SBIR/STTR Research Award

Voice Controlled Office Assistant Using Abductive Networks ($19,968 Phase I option proposed)

Award identifier 084d9c8d04f9afc2a3f5082b3a95b10a96bfdd700ccb1e347c23534280ff6602Awarding agency Department of Defense · Air Force

Award amount

$79,853

Program and phase

SBIR · Phase I

Award year

1994

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Defense
Branch
Air Force
Recipient company
ABTECH CORPORATION
Proposal award date
Not reported
Contract end date
Not reported
UEI
Not reported
Solicitation number
Not reported
Topic code
Not reported

Research abstract

AbTech will survey the hardware and software available to determine what capabilities should be included in an automated office assistant software suite. A language parsing and encoding system in ANN (a leading semantic understanding software package) will be used to parse and encode ASCII output resulting from a voice recognition system such as DragonDictate. The ASCII output from the voice recognition system will go to the ANN language encoding facility that will parse the sentence and assign deep meaning to the words as a function of context. The encoded strings corresponding to the spoken words will go to information processing modules development with AIM, which will have been trained on variable constructs permissible in English so that the encoded word meaning strings can be used to select an appropriate response to the user. The NASA CLIPS expert system will be used to create expert systems capable of activating the command sequences needed for the various packages to accomplish the result desired by the user or to activate and display the function requested by the user. ANN will determine from parsing the sentence and asking any required clarifying questions the universe of context and will then encode the words. The codes would be used to train AIM networks to select the correct response. Training sets would be prepared for each category we determined was going to be included in the automated office suite of capabilities. After training these or equivalent statements would cause the AIM models to select the appropriate application package activation sequence. Because of the robustness of the AIM network models, statements that had not been part of the training package but were similar would also produce a desired response.

Technology keywords

Not reported

Agency intelligence

Department of Defense

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.