Futurus Group has announced that it is seeking a patent for its proprietary gratitude prediction machine learning model.
Its flagship product, G2G (Gratitude to Give) uses its patent-pending algorithm and, according to Futurus Group, is the first artificially intelligent product on the market to focus on predicting gratitude specifically in a healthcare environment. Early results using G2G show clients have experienced a 4-5 times increase in identifying donors versus their former prioritisation methods.
Chad Gobel, CEO and founder of Gobel Group said:
“We conceptualised this idea a few years ago. We felt that the way philanthropic organisations were finding prospects was incomplete, and we wanted to develop an algorithm that could dramatically improve the methodology. We’re so excited to see such great preliminary results, and the filing of the patent is an exciting milestone for us.”
The patent-pending technology involves the use of various machine learning models in order to generate a gratitude score that characterises the likelihood that patients will make a donation to the healthcare organisation in the future.
Using this method, which is HIPAA compliant, organisations will be able to obtain patient history data, including data collected from patient interactions with the hospital as well as third party data, and then use the gratitude prediction model to process the data in order to generate a “gratitude score” for each individual.
The technology will enable philanthropic organisations to use the gratitude scores generated by the system to optimise resource allocation and reduce the number of resources required to obtain a target objective. The algorithm will prioritise prospects based on their varying levels of gratitude, thereby optimising the allocation of the organisation’s limited resources according to the prioritisation.
Nathan Chappell, President of Futurus Group, said:
“Our goal with Futurus is to expand and improve the uses of AI-based gratitude indication, both nationally and internationally, and we’re very excited to be working towards securing a patent for our proprietary technology. While we have already seen early success with the model in G2G, we intend to take full advantage of the algorithm and utilise it in other products soon to come.”
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