Search engine optimizer

Abstract

A search engine optimizer which 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 ride side check mate combination required to achieve certitude.

Claims

1. A system, using a massive volume input and output processing parallel distributed Internet search engine supercomputer and at least one computer data processing apparatus; and mimicking the function of the human brain by creating a language-based equation and a geospatial equation, for buffering between an Internet browser memory storage apparatus in data communication with a data processing apparatus memory storage apparatus in data communication with the data processing apparatus, the memory storage apparatus storing server instructions that when executed by the data processing apparatus causes the data processing apparatus to perform operations to interpret numerical and textual data and convert statistical interpreted data into probabilistic vector objects representing the mathematical equations used to obtain an optimal match in a search response to an end user regular expression search, hereinafter regex, the system comprising the steps of: at least one computer, continuously updating each Internet browser interactions with the server as an end users-profile, and the geographic area regex indicative of a client device location; at least one computer, receiving from an Internet browser to initialize a session, that is rendered on the Internet browser to provide an interface through which a regex may be entered, and then for each request of a session; accessing the Internet browser and in response to initiating a session with the system, and then, for each request of a session, defining location specific semantic associations as auxiliary variable suggestions determined from data separate from content of the Internet browser, wherein each location specific auxiliary variable suggestion, associated with the geographic area regex is displayed as an auxiliary variable suggestion when the client device location is within the geographic area regex; selecting based on the end user profile a highest valued location specific auxiliary suggestions before initializing a session with the at least one computer of the system, and prior to rendering the Internet browser, each initial auxiliary suggestion being associated with the geographic area regex; and providing to the Internet browser in response to initializing a session, including first instructions that cause the Internet browser, upon the Internet browser initializes a session regex input field, and the initial auxiliary variable suggestions that are automatically provided upon initializing the Internet browser.

2. The system of claim 1, wherein upon initializing a session of the Internet browser includes second instructions for implementation to: store the initial auxiliary variable suggestions as search patterns in the end user profile; compare the interactive input in the regex input field to search patterns stored in the end user profile; select search patterns that match the interactive input in the regex input field based on the comparison; and display the selected search patterns as auxiliary variable suggestions for the interactive input into the regex input field.

3. The system of claim 2, wherein the second instructions cause the Internet browser to: provide the interactive input in the regex input field to at least one computer data processing apparatus of the system as a regex; receive, in response to the regex caused from the second instructions of the Internet browser, additional auxiliary variable suggestions from the at least one computer data processing apparatus; store the auxiliary variable suggestions as search patterns in the end user profile; and the server instructions cause the at least one computer data processing apparatus to provide the auxiliary variable suggestions sent to the Internet browser in response to the regex, wherein the system assigns one of the computer data processing apparatus of the at least one computer data processing apparatus to store a server side copy of the end user profile.

4. The system of claim 1, wherein the Internet browser, upon initializing session downloads, updates the local keyword database each valid geographic area search pattern within the end user profile with preprocessed and precalculated optimal responses by passing search engine capacities.

5. The system of claim 4, wherein the server instructions further cause the at least one computer data processing apparatus to provide a search results resource in response to the regex received from the Internet browser, the search results resource comprising: third instructions that cause the Internet browser to generate a search results page that displays search results referencing resources determined to be responsive to the regex and auxiliary variable suggestions; and fourth instructions that cause the Internet browser to store the auxiliary variable suggestions as search patterns in the end user profile.

6. The system of claim 1, wherein the search results page comprises a session query input field, and the fourth instructions cause the Internet browser to; compare query characters input in the session regex input field to the search pattern stored end user profile; select search patterns that match the interactive input in the session regex input field based on the comparison; and display the selected search patterns as auxiliary variable suggestions for the interactive input into the session regex input field.

7. The system of claim 6, wherein the second instructions cause the Internet browser to provide a separate interactive regex for each valid interactive input into the regex input field.

