2335605
Project Grant
Overview
Grant Description
SBIR PHASE I: VOXCARE: ARTIFICIAL INTELLIGENCE-BASED MONITORING FOR SUBSTANCE USE INDICATORS IN YOUTH -The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to reduce the $740B a year burden of substance use. Healthcare costs, crime, and lost productivity are only a fraction of the problem that has claimed over 100,000 lives in 2021 alone.
The proposed innovation is a novel AI-based technology integrated into a mobile app that allows the assessment of drug use or alcohol intoxication based on voice signals. Substance and alcohol use disorders are prevalent across every stratum of our society. Not only are the users impacted, but the penumbra of individuals impacted includes every citizen in the US.
Preventing early exposure to illicit drugs and alcohol lowers the likelihood of developing severe addiction later in life. By disrupting normal brain development, various substances increase the long-term negative effects on a child?s life. Given the skyrocketing rates of depression and anxiety among teenagers, the wide availability of highly potent synthetic substances, and the ease of access to drugs, parents feel ill-equipped to protect their children.
This Small Business Innovation Research Phase 1 project will help advance knowledge in several important areas, such as artificial intelligence capabilities, leveraging digital media platforms for data collection, synthetic audio data generation, and using the mix of temporal and spectral domains for audio analysis. Each area is an active research field, but the combination of all these areas into a product for analyzing voice to detect intoxication caused by various substances has not been previously attempted by academia or industry.
The company?s technology relies on the unique impact that each substance use has on neuromotor function, as manifested by the bio-mechanical process of voice production. By comparing the voice signal against the speakers? baseline voice, the technology will flag abnormal deviations of acoustic features that are typically consistent with intoxication. The field of voice analysis integrates a range of features and signal characteristics to extract valuable insights from voice signals.
The primary objectives are to develop and deploy this AI-based technology in ubiquitous devices such as mobile phones while ensuring its performance in real-life conditions and achieving soft real-time processing. 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 proposed innovation is a novel AI-based technology integrated into a mobile app that allows the assessment of drug use or alcohol intoxication based on voice signals. Substance and alcohol use disorders are prevalent across every stratum of our society. Not only are the users impacted, but the penumbra of individuals impacted includes every citizen in the US.
Preventing early exposure to illicit drugs and alcohol lowers the likelihood of developing severe addiction later in life. By disrupting normal brain development, various substances increase the long-term negative effects on a child?s life. Given the skyrocketing rates of depression and anxiety among teenagers, the wide availability of highly potent synthetic substances, and the ease of access to drugs, parents feel ill-equipped to protect their children.
This Small Business Innovation Research Phase 1 project will help advance knowledge in several important areas, such as artificial intelligence capabilities, leveraging digital media platforms for data collection, synthetic audio data generation, and using the mix of temporal and spectral domains for audio analysis. Each area is an active research field, but the combination of all these areas into a product for analyzing voice to detect intoxication caused by various substances has not been previously attempted by academia or industry.
The company?s technology relies on the unique impact that each substance use has on neuromotor function, as manifested by the bio-mechanical process of voice production. By comparing the voice signal against the speakers? baseline voice, the technology will flag abnormal deviations of acoustic features that are typically consistent with intoxication. The field of voice analysis integrates a range of features and signal characteristics to extract valuable insights from voice signals.
The primary objectives are to develop and deploy this AI-based technology in ubiquitous devices such as mobile phones while ensuring its performance in real-life conditions and achieving soft real-time processing. 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
San Jose,
California
95136-5024
United States
Geographic Scope
Single Zip Code
Tenvos was awarded
Project Grant 2335605
worth $275,000
from National Science Foundation in March 2024 with work to be completed primarily in San Jose California United States.
The grant
has a duration of 6 months 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: VoxCare: Artificial Intelligence-based Monitoring for Substance Use Indicators in Youth
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to reduce the $740B a year burden of substance use. Healthcare costs, crime, and lost productivity are only a fraction of the problem that has claimed over 100,000 lives in 2021 alone. The proposed innovation is a novel AI-based technology integrated into a mobile app that allows the assessment of drug use or alcohol intoxication based on voice signals. Substance and alcohol use disorders are prevalent across every stratum of our society. Not only are the users impacted, but the penumbra of individuals impacted includes every citizen in the US. Preventing early exposure to illicit drugs and alcohol lowers the likelihood of developing severe addiction later in life. By disrupting normal brain development, various substances increase the long-term negative effects on a child’s life. Given the skyrocketing rates of depression and anxiety among teenagers, the wide availability of highly potent synthetic substances, and the ease of access to drugs, parents feel ill-equipped to protect their children.
This Small Business Innovation Research Phase 1 project will help advance knowledge in several important areas, such as artificial intelligence capabilities, leveraging digital media platforms for data collection, synthetic audio data generation, and using the mix of temporal and spectral domains for audio analysis. Each area is an active research field, but the combination of all these areas into a product for analyzing voice to detect intoxication caused by various substances has not been previously attempted by academia or industry. The company’s technology relies on the unique impact that each substance use has on neuromotor function, as manifested by the bio-mechanical process of voice production. By comparing the voice signal against the speakers’ baseline voice, the technology will flag abnormal deviations of acoustic features that are typically consistent with intoxication. The field of voice analysis integrates a range of features and signal characteristics to extract valuable insights from voice signals. The primary objectives are to develop and deploy this AI-based technology in ubiquitous devices such as mobile phones while ensuring its performance in real-life conditions and achieving soft real-time processing.
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
(Complete)
Last Modified 3/5/24
Period of Performance
3/1/24
Start Date
9/30/24
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2335605
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
CXCPXF7ARM26
Awardee CAGE
9HPD2
Performance District
CA-16
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Modified: 3/5/24