AUTOMATIC CLUSTERING BY TOPIC AND PRIORITIZING ONLNE FEED ITEMS

• US 20160103917A1
• Filed: 10/10/2014
• Published: 04/14/2016
• Est. Priority Date: 10/10/2014
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Talmor, Alon, Brown, Tal Menahem
• Granted Patent: US 10,592,841 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling

Abstract

The technology disclosed relates to presenting important business insights to a sales engineer. In particular, the technology disclosed assembles a set of news feed items for companies of interest to a sales engineer and groups them into topics. It also qualifies some of the news feed items to return or not based on mandatory or prohibited words in the news feed items. Further, it determines a plurality of metric values for each of the returned news feed items that are based on one of a source metric, business metric, company reference metric, social buzz metric, and matched account metric. It then orders the news feed items, based on the determined metric values, with respect to one or more of source reputation, business activity-related vocabulary, company-name mention, social buzz, and correlation with accounts preferred by the sales engineer, and presents the ordered news feed items as business insights about the topics.

Claims

1. A method of automatically prioritizing news feed items, the method including:

assembling a set of news feed items for companies of interest from a plurality of electronic sources for display to a sales engineer and grouping the assembled set into clusters based on topics;

preprocessing the clusters to qualify some of the news feed items to return or not based on one or more occurrences of mandatory or prohibited words in respective titles and descriptions of the news feed items;

determining a plurality of metric values for each of the returned news feed item, wherein the metric values are based on at least one of source metric, business metric, company reference metric, social buzz metric, and matched account metric;

ordering the news feed items, based on the determined metric values, with respect to one or more of source reputation, business activity-related vocabulary, company-name mention, social buzz, and correlation with accounts preferred by the sales engineer; and

presenting, to the sales engineer, the ordered news feed items about the topics.

2. The method of claim 1, wherein grouping the assembled set into clusters further includes:

constructing a graph of the news feed items in the assembled set and representing connection strength as edges between nodes representing the news feed items, thereby forming connected node pairs;

calculating the connection strength between the connected node pairs based on at least one of;

titles of the news feed items,

publication duration of the news feed items, and

target unified resource locators (URLs) of the news feed items; and

grouping the news feed items with connection strength above a threshold into clusters.

3. The method of claim 2, wherein calculating the connection strength between the connected node pairs based on the titles of the news feed items further includes resolving at least one of spelling variations, alternative names, and abbreviated forms in the titles.

4. The method of claim 1, further including:

identifying from the ordered news feed item a representative news feed item with a highest total metric value; and

presenting the representative news feed item to the sales engineer.

5. The method of claim 1, wherein the source metric specifies credibility of the plurality of electronic sources hosting the news feed items.

6. The method of claim 1, wherein the business metric specifies business value of the news feed items based on one or more occurrence of business nomenclature in at least one of respective titles and descriptions of the news feed items.

7. The method of claim 1, wherein the company reference metric specifies definality of one or more mentions in the news feed items as company names based on at least one of:

alternative usage of the mentions;

location of the mentions in at least one of respective titles and descriptions of the news feed items;

letter case of the mentions;

letter case of first character of the mentions; and

subject and predicate analysis of the news feed items.

8. The method of claim 1, wherein the social buzz metric specifies at least one of:

a number of users that endorse respective sources in the plurality of electronic sources on one or more social networking environments; and

a number of times the news feed items have been posted on the social networking environments.

9. The method of claim 1, wherein the matched account metric specifies correlation between one or more customer relation management (CRM) accounts preferred by the sales engineer and the news feed items.

10. The method of claim 1, further including determining a recency metric that specifies a time duration since publication of the news feed items.

11. The method of claim 1, further including determining a title length metric that specifies a number of words in respective titles of the news feed items.

12. The method of claim 1, further including determining a global interest metric that specifies importance of the news feed items based on one or more occurrences of popularity-indicative words in respective titles and descriptions of the news feed items.

13. The method of claim 1, further including determining a dangerous phrases metric that specifies impropriety of the news feed items based on one or more occurrences of offensive words in respective titles and descriptions of the news feed items.

14. The method of claim 1, further including determining a picture metric that specifies content-quality of the news feed items based on one or more occurrences of images in the news feed items.

15. The method of claim 1, further including determining a ticker metric that specifies financial-contextuality of the news feed items based on one or more occurrences of stock stickers in respective titles and descriptions of the news feed items.

16. The method of claim 1, further including calculating cluster metric values for the clusters based on at least one of:

number of insights in respective clusters;

source metric values of insights in respective clusters;

average total metric values of insights in respective clusters; and

average total metric values of a subset of non-representative insights in respective clusters.

17. A system of automatically prioritizing news feed items, the system including:

a processor and a computer readable storage medium storing computer instructions configured to cause the processor to;

assemble a set of news feed items for companies of interest from a plurality of electronic sources for display to a sales engineer and grouping the assembled set into clusters based on topics;

preprocess the clusters to qualify some of the news feed items to return or not based on one or more occurrences of mandatory or prohibited words in respective titles and descriptions of the news feed items;

determine a plurality of metric values for each of the returned news feed item, wherein the metric values are based on at least one of source metric, business metric, company reference metric, social buzz metric, and matched account metric;

order the news feed items, based on the determined metric values, with respect to one or more of source reputation, business activity-related vocabulary, company-name mention, social buzz, and correlation with accounts preferred by the sales engineer; and

present, to the sales engineer, the ordered news feed items about the topics.

18. The system of claim 17, wherein the source metric specifies credibility of the plurality of electronic sources hosting the news feed items.

19. The system of claim 17, wherein the company reference metric specifies definality of one or more mentions in the news feed items as company names based on at least one of:

alternative usage of the mentions;

location of the mentions in at least one of respective titles and descriptions of the news feed items;

letter case of the mentions;

letter case of first character of the mentions; and

subject and predicate analysis of the news feed items.

20. The system of claim 17, wherein the social buzz metric specifies at least one of:

a number of users that endorse respective sources in the plurality of electronic sources on one or more social networking environments; and

a number of times the news feed items have been posted on the social networking environments.