurality of computing devices—and may accordingly determine or choose a task schedule for which the smallest degree of monotony is calculated. A computerized system comprising one or more processors, a communication interface to communicate via a communication network with remote computing devices, and a memory including data items describing tasks involving the remote computing devices, may be used for selecting remote computers based on the stored data items; calculate monotony indices for the selected computing devices based on, e.g., a plurality of tasks and corresponding time windows (in which, e.g., the tasks were performed or executed); automatically documenting the calculated monotony indices in a database; and transmitting instructions to automatically execute computer operations on a remote computer based on calculated monotony indices.

公开(公告)号：US20220245647A1

公开(公告)日：2022-08-04

申请号：US17165449

申请日：2021-02-02

Applicant: NICE LTD.

Inventor： Salil DHAWAN ,  Swati Pranay KADU ,  Yuvraj Amrutrao SAWANT

IPC: G06Q30/00 ,  G06N20/00 ,  G06Q30/02 ,  H04M3/51

Abstract: Systems for and methods of assessing the priority of a customer reported issue include receiving input regarding a customer issue experienced by a customer; calculating an incident grievance score by inserting the received input into a machine learning model; assigning a priority to the customer issue based on the calculated incident grievance score; receiving updated input regarding the customer issue; periodically recalculating the incident grievance score for the customer issue by inserting the received input and the updated input into the machine learning model; changing the priority of the customer issue when the recalculated incident grievance score differs from the calculated incident grievance score; and notifying a team assigned to fix the customer issue when the priority of the customer issue changes.

公开(公告)号：US20220253788A1

公开(公告)日：2022-08-11

申请号：US17170390

申请日：2021-02-08

Applicant: NICE LTD.

Inventor： Onkar Dhananjay HINGNE ,  Yuvraj Amrutrao SAWANT ,  Mohin NADAF ,  Sujoy D. DATTA

IPC: G06Q10/06 ,  G06F16/23 ,  G06F16/245 ,  H04L29/08

Abstract: A system is provided for a cloud computing environment that is adapted to perform data processing and tracking of agent data between cloud computing tenants. The system includes a processor and a computer readable medium operably coupled thereto, to perform operations which include determining a unique identifier (ID) for an agent of the cloud computing tenants, accumulating, over a time period, agent data for the agent, refining the agent data to curated data views for the agent data based on a plurality of aggregate reports for each of a plurality of KPIs in the agent data, determining a batch processing job for the refined agent data, calculating, using the batch processing job, a base asset value (BAV) score for the agent, and updating a profile for the agent associated with the unique ID based on the calculated BAV score.

公开(公告)号：US20220147397A1

公开(公告)日：2022-05-12

申请号：US17094143

申请日：2020-11-10

Applicant: Nice Ltd.

Inventor： Pratim Kumar BHATTACHARYA ,  Sameer POKARNA ,  Yuvraj Amrutrao SAWANT

IPC: G06F9/50

Abstract: A system and method of altering computer resource allocation may include receiving a first time-series metric describing a processes; receiving a second time-series resource metric describing a computer resource; and analyzing the first time-series metric and the second time-series resource metric as an independent and a dependent variable to determine a gradient coefficient defining the ratio of the rate of change between the first time-series metric and the second time-series resource metric. The result may be used to predict resource usage, and to provision or allocate the resource accordingly.