2423392
Project Grant
Overview
Grant Description
SBIR Phase I: Reducing medical insurance claim denials with code-augmented policies
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project to provide a framework so that AI systems can follow rules given by humans, in the form of policies, laws, contractual agreements, or the like.
This will allow for trustworthy chatbots and interactive AI agents, which are already becoming widespread amongst all industries despite their known limitations (particularly problems of hallucination) and inability to behave in accordance with the given policies.
Actualization’s technology will streamline build the medical claims creation process, by allowing for complex insurance policies and regulations to be incorporated into the considerations of healthcare management systems.
Given that virtually all industries with a customer interaction component are turning to chatbots, the economic impact of the project is significant.
Furthermore, this work will advance the scientific and technological understanding of how to design rules such that they can be consistently interpreted not only by different humans, but by artificially intelligent systems.
To establish commercial feasibility, market and customer hypotheses will be tested through a survey, customer discovery interviews, expert feedback, and the development and testing of a pilot prototype.
This Small Business Innovation Research Phase I project seeks to develop an automated method for converting policies, rules, and laws into a format that can be understood and enforced by both humans and machines.
It does this by using a combination of state-of-the-art natural language processing techniques developed through prior research on automated legal reasoning to convert policies and examples of that policy’s interpretation into code-augmented policies (CAPS), and to generate test cases designed so that human experts can evaluate whether the CAPS capture the intent and spirit of the original policies.
The CAPS can then be integrated into existing frameworks, focusing initially on the domains of customer service chatbots and healthcare claims.
Because legal, regulatory, policy, and contractual language are open-textured to allow for flexibility in interpretation, it can be difficult for automated systems to reason about whether a novel action is permitted.
And because it is typically impossible to anticipate all possible boundary cases and implications of policies, writing policies can be difficult.
Thus, this project will establish technical and commercial feasibility via three experiments designed to discover which AI approaches best overcome these technological hurdles, and which automatic measures of policy-cap fit best reflect human preferences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project to provide a framework so that AI systems can follow rules given by humans, in the form of policies, laws, contractual agreements, or the like.
This will allow for trustworthy chatbots and interactive AI agents, which are already becoming widespread amongst all industries despite their known limitations (particularly problems of hallucination) and inability to behave in accordance with the given policies.
Actualization’s technology will streamline build the medical claims creation process, by allowing for complex insurance policies and regulations to be incorporated into the considerations of healthcare management systems.
Given that virtually all industries with a customer interaction component are turning to chatbots, the economic impact of the project is significant.
Furthermore, this work will advance the scientific and technological understanding of how to design rules such that they can be consistently interpreted not only by different humans, but by artificially intelligent systems.
To establish commercial feasibility, market and customer hypotheses will be tested through a survey, customer discovery interviews, expert feedback, and the development and testing of a pilot prototype.
This Small Business Innovation Research Phase I project seeks to develop an automated method for converting policies, rules, and laws into a format that can be understood and enforced by both humans and machines.
It does this by using a combination of state-of-the-art natural language processing techniques developed through prior research on automated legal reasoning to convert policies and examples of that policy’s interpretation into code-augmented policies (CAPS), and to generate test cases designed so that human experts can evaluate whether the CAPS capture the intent and spirit of the original policies.
The CAPS can then be integrated into existing frameworks, focusing initially on the domains of customer service chatbots and healthcare claims.
Because legal, regulatory, policy, and contractual language are open-textured to allow for flexibility in interpretation, it can be difficult for automated systems to reason about whether a novel action is permitted.
And because it is typically impossible to anticipate all possible boundary cases and implications of policies, writing policies can be difficult.
Thus, this project will establish technical and commercial feasibility via three experiments designed to discover which AI approaches best overcome these technological hurdles, and which automatic measures of policy-cap fit best reflect human preferences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Tampa,
Florida
33613-1809
United States
Geographic Scope
Single Zip Code
Actualization Ai was awarded
Project Grant 2423392
worth $274,926
from National Science Foundation in September 2024 with work to be completed primarily in Tampa Florida United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Reducing Medical Insurance Claim Denials with Code-Augmented Policies
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project to provide a framework so that AI systems can follow rules given by humans, in the form of policies, laws, contractual agreements, or the like. This will allow for trustworthy chatbots and interactive AI agents, which are already becoming widespread amongst all industries despite their known limitations (particularly problems of hallucination) and inability to behave in accordance with the given policies. Actualization’s technology will streamline build the medical claims creation process, by allowing for complex insurance policies and regulations to be incorporated into the considerations of healthcare management systems. Given that virtually all industries with a customer interaction component are turning to chatbots, the economic impact of the project is significant. Furthermore, this work will advance the scientific and technological understanding of how to design rules such that they can be consistently interpreted not only by different humans, but by artificially intelligent systems. To establish commercial feasibility, market and customer hypotheses will be tested through a survey, customer discovery interviews, expert feedback, and the development and testing of a pilot prototype.
This Small Business Innovation Research Phase I project seeks to develop an automated method for converting policies, rules, and laws into a format that can be understood and enforced by both humans and machines. It does this by using a combination of state-of-the-art natural language processing techniques developed through prior research on automated legal reasoning to convert policies and examples of that policy’s interpretation into code-augmented policies (CAPs), and to generate test cases designed so that human experts can evaluate whether the CAPs capture the intent and spirit of the original policies. The CAPs can then be integrated into existing frameworks, focusing initially on the domains of customer service chatbots and healthcare claims. Because legal, regulatory, policy, and contractual language are open-textured to allow for flexibility in interpretation, it can be difficult for automated systems to reason about whether a novel action is permitted. And because it is typically impossible to anticipate all possible boundary cases and implications of policies, writing policies can be difficult. Thus, this project will establish technical and commercial feasibility via three experiments designed to discover which AI approaches best overcome these technological hurdles, and which automatic measures of policy-CAP fit best reflect human preferences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 23-515
Status
(Ongoing)
Last Modified 9/17/24
Period of Performance
9/1/24
Start Date
8/31/25
End Date
Funding Split
$274.9K
Federal Obligation
$0.0
Non-Federal Obligation
$274.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2423392
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
WE1HTBZ6AM35
Awardee CAGE
9PNQ8
Performance District
FL-15
Senators
Marco Rubio
Rick Scott
Rick Scott
Modified: 9/17/24