Search engine optimizer

• US 7,908,263 B1
• Filed: 06/25/2008
• Issued: 03/15/2011
• Est. Priority Date: 06/25/2008
• Status: Active Grant

• Current Assignee: Richard S. Paiz
• Original Assignee: Richard S. Paiz
• Inventors: Paiz, Richard S
• Primary Examiner(s): Wong; Don
• Assistant Examiner(s): Nguyen; Merilyn P
• Application Number:
  US12/146,420
• Time in Patent Office: 993 Days
• Field of Search: 707/706, 707/707, 707/708, 707/720, 707/737, 707/752, 707/776, 707/778
• US Class Current: 707/708
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A search engine optimizer that works independently and in parallel with a browser and search engine supercomputer to gather, analyze, and distill input information interactively. The optimizer reorganizes the input, and providing an optimized version as an output. The optimized version of the input (e.g. output) is sent to the search engine, which responds to the end user with search results. The optimizer recognizes each request as a pattern and stores the pattern in an advanced Glyph format. This permits the optimizer to identify a left and right side checkmate combination required to achieve certitude.

Claims

1. An Internet search engine method mimicking the function of the human brain by creating a language-based (left brain) equation and a geospatial (right brain) equation, for buffering between an Internet browser and a parallel distributed search engine supercomputer to interpret numerical and textual data and convert the interpreted data into statistical Glyph objects representing the mathematical equations used to obtain an optimal match in a search response to an end user search request, the method comprising the steps of:

receiving a plurality of consecutive end user search requests in the form of at least one keyword;

interactively interpreting and parsing said at least one keyword to determine whether or not said keyword exists within a master keyword database and whether said keyword matches any data in the database;

analyzing and priming the raw and unmanaged Internet as an Internet environment and then assigning a rank to each web page from lowest to highest;

validating said at least one keyword and deriving a list of associated keywords and common known keyword clusters within said database to yield an improved search pattern;

verifying said at least one keyword in the request and determining whether or not said at least one keyword will function as a common denominator or as an independent variable that can aid in reducing the size of the search environment;

processing the impact of a managerial hierarchical related group of independent variables to create an equation that substantially shrinks the size of the search environment by parsing the search request into primary, secondary and tertiary tiers or managerial hierarchical equations;

numerically determining the significance in a session;

mimicking a dynamic hierarchical set of filters in the form of Basic Glyphs that best reflect existing search engine capacities;

reorganizing and improving the end user search request to create Advanced Glyphs improved hierarchical set of filters to further distill and shrink the size of the search environment using existing search engine capacities;

converting human keywords into a logical sequence of words or numbers that yield an optimal hierarchical set;

probabilistically mapping geospatial information, such as continents, country, state, city, zip code, telephone number ranges and IP Addresses;

standardizing and transforming said search response into vector components;

mapping each response into Super Pages that include probabilities and calculated weights of a plurality of user session requests;

associating said Super Pages to Super Sites that merge the plurality of information contained in related Super Pages;

plotting the Super Pages and Super Sites into Super Glyphs that contain the dynamic characteristics of each keyword and cluster as the end user continues an Internet search, thereby transforming said Super Glyphs into statistical objects;

correlating a plurality of spatial environments into a single improved probabilistic environment;

dynamically adjusting the value of each keyword and cluster based on the end user pattern of behavior;

disabling identified Zero Cluster keywords or confounding elements of a search;

determining common denominator words using rules of association to define a final equation;

deemphasizing weak usage patterns and words that are determined to be unnecessary in aiding in the determination of the final equation;

maximizing keyword values by dynamically measuring the relevance of a keyword in a user search session based on usage patterns;

minimizing keyword values by dynamically measuring the irrelevancy of a keyword during a search session based on usage patterns;

organizing and distributing the Internet environment into ten concurrent quality partitions, labeled from one to ten, with one representing confounded information and ten representing optimal site information based upon existing page ranking capacities;

further dynamically reducing the improved environmental size, following the steps of maximizing and minimizing keyword values, based upon usage patterns, then matching and merging top responses into Super Site and Super Page objects that yield an optimal statistical Super Glyph equation;

filtering statistical objects based upon significance quality levels or values;

assigning a corporate signature to each Super Site object;

mapping, plotting and rendering a plurality of existing information into a informational corporate managerial data structure that binds together any related information concerning a particular company;

