US 12,450,517 B2
Quantum computing assisted reduction of carbon footprint
Atul Abhyankar, Pune (IN); Payal Panda, Bangalore (IN); Samanwita Majumdar, Kolkata (IN); Nishikanta Panda, Bangalore (IN); Swaroop Prasad Bhongade, Mumbai (IN); Shruti Sudhakar Marathe, Mumbai (IN); and Shaista Firdose, Bangalore (IN)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Jan. 17, 2023, as Appl. No. 18/155,443.
Prior Publication US 2024/0242104 A1, Jul. 18, 2024
Int. Cl. G06N 10/60 (2022.01); G06F 3/0482 (2013.01); G06F 3/0484 (2022.01); G06F 40/134 (2020.01); G06F 40/166 (2020.01); G06F 40/20 (2020.01); G06F 40/40 (2020.01)
CPC G06N 10/60 (2022.01) [G06F 3/0482 (2013.01); G06F 3/0484 (2013.01); G06F 40/134 (2020.01); G06F 40/166 (2020.01); G06F 40/20 (2020.01); G06F 40/40 (2020.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method for applying machine learning and quantum computing to generate carbon emissions for emails and modifying emails to reduce carbon emissions, the method comprising:
receiving an image of a draft email and an email log description of the draft email;
training a first classical machine learning model to classify draft emails including a single email address or a group email address in the “to:” field resulting in the first trained classical machine learning model;
applying the first trained classical machine learning model to the email log description to classify the draft emails as including the single email address or the group email address in the “to:” field;
training a second classical machine learning model to classify the draft emails as spam or standard emails resulting in the second trained classical machine learning model;
applying the second trained classical machine learning model to the email log description to classify the draft email as spam or a standard email;
training a QKNN machine learning model to classify an email as containing an attachment or not containing an attachment resulting in the trained QKNN machine learning model;
applying the trained quantum K nearest-neighbor (KNN) machine learning model to classify the draft email as containing the attachment or not containing the attachment;
based on output from the first trained classical machine learning model, the second trained classical machine learning model, and the trained QKNN machine learning model, determining one or more modifications that can be made to the draft email to reduce carbon emissions;
calculating a quantity of carbon emissions corresponding with each of the one or more modifications; and
presenting to a user, via a display of a user interface, the determined one or more modifications with the calculated quantity of carbon emissions.