8. The system of claim 1, wherein the internet browser transforms the interactive input into a search pattern and then validates the search pattern using a local keyword database, and upon a positive match provides a separate interactive regex for each valid interactive input into the regex input field.

9. The system of claim 8, wherein the internet browser updates the local keyword database after validation ach search pattern in the end user profile.

10. A method, using a massive volume input and output processing parallel distributed Internet search engine supercomputer and at least one computer data processing apparatus; and mimicking the function of the human brain by creating a language-based equation and a geospatial equation, for buffering between an Internet browser memory storage apparatus in data communication with a data processing apparatus memory storage apparatus in data communication with the data processing apparatus, the memory storage apparatus storing server instructions that when executed by the data processing apparatus causes the data processing apparatus to perform operations to interpret numerical and textual data and convert statistical interpreted data into probabilistic vector objects representing the mathematical equations used to obtain an optimal match in a search response to an end user regular expression search, hereinafter regex, the system comprising the steps of: at least one computer, continuously updating each Internet browser interactions with the server as an end users-profile, and the geographic area regex indicative of a client device location; at least one computer, receiving from an Internet browser to initialize a session, that is rendered on the Internet browser to provide an interface through which a regex may be entered; accessing the Internet browser and in response to initiating a session with the system and, for each request of a session, defining location specific semantic associations as auxiliary variable suggestions determined from data separate from content of the Internet browser, wherein each location specific auxiliary variable suggestion, associated with the geographic area regex is displayed as an auxiliary variable suggestion when the client device location is within the geographic area regex; selecting based on the end user profile a highest valued location specific auxiliary suggestions before initializing a session with the at least one computer of the system, and prior to rendering the Internet browser, each initial auxiliary suggestion being associated with the geographic area regex; and providing to the Internet browser in response to initializing a session, including first instructions that cause the Internet browser, upon the Internet browser initializing a session regex input field, and the initial auxiliary variable suggestions that are automatically provided upon initializing the Internet browser based on distance from the center of calculation given the client device location.

11. The method of claim 10, wherein upon initializing a session of the Internet browser includes second instructions for implementation to: store the initial auxiliary variable suggestions as search patterns in the end user profile; compare the interactive input in the regex input field to search patterns stored in the end user profile; select search patterns that match the interactive input in the regex input field based on the comparison; and display the selected search patterns as auxiliary variable suggestions for the interactive input into the regex input field.

12. The method of claim 11, wherein the second instructions cause the Internet browser to: provide the interactive input in the regex input field to at least one computer data processing apparatus of the system as a regex; receive, in response to the regex caused from the second instructions of the Internet browser, additional auxiliary variable suggestions from the at least one computer data processing apparatus; store the auxiliary variable suggestions as search patterns in the end user profile; and the server instructions cause the at least one computer data processing apparatus to provide the auxiliary variable suggestions sent to the Internet browser in response to the regex, wherein the system assigns one of the computer data processing apparatus of the at least one computer data processing apparatus to store a server side copy of the end user profile.

13. The method of claim 10, wherein the Internet browser, upon initializing session downloads, updates the local keyword database each valid geographic area search pattern within the end user profile with preprocessed and precalculated optimal responses by passing search engine capacities.

14. The method of claim 13, wherein the server instructions further cause the at least one computer data processing apparatus to provide a search results resource in response to the regex received from the Internet browser, the search results resource comprising: third instructions that cause the Internet browser to generate a search results page that displays search results referencing resources determined to be responsive to the regex and auxiliary variable suggestions; and fourth instructions that cause the Internet browser to store the auxiliary variable suggestions as search patterns in the end user profile.

15. The method of claim 10, wherein the search results page comprises a session query input field, and the fourth instructions cause the Internet browser to: compare query characters input in the session regex input field to the search pattern stored end user profile; select search patterns that match the interactive input in the session regex input field based on the comparison; and display the selected search patterns as auxiliary variable suggestions for the interactive input into the session regex input field.

16. The method of claim 15, wherein the second instructions cause the Internet browser to provide a separate interactive regex for each valid interactive input into the regex input field.