distilling the Internet environment in order to create supersets (i), secondary sets (i, j), and tertiary subsets (i, j, k) of managerial hierarchical partitions and weighing each Super Site through the use of precise geospatial dimensions using latitude and longitude coordinates;

gathering, analyzing and distilling a best response from each global, regional and local scope analysis node to create a limited size collection of valid Super Pages, to define a dynamic and optimal environments;

selecting optimal responses from the dynamic optimal environment, wherein the top responses of each dynamic and improved hierarchical set are used to generate a limited collection of valid Super Pages displayed within existing browser capacities;

commercializing each keyword and associating media value resources and priorities to Super Sites and Super Pages;

deciphering and solving and optimal Super Glyph mathematical equation using deductive reasoning;

translating the end user'"'"'s language based request into a single Super Glyph equation;

using the Super Glyph equation to obtain information from any give language by using math in lieu of linguistics;

consolidating all related and significant requests into a resultant environment that contains all of the relevant and corresponding dynamic and improved hierarchical set to create an outcome optimal hierarchical sets;

saving and storing said newly-identified Super Glyph equation into the master database; and

,

responding to existing browser capacities with said optimal response, wherein the best set of results are displayed in a prioritized order.

2. The method of claim 1, further comprising the step of scanning each request and determining based on the quality of the keyword combination whether at least one of the supersets (i), the secondary sets (i, j), and the tertiary subsets (i, j, k) of the managerial hierarchical partition exists in the Super Glyph database, and thus having readily available and pre-processed the optimal response, requiring no further calculations, bypassing the usage of existing search engine capabilities.

3. The method of claim of claim 1, further comprising the step of probabilistically inserting into each Super Page keyword information known to exist in a parent Super Page.

4. The method of claim 1, further comprising the step of assigning to each Super Site a w_rank basis value.

5. The method of claim 4, further comprising the step of assigning to each Super Page a w_rank basis value, based on the parent Super Site related quality partition value within the Internet environment.

6. The method of claim 1, further comprising the step of shrinking the Internet environment using existing search engine capabilities by dynamically assigning keyword values and clusters to shrink the dynamic environment size.

7. The method of claim 6, further comprising the step of optimally reorganizing the end user'"'"'s typed keywords to further shrink the dynamic environment size.

8. The method of claim 1, further comprising the steps of shrinking the Internet environment by using unused keywords, mulligan keywords, and minimizing keyword values to further shrink the size of the improved environment.

9. The method of claim 1, further comprising the step of converting the keywords, clusters and geospatial information, into a probabilistic dynamic mathematical Super Glyph equation.

10. The method of claim 1, further comprising the step of sorting the optimal responses in descending order to yield the optimal result, wherein the top responses has the highest value and most accurate.

11. The method of claim 1, further comprising the step of physically parsing and data mining only the optimal size environment, and then certifying that the results to be displayed to the end user are valid, or are not confounded.

12. The method of claim 1, further comprising the step of identifying optimal results and storing the results within the database.

13. The method of claim 12, further comprising the step of programming computer spiders to gather, analyze and distill, the optimal environment by continuously identifying, verifying and updating, the content of the Internet, to maintain the database information synchronized and up-to-date in real time.

14. The method of claim 1, further comprising the step of making searches using Super Glyph mathematical equations, wherein each keyword value dynamically changes during a search session based upon usage patterns and analysis of previously optimal results.

15. The method of claim 14, further comprising the step of replacing the keywords, clusters and geospatial information, into a Super Glyph equation that can search regardless of language for an optimal dynamic environmental response.

16. The method of claim 1, further comprising the step of assigning a corporate value to each Super Site based available corporate media while calculating an optimal dynamic environmental response.

17. The method of claim 16, further comprising the step of assigning a client side commercial value based on the end users pattern of behavior, permitting existing supercomputer capabilities to display a highest value advertisement to the end user.

18. The method of claim 16, further comprising the step of maximizing an advertisement value of an end user request by being able to adjust the commercial media value of each advertisement based upon supply side economics by taking into account end user profile, usage patterns, and geospatial-based areas of influence for an advertisement.

19. The method of claim 18, further comprising the step of dynamically solving for the media value of an advertisement based upon the corporate value of a given geographical area, such as a zip code.

20. The method of claim 1, further comprising the step of dynamically solving for the media value of an advertisement based upon the frequency quantity of times an actual corporate sponsor has interacted with a given consumer (perceived freshness that the advertisement will be to the final consumer).