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SBIR Phase I:A Human-Aware Platform for Socially Collaborative Personal Artificial Intelligence (AI) Assistants

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2223224
Agency Tracking Number: 2223224
Amount: $275,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: HC
Solicitation Number: NSF 22-551
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-05-01
Award End Date (Contract End Date): 2024-04-30
Small Business Information
353 Kearny St Suite 23
San Francisco, CA 94108
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Crystal Chao
 (415) 275-0362
 crystal@appliedsentience.ai
Business Contact
 Crystal Chao
Phone: (415) 275-0362
Email: crystal@appliedsentience.ai
Research Institution
N/A
Abstract

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is enabling Artificial Intelligence (AI) assistants to become proactive, empowering them to provide better service to users. Currently, commercial AI assistants respond to user requests reactively. The technologies developed in this project would provide AI assistants with the situational awareness to understand users’ lives and predict their needs.The technology will also enable social intelligence to take the initiative to support users in appropriate ways. This SBIR Phase I project will apply these technologies to a consumer product for assisting users with time management and meeting goals while establishing and strengthening healthy, desirable habits in their daily lives. Proactive personal AI assistants have the potential to improve productivity, convenience, and quality of life for every person, as well as to promote aging in place with greater independence and wellness. Fundamental scientific advancements will also enable a new generation of potential applications for AI assistants across sectors, fueling economic growth and creating jobs. _x000D_
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This project addresses two central technical challenges for enabling proactive AI assistants: contextual awareness of users and agent-initiated interaction. Contextual awareness includes the AI agent’s real-time understanding of current user state and activity, as well as a long-term understanding of past user habits. The project proposes to develop hybrid computational models combining machine learning of multimodal user observations from visual, acoustic, and geolocation data with probabilistic graphical models that perform long-term inference and prediction over historical user observations. A virtually-embodied AI agent will leverage these contextual awareness representations to conduct real-time, face-to-face collaborations with users. The project proposes to research and develop a dynamic scheduling approach to proactively enable the agent to communicate with users. These models will be integrated within a broader system that assists users with time management. This system will implement an end-to-end architecture for protecting user privacy while handling their data. The technical solution will be validated based on quantitative metrics related to utility and user acceptance by deploying the prototype in end users’ homes over a multi-week period and conducting surveys about their subjective experience of the proactive AI assistants._x000D_
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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.

* Information listed above is at the time of submission. *

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