Codex search patterns

• US 8,386,456 B1
• Filed: 05/21/2010
• Issued: 02/26/2013
• Est. Priority Date: 02/24/2000
• Status: Expired due to Fees

• Current Assignee: Richard Paiz
• Original Assignee: Richard Paiz
• Inventors: Paiz, Richard
• Primary Examiner(s): Somers, Marc
• Application Number:
  US12/785,122
• Time in Patent Office: 1,012 Days
• Field of Search: 707/705, 707/706, 707/711, 707/736, 707/741
• US Class Current: 707/706
• 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 that 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 ride side checkmate combination required to achieve certitude.

Claims

1. An Internet search engine method mimicking the function of the human brain creating a language-based, left brain equation and a geospatial, right brain equation, for buffering between an Internet browser and a search engine supercomputer to interpret numerical and textual data and convert the interpreted data into statistical glyph objects representing the mathematical equation used to determine the optimal partition of the Internet;

then finds and supplies missing gaps of information, dynamically valorizes, reorganizes, and hierarchically prioritizes the glyphs as a search pattern used to obtain an optimal match in response to an end user valid request and stores them into a codex that serves as knowledge and customer satisfaction inventory control encyclopedia of human ideas, the method comprising the steps of;

providing a word database comprising each recognized word and common denominator words respective to a specific language;

providing a glyph database with statistical and vector components for each recognized word;

providing a codex page comprising each recognized index relationship search patterns;

establishing a codex inventory control database comprising a series of comprehensive collection index relationship search patterns that are stored as codex pages;

establishing a quantitative hierarchical value for each of the words in the word database, wherein each word is related to a quantitative value between a lowest value and a highest value having a relationship where the lowest value is respective to the highest occurrence rate and the highest value is respective to the lowest occurrence rate;

establishing a set of predetermined semantic guidelines for the language;

establishing a webpage database comprising a plurality of searchable Internet web pages;

assigning a unique value to each searchable Internet web page;

parsing end user valid requests

interpreting each word to determine if each word exists within the word database;

interpreting each word to determine if each word matches any data in the database;

converting each word into a linguistic glyph and probabilistically mapping geospatial information into a geospatial glyph;

standardizing and transforming each linguistic and geospatial glyph into vector components;

arranging, grouping and prioritizing each linguistic and geospatial glyph into a logical sequence of words and quantitative hierarchal values that yield an optimal hierarchical set;

validating each linguistic and geospatial glyph and deriving a list of associated words and common known words clusters within glyph database to yield search pattern;

arranging, reorganizing, and prioritizing linguistic glyphs using static vector values for requests and dynamic values for sessions to solve for the left brain equation;

processing the impact of a managerial hierarchical related group of language-based independent variables to create an equation that shrinks the size of the search environment by parsing each request and each session into index relationships;

verifying each linguistic and geospatial glyph and determining whether or not the glyph will function as an independent variable that can aid in reducing the size of the environment;

dynamically adjusting the value of each linguistic and geospatial glyph based on an end user pattern of behavior;

identifying and disabling zero clusters, confounding elements of a search;

determining common denominator words using rules of association and transitivity to determine relevant and irrelevant glyphs;

mapping and plotting each linguistic glyph that is recognized as an independent variables into index relationships using left brain analysis;

then establishing a primary filter as a primary index relationship (I), a secondary filter as a second index relationship (J), and a tertiary filter as a third index relationship (K);

adding the vector value of each index relationship into a resultant linguistic vector value that determines a significance level;

using the resultant linguistic vector value to determine a smallest partition of the Internet that will serves as a point of origin for the search process;

comparing the resultant linguistic vector value against optimal mass limits to determine how many linguistic index relationship exist as follows;

deriving zero index relationships and using the Internet (U) as the environment and ranking each web page to the master index;

deriving one index relationship and subdividing the Internet using primary index relationship to create a block (I) as the visible environment and eliminating from calculation any web page not belonging to block (I);

deriving two index relationship and subdividing the Internet using primary and secondary index relationship to create a sub block (I, J) as the visible environment and eliminating from calculation any web page not belonging to sub block (I, J);

deriving three index relationship and subdividing the Internet using primary, secondary and tertiary index relationships to create a mini block (I, J, K) as the visible environment and eliminating from calculation any web page not belonging to mini block (I, J, K);

ranking for each partition each web page to a relative master index;

binding and mapping the block(I) into the Internet environment (U), the sub block (I, J) into the block (I), and the mini block (I, J, K) into the sub block (I, J);

hierarchically subordinating each relative master index to their parent relative master index and also subordinating the entire relative master index to the master index;

identifying a left brain linguistic based checkmate combination when three directly related linguistic index relationships (I, J, K) exists;

analyzing using right side of the brain to map and plot each recognized geospatial independent variables glyphs into index relationships and then establishing the primary filter as the primary index relationship (X), the secondary filter as the second index relationship (Y), and the tertiary filter as the third index relationship or (Z);

adding the vector value of each index relationship into a resultant geospatial vector value that determines the significance level;

using the resultant geospatial vector value to determine the smallest partition of the Internet that will serves as point of origin for the search process;

relevant glyphs become independent variables, whereas irrelevant glyphs de-emphasize web pages when determining the final equation;

comparing the resultant geospatial vector value against mass limits to determine how many linguistic index relationships exist;

deriving no index relationships and using the Internet (U) as the environment and ranking each web page to the master index;

deriving one index relationship and subdividing the Internet using primary index relationship to create a block (X) as the relevant environment and de-emphasizing from calculation any web page not belonging to block (X);

