Discovery of new business openings using web content analysis

Patent metadata

Field	Value
Publication number	US-12175483-B2
Application number	US-202318365775-A
Country	US
Kind code	B2
Filing date	Aug 4, 2023
Priority date	Mar 12, 2013
Publication date	Dec 24, 2024
Grant date	Dec 24, 2024

Abstract

In general, embodiments of the present invention provide systems, methods and computer readable media for identifying a new business based on programmatically analyzing content received from online sources and, as a result, discovering one or more references to the business. In embodiments, the system stores historical data representing previously identified new businesses and then uses attributes of those businesses in search queries to receive related content. Additionally or alternatively, the system stores data representing online sources that historically provided content containing references to new businesses and then continues to access those sources for additional content. In embodiments, the system performs content analysis on structured and/or unstructured content. In some embodiments, analysis of content received from a particular online source includes a source-specific algorithm that takes a source-specific representation of the content as input and produces a result indicating the likelihood that the content includes a new business reference.

First claim

That which is claimed:

1. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to:
receive content data from an online source;
determine, based at least in part on parsing the content data, that the content data contains one or more provider references associated with a provider that is a new business;
responsive to determining that data representing the new business is not stored in a repository, store the data in the repository; and
generate a confidence rating associated with the online source based at least in part on the one or more provider references and whether the new business was verified; and
update a source search index based at least in part on the confidence rating.

2. The apparatus of claim 1 , wherein the parsing is based at least in part on a pattern recognition algorithm.

3. The apparatus of claim 2 , wherein the pattern recognition algorithm is selected based in part on determining whether the content data is structured content or unstructured content.

4. The apparatus of claim 2 , wherein the pattern recognition algorithm is a trainable function generated using machine learning.

5. The apparatus of claim 1 , wherein the content data is received based at least in part on the source search index.

6. The apparatus of claim 1 , wherein the source search index is updated based at least in part on based on source data quality signals.

7. The apparatus of claim 1 , further caused to maintain the source search index by pruning the source search index to remove online sources that have not included any further new business references within a predetermined period of time.

8. A non-transitory computer readable storage medium including computer program code that, when executed by a processor of an apparatus, cause the apparatus to:
receive content data from an online source;
determine, based at least in part on parsing the content data, that the content data contains one or more provider references associated with a provider that is a new business;
responsive to determining that data representing the new business is not stored in a repository, store the data in the repository; and
generate a confidence rating associated with the online source based at least in part on the one or more provider references and whether the new business was verified; and
update a source search index based at least in part on the confidence rating.

9. The non-transitory computer readable storage medium of claim 8 , wherein the parsing is based at least in part on a pattern recognition algorithm.

10. The non-transitory computer readable storage medium of claim 9 , wherein the pattern recognition algorithm is selected based in part on determining whether the content data is structured content or unstructured content.

11. The non-transitory computer readable storage medium of claim 9 , wherein the pattern recognition algorithm is a trainable function generated using machine learning.

12. The non-transitory computer readable storage medium of claim 8 , wherein the content data is received based at least in part on the source search index.

13. The non-transitory computer readable storage medium of claim 8 , wherein the source search index is updated based at least in part on based on source data quality signals.

14. The non-transitory computer readable storage medium of claim 8 , wherein the apparatus is further caused to maintain the source search index by pruning the source search index to remove online sources that have not included any further new business references within a predetermined period of time.

15. A computer-implemented method, comprising:
receiving content data from an online source;
determining, based at least in part on parsing the content data, that the content data contains one or more provider references associated with a provider that is a new business;
responsive to determining that data representing the new business is not stored in a repository, storing the data in the repository;
generating a confidence rating associated with the online source based at least in part on the one or more provider references and whether the new business was verified; and
updating a source search index based at least in part on the confidence rating.

16. The computer-implemented method of claim 15 , wherein the parsing is based at least in part on a pattern recognition algorithm.

17. The computer-implemented method of claim 16 , wherein the pattern recognition algorithm is selected based in part on determining whether the content data is structured content or unstructured content.

18. The computer-implemented method of claim 16 , wherein the pattern recognition algorithm is a trainable function generated using machine learning.

19. The computer-implemented method of claim 15 , wherein the content data is received based at least in part on the source search index.

20. The computer-implemented method of claim 15 , wherein the source search index is updated based at least in part on based on source data quality signals.

Assignees

• Bytedance Inc

Inventors

• Jeffery Shawn Ryan
• Pendar Nick
• Barber Richard Clark

Classifications

• G06Q10/0637

  Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals · CPC title

• G06F16/951

  Indexing; Web crawling techniques · CPC title

• G06Q30/0201 Primary

  Market modelling; Market analysis; Collecting market data · CPC title