US 11,888,600 B2
Use of machine-learning models in creating messages for advocacy campaigns
Vladimir Eidelman, Chevy Chase, MD (US); Daniel Argyle, Provo, UT (US); Paul Matthew Ellender, Jr., Baton Rouge, LA (US); and Megan McCoskey, Washington, DC (US)
Filed by FiscalNote, Inc., Washington, DC (US)
Filed on Sep. 20, 2022, as Appl. No. 17/933,837.
Application 17/933,837 is a continuation of application No. 17/933,836, filed on Sep. 20, 2022, granted, now 11,711,324.
Application 17/933,836 is a continuation in part of application No. 17/933,838, filed on Sep. 20, 2022.
Application 17/933,838 is a continuation of application No. 17/652,780, filed on Feb. 28, 2022, granted, now 11,451,497, issued on Sep. 20, 2022.
Application 17/652,780 is a continuation of application No. 17/453,647, filed on Nov. 4, 2021, granted, now 11,316,808, issued on Apr. 26, 2022.
Application 17/453,647 is a continuation of application No. PCT/US2021/072254, filed on Nov. 4, 2021.
Claims priority of provisional application 63/109,852, filed on Nov. 4, 2020.
Prior Publication US 2023/0124697 A1, Apr. 20, 2023
Int. Cl. H04L 51/02 (2022.01); H04L 51/52 (2022.01); H04L 51/18 (2022.01); G06N 20/20 (2019.01); G06F 40/40 (2020.01); G06F 40/186 (2020.01); G06F 40/174 (2020.01); G06F 3/04847 (2022.01); G06Q 30/02 (2023.01); G06Q 30/0251 (2023.01); G06Q 30/0241 (2023.01)
CPC H04L 51/02 (2013.01) [G06F 3/04847 (2013.01); G06F 40/174 (2020.01); G06F 40/186 (2020.01); G06F 40/40 (2020.01); G06N 20/20 (2019.01); G06Q 30/0269 (2013.01); G06Q 30/0276 (2013.01); H04L 51/18 (2013.01); H04L 51/52 (2022.05)] 20 Claims
OG exemplary drawing
 
1. A method performed by a computer system having at least one processor and a memory, the method comprising, the computer system:
maintaining one or more databases that store data comprising:
a record for each person of a plurality of people;
for each person, a plurality of personal profile characteristics specific to the person;
a record for each message template of a plurality of message templates, wherein each message template comprises one or more variable fields for receiving message characteristics and one or more static fields storing content common to all messages based on the message template;
a record for each message of a plurality of messages, wherein the each message was previously sent by a sending person of the plurality of people to a receiving person of the plurality of people based on one of the plurality of message templates, the each message including a respective request by the sending person for the receiving person to perform an action; and
for each message of the plurality of messages, an indication of an outcome of the request by the sending person for the receiving person to perform the action;
training a machine learning model based on a portion of the stored data, wherein the trained machine learning model is configured to select a subset from plural message characteristics using input based on:
a message template comprising one or more variable fields for receiving message characteristics and one or more static fields storing content common to all messages based on the message template;
a set of personal profile characteristics specific to a sending person; and
a set of personal profile characteristics specific to a receiving person; and
for each pair of a plurality of pairs of a sending person and a receiving person selected from the plurality of people, the sending person being selected to send a message based on a current message template to the receiving person:
receiving a subset selected from plural message characteristics in response to providing input to the trained machine learning model, the input being based on the current message template, the set of personal profile characteristics specific to the sending person, and the set of personal profile characteristics specific to the receiving person;
integrating the selected set of message characteristics into a new message based on the current message template; and
causing the new message to be sent by or on behalf of the sending person to the receiving person.