US 12,223,080 B1 · Row level security in natural language question answering

Abstract

This disclosure describes a natural language question (NLQ) query service within a service provider network that provides row level security (RLS) for autocomplete during entry of NLQs and fuzzy matching in NLQ answering. The rules take the form of per-user predicates such as Tim can only see rows with region=US. In configurations a complex extraction and preprocessing pipeline to extract distinct combinations of values against RLS predicate “rule keys” is used. Those distinct values are indexed along with grouped rule keys to enable pushing down predicates at auto-complete time. This enables pushing part of RLS rule handling to ingestion time of a dataset rather than handling all RLS rule handling at query time, enabling meeting of latency goals. In configurations, a single logical document of unique cell values is split into multiple documents with a subset of rule keys to handle scalability limits.

Claims20

Dependent claims

A computer-implemented method comprising:

providing a dataset consisting of rows and columns to define a plurality of cells, where each cell has a cell value, which thereby results in a plurality of cell values for the plurality of cells:
extracting, from the dataset, distinct cell values with respect to a first column value of a first column of the columns:
with respect to a second column value of a second column of the columns and a third column value of a third column of the columns, grouping the distinct cell values into groups:
transforming, using a distributed analysis platform, the groups into corresponding rule keys:
storing the rule keys in one or more documents of a database:
receiving, from a user, a natural language question (NLQ) requesting information with respect to the dataset:
determining a classification for the user with respect to the groups:
based on the classification, searching the one or more documents for applicable rule keys associated with the NLQ;
and
based on the applicable rule keys, disregarding results that are responsive to the NLQ, wherein the results are from the rows of the dataset.

7 more elements

Dependencies are inferred from the claim text.

Row level security in natural language question answering

Life of this patent

Row level security in natural language question answering

Life of this patent

Abstract

Claims20