公开(公告)号：US11934793B2

公开(公告)日：2024-03-19

申请号：US17516409

申请日：2021-11-01

Applicant: SRI International

Inventor： Ajay Divakaran ,  Karan Sikka ,  Yi Yao ,  Yunye Gong ,  Stephanie Nunn ,  Pritish Sahu ,  Michael A. Cogswell ,  Jesse Hostetler ,  Sara Rutherford-Quach

IPC: G06F40/35 ,  G06F16/33 ,  G06N5/04

CPC classification number: G06F40/35 ,  G06F16/3335 ,  G06N5/04

Abstract: A method, apparatus and system for training an embedding space for content comprehension and response includes, for each layer of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, determining a question answer pair based on a question generated using at least one word of the set of words and at least one content domain, determining a vector representation for the generated question and for content related to the at least one content domain of the question answer pair, and embedding the question vector representation and the content vector representations into a common embedding space where vector representations that are related, are closer in the embedding space than unrelated embedded vector representations. Requests for content can then be fulfilled using the trained, common embedding space.

公开(公告)号：US20240403728A1

公开(公告)日：2024-12-05

申请号：US18614388

申请日：2024-03-22

Applicant: SRI International

Inventor： Yunye Gong ,  Yi Yao ,  Xiao Lin ,  Ajay Divakaran

IPC: G06N20/20 ,  G06N7/01

Abstract: In general, techniques are described that address the limitations of existing conformal prediction methods for cascaded models. In an example, a method includes receiving a first validation data set for validating performance of an upstream model of the two or more cascaded models and receiving a second validation data set for validating performance of a downstream model of the two or more cascaded models wherein the second validation data set is different than the first validation set; estimating system-level errors caused by predictions of the upstream model based on the first validation data set; estimating system-level errors caused by predictions of the downstream model based on the second validation data set; and generating a prediction confidence interval that indicates a confidence for the system based on the system-level errors caused by predictions of the upstream model and based on the system-level errors caused by predictions of the downstream model.