17. The method of claim 10, wherein the internet browser transforms the interactive input into a search pattern and then validates the search pattern using a local keyword database, and upon a positive match provides a separate interactive regex for each valid interactive input into the regex input field.

18. The method of claim 17, wherein the internet browser updates the local keyword database after validating each search pattern in the end user profile.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) For the purpose of initially illustrating the invention, the specification presents drawings, flow diagrams, and embodiments that are presently preferred as well as alternates. It should be understood, however, that the invention is not limited to the specific instrumentality and methods disclosed herein. It can be recognized that the figures represent a layout in which persons skilled in the art may make variations therein. In the drawings:

(2) FIG. 1 presents a main page of the search engine optimizer;

(3) FIG. 2 presents an output of a search executed by a plurality of search engines;

(4) FIG. 3 presents a basic Glyphs left side;

(5) FIG. 4 presents a basic Glyphs right side;

(6) FIG. 5 presents a basic Glyphs overview;

(7) FIG. 6 presents an advanced Glyphs left side;

(8) FIG. 7 presents an advanced Glyphs right side;

(9) FIG. 8 presents an advanced Glyphs overview;

(10) FIG. 9 presents block diagram illustrating the process of stimulating the brain;

(11) FIG. 10 presents a block diagram illustrating the process performed to reach certitude;

(12) FIG. 11 presents various software symbols utilized by the search engine optimizer;

(13) FIG. 12 presents a process utilizing a hot and cold algorithm;

(14) FIG. 13 presents an enhanced Glyphs overview;

(15) FIG. 14 presents a block diagram illustrating a deductive reasoning search; and

(16) FIG. 15 presents a super Glyph overview.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

(17) These are the figures that describe Cholti:

(18) FIG. 1: Cholti Search Engine Optimizer: (1000) Consists of the main page of the visual representation of the Cholti Scripted Algorithm. [A], [B], [C] and [D] represent leading ‘Boolean Algebra’ competitors, and [E] represents Cholti. Once the end user clicks (50) the Search button the system calculates ‘Before’ and ‘After’ (Ax & Bx) samples. This diagram shows the results for the Bx or ‘After’ request for “American Civil War Battlefield” the system has determined the accuracy for each Search engine as follows: [A]=85%, [B]=72%, [C]=58%, [D]=78% and [E]=86%.

(19) (100) is the list of keywords typed by the end user, (200) consists the list of valid request that belong to a session, (300) represents Hot Algorithm vector component concerning each Keyword, (60) represent identified Auxiliary Variables, and (70) represent identified Clusters.

(20) FIG. 2: ‘Boolean Algebra’ Search Engines: [A] or (10), [B] or (20), [C] or (30) and [D] or (40) represent leading browser engines that perform their calculation using ‘Boolean Logic’ or ‘Caesar's divide and conquer’ method. In this case the end user request is “American Civil War Battlefield”. Once the end user clicks on the corresponding ‘Search button’ or (50) the search begins. [B], [C] & [D] Search Engine have interactive cluster recognition that consists of a plurality of optimal valued preprocessed requests.

(21) FIG. 3: Basic Glyphs [L] Left Side: Using Cholti (1000) the end user types the following keywords (200) “American Civil War Confederates Battlefield”. This request is measured to have (400) an overall accuracy of 61% to 85% and (500) quality level of 4. Respectively, (300) shows the relative accuracy of [A]=82%, [B]=84%, [C]=84%, [D]=85% the ‘Boolean Search’ Engines, and [E]=86% for Cholti or ‘Deductive Reasoning Search’ Engine. In this case the request is converted into (100) “American”=[G1], “Civil”=[E1], “War”=[E2], “Confederates”=[E3], and “Battlefield”=[E4]. The system selects (600) “American Civil War” or ([G1]+[E1]+[E2]) as the left side of the brain Superset (i). The system selects “Confederates” (700) or [E3] as the left side of the brain Set (i, j). The system selects “Battlefield” (800) or [E4] as the left side of the brain Subset (i, j, k).

