Methods and systems of detection of most relevant insights for large volume query-based social data stream

• US 9,634,909 B2
• Filed: 04/30/2013
• Issued: 04/25/2017
• Est. Priority Date: 04/30/2012
• Status: Active Grant

• Current Assignee: uberVU Ltd. (Hootsuite, Inc.)
• Original Assignee: uberVU Ltd. (Hootsuite, Inc.)
• Inventors: Oane, Vladimir, Sandulescu, Bogdan
• Primary Examiner(s): JEAN, FRANTZ B
• Application Number:
  US13/873,807
• Publication Number:
  US 20130304819A1
• Time in Patent Office: 1,456 Days
• Field of Search: 709206, 709204, 705 729, 707722, 707754
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

The present invention relates to novel methods and implementing systems that afford a user the ability to automatically analyze all mentions and metrics/analytics of any query-based social data stream, and detect mentions or analytics that can be classified as important for a user of that stream.

Claims

1. A system comprising a non-transitory machine-readable medium having instructions stored thereon for execution by a processor to perform a method for detection of the most relevant insights for any large volume query-based social data stream comprising the steps of:

interfacing with one or more streaming social data sources;

collecting one or more mentions that match a search query;

storing said mentions on a first server;

enhancing said mentions with additional information to produce enhanced mentions;

storing said enhanced mentions on a second server;

filtering said enhanced mentions into filtered streams;

aggregating statistical data of said filtered streams; and

analyzing for an anomaly in the statistical data of each of said filtered streams to detect the most relevant insights using a hybrid statistical approach adapted to the volume and steadiness of the data of the filtered streams, such that the most relevant insights for the streaming social data sources are detected and a real-time alert may be generated based on the detected anomaly or insight.

2. The system of claim 1, wherein the instructions stored on the machine-readable medium are online software or offline software.

3. The system of claim 1, wherein the software is an online application.

4. The system of claim 3, wherein the software is a web-based application.

5. The system of claim 3, wherein the software is a cloud-based application.

6. The system of claim 1, wherein the software is an offline application.

7. The system of claim 1, wherein the data sources are selected from the group consisting of social network or websites where an author can be uniquely identified, and any combination thereof.

8. The system of claim 1, wherein the machine-readable medium is selected from the group consisting of magnetic media, punched cards, paper tapes, optical disks, barcodes, magnetic ink characters, and solid state devices.

9. The system claim 1, wherein the method further comprises the step of identifying an anomaly.

10. The system of claim 1, wherein the method further comprises the step of reporting one or more of the most relevant insights.

11. The system of claim 1, wherein the first server is the same as the second server.

12. The system of claim 1, wherein the step of collecting the mentions further comprises establishing a search inquiry.

13. The system of claim 1, wherein the step of collecting the mentions may be limited by prescribed time interval or may be continual.

14. A method for detection of the most relevant insights for any large volume query-based social data stream comprising the steps of:

interfacing with one or more streaming social data sources;

collecting one or more mentions that match a search query;

storing said mentions on a first server;

enhancing said mentions with additional information to produce enhanced mentions;

storing said enhanced mentions on a second server;

filtering said enhanced mentions into filtered streams;

aggregating statistical data of said filtered streams; and

analyzing for an anomaly in the statistical data of each of said filtered streams to detect the most relevant insights using a hybrid statistical approach adapted to the volume and steadiness of the data of the filtered streams, such that the most relevant insights for the streaming social data sources are detected and a real-time alert may be generated based on the detected anomaly or insight.

15. The method of claim 14, wherein the method further comprises the step of identifying an anomaly.

16. The method of claim 14, wherein the method further comprises the step of reporting one or more of the most relevant insights.

17. The method of claim 14, wherein the first server is the same as the second server.

18. The method of claim 14, wherein the step of collecting the mentions further comprises establishing a search inquiry.

19. The method of claim 14, wherein the step of collecting the mentions may be limited by prescribed time interval or may be continual.

20. The method of claim 14, wherein the data sources are selected from the group consisting of social network or websites where an author can be uniquely identified, and any combination thereof.