公开(公告)号：US20230022673A1

公开(公告)日：2023-01-26

申请号：US17373385

申请日：2021-07-12

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Noemi Derzsy ,  Subhabrata Majumdar ,  Rajat Malik

IPC: G06N5/02 ,  G06N5/04 ,  G06F40/40 ,  G06F40/295

Abstract: Techniques for knowledge discovery based on indirect inference of association are presented. A data management component (DMC) can determine and extract, in a structured format, entities, relationships between entities, and concepts relating thereto in documents, based on analysis of information in the documents and/or keywords relating to concepts, to generate an association inference model. Using artificial intelligence techniques, DMC can embed the entities and relationships to a common representation to generate and train a scoring model that can be used to evaluate and score similarity strength between entities, including entities that do not have a known relationship, and can predict or infer relationships, including indirect relationships, between entities or between concepts. In that regard, DMC or user can evaluate concept-level scores to determine a level of relationship between concepts. DMC can feedback information from the scoring model or evaluation to update the association inference model.

公开(公告)号：US20230267362A1

公开(公告)日：2023-08-24

申请号：US17652268

申请日：2022-02-23

Applicant: AT&T INTELLECTUAL PROPERTY I, L.P.

Inventor： Noemi Derzsy ,  Balachander Krishnamurthy

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A method includes obtaining descriptive information for a first machine learning project, identifying, based on the descriptive information, a plurality of past machine learning projects which are similar to the first machine learning project, retrieving digital documents that describe the bias evaluation pipelines that were used to evaluate the plurality of past machine learning projects, detecting a common bias evaluation pipeline step among at least a subset of the digital documents, extracting, from the subset, a snippet of machine-executable code that corresponds to the common bias evaluation pipeline step, modifying the snippet of machine-executable code with use case data that is specific to the first machine learning project to generate modified machine-executable code, and generating a proposed bias evaluation pipeline for evaluating the first machine learning project, wherein the proposed bias evaluation pipeline includes the modified machine-executable code.