METHOD AND SYSTEM FOR AUTOMATED VERIFICATION OF CUSTOMER REVIEWS

• US 20140258169A1
• Filed: 03/05/2013
• Published: 09/11/2014
• Est. Priority Date: 03/05/2013
• Status: Abandoned Application

• Current Assignee: Bental Wong, Rebekah Wong
• Original Assignee: Bental Wong, Rebekah Wong
• Inventors: Wong, Bental, Wong, Rebekah
• Application Number:
  US13/784,844
• Publication Number:
  US 20140258169A1
• Time in Patent Office: Days
• US Class Current: 705/347
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

Described embodiments provide systems and methods for verifying customer reviews. Certain embodiments evaluate the authenticity of a proof-of-purchase provided by a customer. Other embodiments may provide an electronic service that verifies the authenticity of a proof-of-purchase provided by a customer to document a transaction between the customer and a merchant.

Claims

1. A method to automatically verify a review by evaluating and determining the authenticity of the proof-of-purchase provided, the method comprising:

(a) receiving merchant review verification information at a computer device wherein the review verification information comprises at least one of a digital copy of a proof-of-purchase, meta data associated with a digital copy of a proof-of-purchase and user profile information;

(b) determining model variables based on evaluating at least one of data contained in a digital copy of a proof-of-purchase, meta data contained in a digital copy of a proof-of-purchase or user profile information;

(c) determining a risk score for the review information, wherein the risk score identifies a probability that a proof-of-purchase is fraudulent based on the model variables; and

(d) determining a review verification status for the review by comparing the risk score against business rules.

2. The method of claim 1, wherein review verification information is received from a plurality of websites via a plurality of devices comprising of desktop clients, smart phones and via a plurality of Application Programming Interfaces.

3. The method of claim 1, wherein a proof-of-purchase comprises one of a merchant-issued receipt or a bank-issued account statement.

4. The method of claim 1, wherein evaluating the data contained in the digital copy of the proof-of-purchase comprises:

(a) extracting text content from the digital copy of the proof-of-purchase;

(b) identifying attribute values and text patterns within the extracted proof-of-purchase text;

(c) matching identified attribute values and text patterns to those previously associated with the merchant or the bank that issued the proof-of-purchase;

(d) categorizing the identified attribute values and text patterns by their importance and whether they are indicative of being an authentic proof-of-purchase or indicative of being a falsified proof-of-purchase;

(e) creating model variables by applying data transformation steps to the attribute values and text patterns.

5. The method of claim 1, wherein evaluating the meta data contained in the digital copy of the proof-of-purchase comprises:

(a) receiving meta data information;

(b) matching meta data elements contained on the proof-of-purchase to meta data previously associated with a merchant or a bank;

(c) categorizing the identified meta data elements by whether they are indicative of being an authentic proof-of-purchase or indicative of being a falsified proof-of-purchase;

(d) creating model variables by applying data transformation steps to the meta data elements.

6. The method of claim 1, wherein evaluating the user profile information comprises:

a) receiving user profile information;

b) calculating variables which evaluate the profile;

c) creating model variables by applying data transformation steps to the user profile variables.

7. The method of claim 1, wherein determining the risk score for the review further comprises:

(a) creating contribution values for each model variable by multiplying each model variable by its associated regression coefficient

(b) determining the total contribution value by summing up all of the individual contribution values and the intercept value

(c) converting the total contribution value into a probability score which indicates the probability that the proof-of-purchase is not authentic using the logistic function.