US 12,223,080 B1
Row level security in natural language question answering
Amjad Al-Rikabi, Seattle, WA (US); Stephen Michael Ash, Seattle, WA (US); William Michael Siler, Germantown, TN (US); Rajkumar Haridoss, Kirkland, WA (US); Rajesh Patel, Austin, TX (US); and Kushal Yelamali, Issaquah, WA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Nov. 28, 2022, as Appl. No. 18/070,086.
Int. Cl. G06F 16/00 (2019.01); G06F 16/242 (2019.01); G06F 16/2455 (2019.01); G06F 21/62 (2013.01); G06F 16/13 (2019.01)
CPC G06F 21/6227 (2013.01) [G06F 16/243 (2019.01); G06F 16/2455 (2019.01); G06F 16/24564 (2019.01); G06F 16/13 (2019.01)] 20 Claims
OG exemplary drawing
 
1. 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.