FiscalNote‘s patented advocacy system utilizes machine learning models to tailor messages sent to advocates or policymakers for desired outcomes. The system analyzes personal profiles and past message outcomes to create effective messages. This innovative method enhances advocacy efforts by optimizing message content based on recipient characteristics. GlobalData’s report on FiscalNote gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on FiscalNote, was a key innovation area identified from patents. FiscalNote's grant share as of February 2024 was 59%. Grant share is based on the ratio of number of grants to total number of patents.

Machine learning models for advocacy message creation and delivery

Source: United States Patent and Trademark Office (USPTO). Credit: FiscalNote Inc

A recently granted patent (Publication Number: US11888600B2) outlines a method performed by a computer system that involves maintaining databases storing data related to individuals, message templates, and messages. The method includes training a machine learning model to select message characteristics based on input parameters such as personal profile characteristics of the sender and receiver. This model is then used to integrate selected message characteristics into new messages based on templates, which are subsequently sent by the sender to the receiver.

Furthermore, the patent specifies that the method is particularly applicable when the sender is an advocate and the receiver is a policymaker, with the goal of influencing legislation or policy decisions. The stored data used for training the machine learning model includes information about message templates, message characteristics, and outcomes of previous messages. Additionally, the method involves user interfaces for specifying message templates and search criteria for selecting sender-receiver pairs based on personal profile characteristics. The selected message characteristics can include various elements such as message topics, requested actions, message content, formatting, and metadata, among others. The input for the machine learning model also considers the desired outcome of sending a message to the receiver, enhancing the customization and effectiveness of the communication process.

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