Systems and methods for coverage analysis of textual queries

Patent metadata

Field	Value
Publication number	US-2020342016-A1
Application number	US-201916391761-A
Country	US
Kind code	A1
Filing date	Apr 23, 2019
Priority date	Apr 23, 2019
Publication date	Oct 29, 2020
Grant date	—

Abstract

A computer based system and method for assigning queries to topics and/or visualizing or analyzing query coverage may include, using a computer processor, searching, using a set of queries, over a set of text documents, to produce for each query a set of search results for the query. Each search result may include a subset of text from a text document of the set of text documents. For each query, a query vector may be calculated based on the set of search results for the query, and for each of a set of topics describing the set of text documents, a topic vector may be calculated. A report or visualization may be generated including the set of queries and the set of topics using the topic vectors and the query vectors.

First claim

What is claimed is:

1 . A method for assigning queries to topics, comprising, using a computer processor:
searching, using a set of queries, over a set of text documents, to produce for each query a set of search results for the query, each search result comprising a subset of text from a text document of the set of text documents; for each query, calculating a query vector based on the set of search results for the query; for each of a set of topics describing the set of text documents, calculating a topic vector; and generating a visualization of the set of queries and the set of topics using the topic vectors and the query vectors.

2 . The method of claim 1 , comprising calculating the query vector by:
for each phrase in a set of phrases calculating a phrase vector for the phrase; and calculating the query vector based on the phrase vectors.

3 . The method of claim 2 , comprising calculating the phrase vectors using a neural network.

4 . The method of claim 2 , wherein each phrase vector is based on a word embedding model and measures the linguistic context of the associated phrase.

5 . The method of claim 1 , wherein the text documents comprise transcripts generated by ASR (automatic speech recognition).

6 . The method of claim 1 , comprising generating each topic vector by clustering phrases extracted from the set of text documents into topics and for each topic calculating the topic vector as the centroid of the topic.

7 . The method of claim 1 , comprising determining for each topic if the topic is covered by a query.

8 . A system for voice authentication of an audio stream represented to be the spoken voice of a person, comprising:
a memory and; a processor configured to:
search, using a set of queries, over a set of text documents, to produce for each query a set of search results for the query, each search result comprising a subset of text from a text document of the set of text documents;
for each query, calculate a query vector based on the set of search results for the query;
for each of a set of topics describing the set of text documents, calculate a topic vector; and
generate a visualization of the set of queries and the set of topics using the topic vectors and the query vectors.

9 . The system of claim 8 , wherein the processor is configured to calculate the query vector by:
for each phrase in a set of phrases calculating a phrase vector for the phrase; and calculating the query vector based on the phrase vectors.

10 . The system of claim 9 , wherein the processor is configured to calculate the phrase vectors using a neural network.

11 . The system of claim 9 , wherein each phrase vector is based on a word embedding model and measures the linguistic context of the associated phrase.

12 . The system of claim 8 , wherein the text documents comprise transcripts generated by ASR (automatic speech recognition).

13 . The system of claim 8 , wherein each query vector is based on a word embedding model and measures the linguistic context of the set of search results.

14 . The system of claim 8 , wherein the processor is configured to determine for each topic if the topic is covered by a query.

15 . A method for analyzing queries, comprising, using a computer processor:
applying one or more queries to documents, to produce query search results; calculating a query embedding for each query; creating clusters based on the documents; creating an embedding for each cluster; and determining for each cluster if the cluster is covered by a query using the cluster embedding and the query embeddings.

16 . The method of claim 15 , comprising calculating the query embedding by:
for each phrase in a set of phrases calculating a phrase embedding for the phrase; and calculating the query embedding based on the phrase embeddings.

17 . The method of claim 16 , comprising calculating the phrase embeddings using a neural network.

18 . The method of claim 16 , wherein each phrase embedding is based on a word embedding model and measures the linguistic context of the associated phrase.

19 . The method of claim 15 , wherein the documents comprise transcripts generated by ASR (automatic speech recognition).

20 . The method of claim 15 , wherein each query embedding is based on a word embedding model and measures the linguistic context of the query search results.

Assignees

• Nice Ltd

Inventors

• Morris Robert William

Classifications

• G06N20/00

  Machine learning · CPC title

• G06N3/08

  Learning methods · CPC title

• G06F16/338

  Presentation of query results · CPC title

• G06F40/30 Primary

  Semantic analysis · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title