deriving two index relationship and subdividing the Internet using primary and secondary index relationship to create a sub block (X, Y) as the visible environment and de-emphasizing from calculation any web page not belonging to sub block (X, Y);

deriving a three index relationship and subdividing the Internet using primary, secondary and tertiary index relationships to create a mini block (X, Y, Z) as the visible environment and de-emphasizing from calculation any web page not belonging to mini block (X, Y, Z);

substituting (I) when null with (X), substituting (J) when null with (Y), substituting (K) when null with (Z);

identifying right brain checkmate combinations when three index relationships or (X, Y, Z) exists;

performing deductive reasoning by adding the index relationships of both sides of the brain to create a resultant equation vector value that determines the significance level;

using the resultant equation vector value to determine the smallest partition of the Internet that will serves as point of origin for the search process;

assigning each index relationship to glyphs relevant search category;

identifying the most relevant codex pages based on the index relationship and to obtain top (n) web pages of each category and their optimal inventory control data structure containing “

related objects”

;

analyzing the ‘

related objects’

to find missing gaps of information;

matching, mapping and mixing pair combinations of two categories against each other in order to determine direct relationships and relevancy between two categories;

emphasizing high probability categories combinations associated to the mathematical equation that yields the final destination;

de-emphasizing low probability categories combinations associated to the mathematical equation that yields the final destination;

integrating index relationships (I) and (X) into event (I!); and

deriving the index relationships using the event (I!) to create element (I, J, K)!;

integrating index relationship (J) and (Y) into event (J!); and

deriving the index relationships using the event (J!) to create element (I, J, K)!!;

identifying left and right brain checkmate combinations when six index relationships or (I, J, K, X, Y, Z) exists;

reading and confirming the content of top ranked valued (n) responses belonging to the optimal sized environment;

validating and verifying the best responses based on content value;

selecting the best fit element subdivision to create the optimal sized environment;

picking the best fit content and top ranked valued (n) responses from the optimal size environment as output;

sending and displaying output to end users terminal;

simulating for each codex page the optimal environment in real time and assigning a relative master index;

continuously scanning the environment and updating each codex page as each new web page is identified having a higher value than the lowest value stored web pages;

associate the new web page to the codex page;

disassociate the lowest valued web page to the codex page;

storing and updating changes in real time to the codex pages;

continuously storing and updating in real time the at least one collection of top (n) web pages, and the top (n) sites geospatial information;

continuously storing and updating in real time relative master index belonging to each codex page;

determining at predefined time intervals the total number of web pages in the codex and for each codex page in its chain of command;

determining at predefined time intervals the total number of significant difference changes in the Internet and then revaluing each site that updated one of its top ranked (n) web pages;

purifying, mapping and plotting each element of the old master index into the new master index using the content value of the relative master index of the highest vector valued codex page;

continuously creating, storing, synchronizing and updating in real time the new master index that reflect the latest condition of the environment that is derived from the continuously detected significant changes and adjustments made to the codex; and

purifying, transforming and updating new master index and in turn the codex and the entire chain of command of codex pages.

2. An Internet search engine method as recited in claim 1, wherein the collection of top (n) web pages has at least one relationship index that represents at least one independent variable as the calculation basis.

3. An Internet search engine method as recited in claim 1, the method further comprising the steps of:

referencing one collection of top (n) web pages to each codex page;

associating each codex page with index relationships that shrink the environment before any calculation is performed;

wherein each codex page has one collection of top (n) web pages measured at different mass combinations with the appropriate dynamic values of each independent variables belonging to each index relationship as they are calculated at the optimal mass limits;

associating each the codex page with specific geospatial dimensions;

estimating the impact of related, unrelated and irrelevant glyphs combinations that may exists while measuring the mass combinations values on each codex page; and

grouping and associating each index relationship into one category and associating one collection of top (n) web pages for each category.

4. An Internet search engine method as recited in claim 1, the method further comprising the steps of:

representing a recognized index relationship search pattern that is stored in the codex.

5. An Internet search engine method as recited in claim 4, wherein each collection of top (n) web pages have additional related, unrelated and irrelevant glyphs that further adjust the calculation basis.

6. An Internet search engine method as recited in claim 4, wherein each collection of top (n) web pages based on the different mass combinations derived from matching and mixing the independent variables, related, unrelated and irrelevant glyphs combination are associated to their optimal inventory control data structure.

7. An Internet search engine method as recited in claim 4, wherein each optimal inventory control data structure contains ‘

related objects’

glyphs belonging to the top (n) sites geospatial information, related content, product inventory, and people and their associated known related personal content.

8. An Internet search engine method as recited in claim 4, further comprises the step of:

probabilistically integrating one left side of the brain category that is directly related to one right side of the brain category into an event.

9. An Internet search engine method as recited in claim 8, further comprises the step of:

grouping and associating pairs of categories into events and associating one collection of top (n) web pages for each event.

10. An Internet search engine method as recited in claim 1, further comprise the step of:

continuously storing and updating in real time relative master index belonging to each Internet related managerial hierarchical index relationship partition.

11. An Internet search engine method as recited in claim 1, further comprises the step of:

purifying, mapping and plotting each element of the old master index into the new master index using the content value of the relative master index of the highest vector valued partition.

12. An Internet search engine method as recited in claim 1, further comprises the step of:

continuously creating, storing, synchronizing and updating in real time the new master index that reflect the latest condition of the environment that is derived from the continuously detected significant changes and adjustments made to the Internet'"'"'s partitions.

13. An Internet search engine method as recited in claim 1, further comprises the step of:

purifying, transforming and updating new master index and in turn the entire content of the Internet (U) and all valid index relationships combinations.