(22) FIG. 4: Basic Glyphs [R] Right Side: Using Cholti (1000) the end user types the following keyword (200) “Miami”. This request is measured to have (400) an overall accuracy of 31% to 60% and (500) quality level of 1. Respectively, (300) shows the relative accuracy of [A]=39%, [B]=32%, [C]=29%, [D]=26% the ‘Boolean Search’ Engines, and [E]=39% for Cholti or ‘Deductive Reasoning Search’ Engine. In this case the request is converted into “Miami” (100) or [G1]. The program selects “USA” (601) and assigns an unloaded Cholti Glyph as the right side of the brain Superset (i). The system selects “Florida” (701) with an unloaded Cholti Glyph as the right side of the brain Set (i, j). The system selects “Miami” (801) or [G1] as the right side of the brain Subset (i, j, k). After having the relative Cholti accuracy level of each browser engine the overall accuracy upper limit is adjusted to 39%.

(23) FIG. 5: Basic Glyphs Overview: Using Cholti (1000) the end user types the following keywords (200) “American Civil War Battlefield Gettysburg”. This request is measured to have (400) accuracy level of 86% and (500) quality level of 5. Respectively, (300) shows the relative accuracy of [A]=85%, [B]=85%, [C]=72%, [D]=68% the ‘Boolean Search’ Engines, and [E]=86% for Cholti or ‘Deductive Reasoning’ Search Engine. In this case the request is converted into (100) “American”=[G1], “Civil”=[E1], “War”=[E2], “Battlefield”=[E3], and “Gettysburg”=[G2]. The system selects “American Civil War” (600) or ([G1]+[E1]+[E2]) as the left side of the brain Superset (i). The system selects “USA” (601) and assigns an unloaded Cholti as the right side of the brain Superset. The system selects “Battlefield” (700) or [E3] as the left side of the brain Set (i, j). The system selects “Pennsylvania” (701) and assigns an unloaded Cholti Glyph as the right side of the brain Set (i, j). The system selects “Gettysburg” (801) or [G2] as the right side of the brain Subset (i, j, k).

(24) FIG. 6: Advanced Glyphs [L] Left Side: Using Cholti (1000) the end user types the following keywords (200) “American Civil War Battlefield”. This request is measured to have (400) an overall accuracy of 86% and (500) quality level of 4. Respectively, (300) shows the relative accuracy of [A]=85%, [B]=72%, [C]=58%, [D]=78% the ‘Boolean Algebra’ Search Engines, and [E]=86% for Cholti or ‘Deductive Reasoning’ Search Engine. In this case the request is converted into (100) “American”=[G1], “Civil”=[E1], “War”=[E2], and “Battlefield”=[E3]. The system selects “American Civil War” (610) or (“[G1]+[E1]+[E2]”) or C1 that is represented with a loaded Cholti as the left side of the brain Superset. The system selects “Battlefield” (710) or [E3] as the left side of the brain Superset.

(25) FIG. 7: Advanced Glyphs [R] Right Side: Using Cholti (1000) the end user types the following keyword (200) “Gettysburg”. This request is measured to have (400) an overall accuracy of 86% and (500) quality level of 4. Respectively, (300) shows the relative accuracy of [A]=84%, [B]=84%, [C]=84%, [D]=84% the ‘Boolean Algebra’ Search Engines, and [E]=86% for Cholti or ‘Deductive Reasoning’ Search Engine. In this case the request is converted into (100) “Gettysburg”=[G1]. The system selects “USA” (601) and assigns a loaded Cholti Glyph as the right side of the brain Superset (i). The system selects “Pennsylvania” (701) or “PA” with a loaded Cholti Glyph as the right side of the brain Set (i, j). The system selects (801) “Gettysburg” or [G1] as the right side of the brain Subset (i, j, k).

