User commentary systems and methods

• US 9,301,015 B2
• Filed: 12/03/2014
• Issued: 03/29/2016
• Est. Priority Date: 08/04/2011
• Status: Expired due to Fees

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Lenahan, Michael George, Johnson, Amanda, Mitchell, Ben, Chung, Chahn, Young, Linda
• Primary Examiner(s): Arjomandi, Noosha
• Application Number:
  US14/559,661
• Publication Number:
  US 20150089529A1
• Time in Patent Office: 482 Days
• Field of Search: 707/736
• US Class Current: 1/1
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0623 Item investigation
• CPC Class Codes: H04N 21/2542 for selling goods, e.g. TV shopping payment schemes, payment architectures or payment protocols for electronic shopping systems G06Q20/12
• CPC Class Codes: H04N 21/41265 having a remote control device for bidirectional communication between the remote control device and client device unidirectional remote control device H04N21/42204
• CPC Class Codes: H04N 21/42208 Display device provided on the remote control
• CPC Class Codes: H04N 21/4312 involving specific graphical features, e.g. screen layout, special fonts or colors, blinking icons, highlights or animations
• CPC Class Codes: H04N 21/4394 involving operations for analysing the audio stream, e.g. detecting features or characteristics in audio streams arrangements characterised by components specially adapted for monitoring, identification or recognition of audio in broadcast systems H04H60/58
• CPC Class Codes: H04N 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 21/4758 for providing answers, e.g. voting
• CPC Class Codes: H04N 21/47815 Electronic shopping payment schemes, payment architectures or payment protocols for electronic shopping systems G06Q20/12
• CPC Class Codes: H04N 21/4788 communicating with other users, e.g. chatting arrangements for providing for computer conferences, e.g. chat rooms, to substation in data switching networks H04L12/1813; distributed application using peer-to-peer [P2P] networks H04L67/104
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73

Abstract

Systems and methods to display content are described. In some embodiments, program content is received from a content source. A method identifies product-related metadata associated with the program content where the product-related metadata includes at least one item displayed in the program content. The program content is communicated to a display device for presentation to a user in a first portion of the display device. The product-related metadata is communicated to the display device for presentation to the user in a second portion of the display device.

Claims

1. A method for generating a composite rating about a first user, the method comprising:

receiving a plurality of ratings about the first user from a plurality of second users, each of the ratings associated with a transaction between the first user and one of the second users;

classifying, using at least one processor, the first user based on a number of the transactions;

determining the second users that submitted the ratings from the received plurality of ratings that satisfy a pre-determined profile, the pre-determined profile being in accordance with the classification of the first user;

adjusting the ratings from at least one of the second users that satisfy the pre-determined profile and which exceed a specific rating on a ratings scale comprising a lowest value rating and a highest value rating; and

generating the composite rating about the first user using the adjusted ratings and remaining ratings from the received plurality of ratings.

2. The method of claim 1, wherein the first user comprises a seller and the second users comprise buyers.

3. The method of claim 1, wherein classifying the first user comprises classifying the first user as a high volume ratee or a low volume ratee.

4. The method of claim 3, wherein the first user is a low volume ratee and the pre-determined profile comprises one of the second user contributing more than 50% of the ratings that match a specific rating from among all ratings in the received plurality of ratings that match the specific rating.

5. The method of claim 3, wherein the first user is a high volume ratee and the pre-determined profile comprises more than one of the second users combined contributing more than 50% of the ratings that match a specific rating from among all ratings in the received plurality of ratings that match the specific rating.

6. The method of claim 1, wherein the plurality of ratings comprise the ratings for a first metric pertaining to the transactions.

7. The method of claim 6, further comprising:

receiving a plurality of second ratings about the first user from a plurality of second users, wherein each of the second ratings is associated with a second metric of the transactions between the first user and one of the second users, the first metric and the second metric being different from each other;

adjusting the second ratings from at least one of the second users that satisfy the pre-determined profile and which exceed the threshold; and

generating a second composite rating about the first user using the adjusted second ratings and the remaining second ratings.

8. The method of claim 7, further comprising:

selecting between the composite rating and the second composite rating for an overall composite rating about the first user.

9. The method of claim 8, further comprising:

setting an average of the plurality of ratings for the first metric as the overall composite rating when the composite rating is selected over the second composite rating and the composite rating is less than the average of the plurality of ratings for the first metric.

10. A non-transitory computer readable medium including instructions, when executed by a processor, causes the processor to perform operations comprising:

calculating a contribution amount of specific ratings from a second user to all ratings from a plurality of second users, wherein the ratings from the plurality of the second users pertain to a performance metric of a high volume first user and the specific ratings are ratings having a particular value from a ratings scale and wherein the calculating of the contribution amount comprises calculating the contribution amount of the specific ratings from more than one of the second users with the highest counts of the specific ratings;

in response to the contribution amount exceeding a threshold value, changing a count of the specific ratings from the second user to an another count; and

calculating a composite rating for the performance metric of the first user based on the another count of the specific ratings and the ratings of the remaining second users.

11. The computer readable medium of claim 10, wherein the specific ratings is at least a lowest ratings value from the ratings scale.

12. The computer readable medium of claim 10, wherein the specific ratings is at least a highest ratings value from the ratings scale.

13. The computer readable medium of claim 10, wherein the another count is a single count.

14. The computer readable medium of claim 10, further comprising classifying the first user as a low volume user or a high volume user based on a transaction count.

15. The computer readable medium of claim 10, wherein the ratings comprises at least one of a feedback, an evaluation, a review, an opinion, a testimonial, or a commentary.

16. A system comprising:

a non-transitory storage device including a plurality of feedbacks about a first user from a plurality of second users, each of the feedback associated with a transaction between the first user and the one of the second users; and

a computing device in communication with the storage device and configured to;

classify the first user based on a number of the transactions,

determine the second users that submitted the feedbacks that satisfy a pre-determined profile, the pre-determined profile being in accordance with a classification of the first user,

adjust the feedbacks from at least one of the second users that satisfy the pre-determined profile and which exceed a specific rating on a ratings scale comprising a lowest value rating and a highest value rating, and

generate a composite feedback about the first user using the adjusted feedbacks and remaining feedbacks in the storage device.