REVERSE SEARCH DATABASE SYSTEM

• US 20140089102A1
• Filed: 09/25/2013
• Published: 03/27/2014
• Est. Priority Date: 09/25/2012
• Status: Abandoned Application

• Current Assignee: Richard A. Ferdinand
• Original Assignee: Richard A. Ferdinand
• Inventors: Ferdinand, Richard A.
• Application Number:
  US14/036,681
• Publication Number:
  US 20140089102A1
• Time in Patent Office: Days
• US Class Current: 705/14.66
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

Disclosed herein are methods useful for reverse-searching, and categorizing consumer markets and preferences according to consumer and/or viewer interaction, in any type of display format, which in turn creates consumer and/or viewer interaction data. The data is then used to process the reverse-search within a database. The methods generally can be utilized for commercial purposes, by a business, such as for advertising or solicitation to the viewer. Also disclosed herein is the incorporation of the Pre-Search Query Label Stack into the reverse-search which improves the quality and reliability of the reverse-search. The methods can provide new and improved commercial success in advertising, education, training, branding, promotional activity, notice, offer and solicitation by the business to the consumer or viewer.

Claims

1. A method of using viewer data in order to reverse-search consumer analytics in a database comprising the steps of:

(a) utilizing commercial value of words and expressions in any language that relates to information, wherein in said information includes advertising, education, training, branding, promotional activity, notice, offer or solicitation;

(b) providing said information in any display format to a viewer;

(c) displaying said information in order for the viewer to interact with the information which creates a viewer'"'"'s data;

(d) using said viewer'"'"'s data and conducting a reverse-search, within a database, for any reason, including for viewer'"'"'s preferences, tastes, marketing and advertising purposes; and

(e) presenting said viewer, in any display format, with potentially inclined products and services based on the results of said reverse-search.

2. A method according to claim 1, further comprising the additional step of:

(a) creating a Pre-Search Query Label; and

(b) including said Pre-Search Query Label Stacks to a semantic library within the information and the reverse-search system.

3. A method according to claim 2, further comprising the additional step of scoring the contents of the Pre-Search Query Label Stacks by database analyst in relation to probable and possible commercial value.

4. A method according to claims 1, further comprising the additional steps of:

(a) scoring the semantic contents of the Pre-Search Query Label Stacks in relation to probable and possible commercial value; and

(b) using a computerized flagging, detecting and recording system that has access to a database of viewers, including but not limited to all active and issued ARC, anonymous recognition codes, that are equipped to scan all physical and cyber panel locations on said system for relevant data, wherein said relevant data includes extrinsic contextual data.

5. A method according to claim 4, further comprising the additional steps of:

(a) correlating the contents of all Pre-Search Query Label Stacks;

(b) identifying potentially hidden relationships;

(c) exploiting said relationships for commercial purposes; and

(d) updating said semantic library in accordance with the scoring of any commercial values in the newly discovered hidden relationships.

6. A method according to claim 4, further comprising the additional steps of:

(a) utilizing automatically the scores of the semantic contents of the Pre-Search Query Label Stacks;

(b) creating profiles of likely new advertisers;

(c) populating said profiles with automatically generated predictive models of performance;

(d) using said predictive models in order to attract advertisers as new customers to said system; and

(e) creating automatic pricing, dispersion, and circulation models for the new customers according to needs.

7. A method according to claim 4, further comprising the additional steps of:

(a) correlating automatically the contents of the Pre-Search Query Label Stacks;

(b) identifying relationships within the contents;

(c) creating a entertaining computerized educational and commercial training system;

(d) offering said system to viewers in a panelized electronic presentation format wherein a schedule of interests and preferences of individuals and groups are pre-recorded in Pre-Search Query Label Stacks;

(e) mining said Pre-Search Query Label Stacks for data; and

(f) incorporating said system with educational and training course contents.