Method and system for measuring individual prescience within user associations

• US 8,930,234 B2
• Filed: 03/24/2011
• Issued: 01/06/2015
• Est. Priority Date: 03/23/2011
• Status: Active Grant

• Current Assignee: Ipar LLC
• Original Assignee: Ipar LLC
• Inventors: Spears, Joseph L., Brandstetter, Jeffrey D.
• Primary Examiner(s): Dwivedi, Mahesh
• Application Number:
  US13/070,548
• Publication Number:
  US 20120245887A1
• Time in Patent Office: 1,384 Days
• Field of Search: 705/7.29, 705/7.31, 707/748
• US Class Current: 705/7.29
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/90324 using system suggestions
• CPC Class Codes: G06Q 2220/18 Licensing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Systems and methods are provided for identifying an influencer from a pool of people based on past interactions with one or more items. A popularity metric is determined for a particular item, and an earliness of interaction metric is determined for a person in the pool based on an interaction with the particular item by the person. A prescience metric is calculated for the person based on the popularity metric and the earliness of interaction metric for the person, and the influencer is identified based on the prescience metric for the person and prescience metrics calculated for other people in the pool.

Claims

1. A computer-implemented method of identifying an influencer from a pool of people based on past interactions with one or more items, comprising:

determining a popularity metric for a particular item;

determining an earliness of interaction metric for a person in the pool based on an interaction with the particular item by the person;

calculating a prescience metric for the person based on the popularity metric and the earliness of interaction metric for the person, wherein the prescience metric is calculated according to;

U_Prescience=(P_After/P_Total)*(S_Item/S_Pool)

where U_Prescience is the prescience metric for the person, P_After is a number of interactions with the particular item after the interaction with the particular item by the person, P_Total is a total number of interactions with the particular item, S_Item is a number of sales of the item, and S_Pool is a number of sales of items in a pool of items; and

identifying the influencer based on the prescience metric for the person and prescience metrics calculated for other people in the pool.

2. The method of claim 1, wherein the earliness of interaction metric is determined based on a time of interaction with the particular item for the person and times of interactions for other people in the pool.

3. The method of claim 1, wherein the earliness of interaction metric is determined based on a total number of interactions with the particular item and a number of interactions with the particular item subsequent to the interaction with the particular item by the person.

4. The method of claim 1, wherein the interaction with the particular item involves purchasing the item, liking or otherwise showing approval for the item, clicking on a link associated with the item, giving a rating of the item, providing feedback regarding the item, concurring with the interaction of another user for the item, or using the item.

5. The method of claim 1, wherein the interaction with the particular item involves disliking or otherwise showing disapproval of the item, removing an item from a page, disabling or otherwise disregarding the item, or reporting the item as inappropriate.

6. The method of claim 1, wherein the item is a digital good, a physical good, or a service.

7. The method of claim 6, wherein the digital good contains a video component, an audio component, a text component, or a game component.

8. The method of claim 1, wherein the pool of people comprises an association of people.

9. The method of claim 8, wherein the association is a target association to whom a second item is introduced.

10. The method of claim 9, wherein the second item is introduced to the influencer before the second item is introduced to other people in the target association.

11. The method of claim 10, wherein the influencer is provided the second item to facilitate introducing of the second item by the influencer.

12. The method of claim 10, wherein the influencer is provided an incentive to introduce the second item to other people in the target association.

13. A computer-implemented system for identifying an influencer from a pool of people based on past interactions with one or more items, comprising:

one or more data processors;

a non-transitory computer-readable medium encoded with instructions for commanding the data processor to execute steps that include;

determining a popularity metric for a particular item;

determining an earliness of interaction metric for a person in the pool based on an interaction with the particular item by the person;

calculating a prescience metric for the person based on the popularity metric and the earliness of interaction metric for the person, wherein the prescience metric is calculated according to;

U_Prescience=(P_After/P_Total)*(S_Item/S_Pool)

where U_Prescience is the prescience metric for the person, P_After is a number of interactions with the particular item after the interaction with the particular item by the person, P_Total is a total number of interactions with the particular item, S_Item is a number of sales of the item, and S_Pool is a number of sales of items in a pool of items; and

identifying the influencer based on the prescience metric for the person and prescience metrics calculated for other people in the pool.

14. A non-transitory computer-readable medium encoded with instructions for commanding one or more data processors to execute a method of identifying an influencer from a pool of people based on past interactions with one or more items, the method comprising:

determining a popularity metric for a particular item;

determining an earliness of interaction metric for a person in the pool based on an interaction with the particular item by the person;

calculating a prescience metric for the person based on the popularity metric and the earliness of interaction metric for the person, wherein the prescience metric is calculated according to;

U_Prescience=(P_After/P_Total)*(S_Item/S_Pool)

wherein U_Prescience is the prescience metric for the person, P_After is a number of interactions with the particular item after the interaction with the particular item by the person, P_Total is a total number of interactions with the particular item, S_Item is a number of sales of the item, and S_Pool is a number of sales of items in a pool of items; and

identifying the influencer based on the prescience metric for the person and prescience metrics calculated for other people in the pool.