The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 12, 2026

Filed:

Nov. 28, 2022
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Wojciech Aleksander Wilk, Austin, TX (US);

Shannon Kalisky, Dripping Springs, TX (US);

Rishav Chakravarti, White Plains, NY (US);

William Michael Siler, Germantown, TN (US);

Stephen Michael Ash, Seattle, WA (US);

Rajesh Patel, Austin, TX (US);

Joshua Noah Malters, Seattle, WA (US);

Gregory David Adams, Seattle, WA (US);

Jose Kunnackal John, Bellevue, WA (US);

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01); G06N 5/022 (2023.01); G06N 5/04 (2023.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06F 40/295 (2020.01); G06F 40/30 (2020.01); G06N 5/022 (2013.01);
Abstract

This disclosure describes techniques and architecture provide automated contribution analysis for 'why question' style NLQ answering, e.g., “why is revenue down in North America Q1 2022.” In particular, the techniques described herein combine multiple signals together including, for example, frequency of use of combinations of dimensions in previous NLQs (warm-start), statistical information about columns (e.g., entropy), correlation/co-occurrence between pairs of dimension columns, and correlation between dimensions and dates. This information is used with a set of heuristics and rules to pick the best set of dimensions as contributing factors for a particular metric over a particular time period and present an automatic contribution analysis to the users to give them insights into their data.


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