Systems and methods for resolving entity data across various data structures
US-11061874-B1 · Jul 13, 2021 · US
US2022067427A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2022067427-A1 |
| Application number | US-202017006384-A |
| Country | US |
| Kind code | A1 |
| Filing date | Aug 28, 2020 |
| Priority date | Aug 28, 2020 |
| Publication date | Mar 3, 2022 |
| Grant date | — |
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
What the patent document calls the invention.
A short plain-language summary of the technical disclosure.
Who owns or filed the patent and who is credited as inventor.
Filing, priority, publication, and grant dates set the timeline.
The legal scope of protection — read this for what is actually claimed.
Technology tags used to group this patent with similar filings.
Prior art links and similar publications in this corpus.
Official abstract text for this publication.
A method, system, and computer-usable medium for streaming or processing data streams. Raw text data is cleansed to a standard format. A fuzzy matching algorithm is performed on the text data. For data where domain expertise is required, alias matching is performed. End state categorizing or grouping is provided for the cleansed raw text data.
Opening claim text (preview).
What is claimed is: 1 . A computer-implementable method for streaming data streams comprising: cleansing raw text data of the data streams; applying a fuzzy matching algorithm on cleansed raw text data to determine an end state category of the cleansed raw text data; performing alias matching on the cleansed raw text data if domain expertise is determined to be used for cleansed raw text data; and providing end state categorizing of the cleansed raw text data. 2 . The method of claim 1 , wherein a configurable software template of a data management platform determines processes that are implemented for the streaming data streams. 3 . The method of claim 1 , wherein the raw text data is received from different data sources. 4 . The method of claim 1 , wherein the fuzzy matching algorithm is Levenshtein edit distance. 5 . The method of claim 1 , wherein the fuzzy matching algorithm implements a confidence level threshold. 6 . The method of claim 1 , wherein the alias matching implements a lookup table. 7 . The method of claim 1 , wherein a best match is performed for the fuzzy matching and alias matching. 8 . A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: applying a fuzzy matching algorithm on cleansed raw text data to determine an end state category of the cleansed raw text data; performing alias matching on the cleansed raw text data if domain expertise is determined to be used for cleansed raw text data; and providing end state categorizing of the cleansed raw text data. 9 . The system of claim 8 , wherein a configurable software template of a data management platform determines processes that are implemented for the streaming data streams. 10 . The system of claim 8 , wherein the raw text data is received from different data sources. 11 . The system of claim 8 , wherein the fuzzy matching algorithm is Levenshtein edit distance. 12 . The system of claim 8 , wherein the fuzzy matching algorithm implements a confidence level threshold. 13 . The system of claim 8 , wherein the alias matching implements a lookup table. 14 . The system of claim 8 , wherein a best match is performed for the fuzzy matching and alias matching. 15 . A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: cleansing raw text data of the data streams; applying a fuzzy matching algorithm on cleansed raw text data to determine an end state category of the cleansed raw text data; performing alias matching on the cleansed raw text data if domain expertise is determined to be used for cleansed raw text data; and providing end state categorizing of the cleansed raw text data. 16 . The non-transitory, computer-readable storage medium of claim 15 , wherein a configurable software template of a data management platform determines processes that are implemented for the streaming data streams. 17 . The non-transitory, computer-readable storage medium of claim 15 , wherein the raw text data is received from different data sources. 18 . The non-transitory, computer-readable storage medium of claim 15 , wherein the fuzzy matching algorithm is Levenshtein edit distance. 19 . The non-transitory, computer-readable storage medium of claim 15 , wherein the fuzzy matching algorithm implements a confidence level threshold. 20 . The non-transitory, computer-readable storage medium of claim 15 wherein the alias matching implements a lookup table.
Matching criteria, e.g. proximity measures · CPC title
Data stream processing; Continuous queries · CPC title
Fuzzy inferencing · CPC title
Machine learning · CPC title
Text processing (natural language analysis G06F40/20; semantic analysis G06F40/30; processing or translation of natural language G06F40/40) · CPC title
Related publications grouped by family.
Answers are generated from the same data shown on this page.