(26) FIG. 8: Advanced Glyphs Overview: Using Cholti (1000) the end user types the following keywords (200) “American Civil War Battlefield Gettysburg”. This request is measured to have (400) an overall accuracy of 86%-95% and (500) quality level of 5. Respectively, (300) shows the relative accuracy of [A]=95%, [B]=95%, [C]=95%, [D]=95% the ‘Boolean Algebra’ Search Engines, and [E]=96% for Cholti or ‘Deductive Reasoning’ Search Engine. In this case the request is converted into (100) “American”=[G1], “Civil”=[E1], “War”=[E2], “Battlefield”=[E3], and “Gettysburg”=[G2]. The system selects (600) “American Civil War” or (“[G1]+[E1]+[E2]”) or C1 represented as a load Cholti as the left side of the brain Superset (i). The system selects “USA” (601) and assigns a loaded Cholti as the right side of the brain Superset (i). The system selects “Battlefield” (700) or [E3] as the left side of the brain Set (i, j). The system selects “Pennsylvania” (701) with a loaded Cholti Glyph as the right side of the brain Set (i, j). The system selects “Gettysburg” (801) or [G2] with (802) loaded Super Glyph {Zip Code}, (803) unloaded Super Glyph {IP Address} and (804) unloaded Super Glyph {ANI} as the right side of the brain Subset. Where ANI equals telephone number ranges, loaded Cholti is certain or reasonable and unloaded Cholti probable. Cholti ‘cherry picks’ results to further shrink the size of the environment and the accuracy upper limit is adjusted to 96%.

(27) FIG. 9: Cholti Simulates the Brain: this diagram explains the step by step process Cholti Scripted Algorithm performs to simulate the human brain. Tier 1 or Join Pyramid (or improbable) the system performs the minimal tasks of identifying, validating and verifying each Keyword and creating a list of Auxiliary variables and related clusters. Tier 2 or Simple Pyramid or (probable) is the process of associating each keyword to specific groups. Tier 3 or Hybrid or (English language or [L] left side of the brain) groups keywords into ‘end user’ and known clusters. An ‘end user cluster’ is determined by examining how an end user types keywords in a request and then measures common denominator consecutive keywords and order or priority in which they appear. Tier 4 or Complex (geospatial or [R] right side of the brain) maps and plots Keywords to geospatial clusters that will help valorizes and narrow down top W_Rank. (100) Cholti match/merges the content of the left side and right ([L]+[R]) to identify check mate combination that triggers the process of ‘Cherry picking’ the optimal result.

(28) FIG. 10: Accuracy: this diagram explains step by step the process Cholti Scripted Algorithm performs to reach certitude or 100% accuracy. Accuracy is derived as informational entropy improves from vague [0] to certain [4]. [0] is the ‘Boolean Algebra’ approach that keywords are expressed as 1 valid and 0 invalid. The ‘Boolean Algebra’ supercomputers use vector components that are feed to the eigenvector based algorithms that select the top page ranks.

(29) [1] or Superset (i) is the process of identifying the primary English [L] left side of the brain and geospatial (or Geodata (w, 0, 0, 0)) [R] right side of the brain primary filter that are used to shrink the size of the environment significant, and serve as the basis of the hierarchical set. [2] or Set (i, j) is the process of further distilling and reducing the environment and then partitioning the Superset(i) into a plurality of Set that have in common a secondary cluster (or keyword) and geospatial (or Geodata (w, x, 0, 0)). [3] or Subset (i, j, k) is the process of further distilling and reducing the environment and then partitioning the Superset (i) into a plurality of Set that have in common a secondary cluster (or keyword) and geospatial (or Geodata (w, x, y, z)). [4] Check Mate combinations or ‘Cherry Picking’ is the process of weighting the Super Sites and Super Pages against the collection of known facts or Super Glyphs to find the optimal result based on the present conditions of a session. Since ‘Boolean Algebra’ search engine have a session limit of one request Cholti always measures both the last request and the entire session for optimal results when it estimates a significant difference.

(30) Note: Many leading search engines use Zip Code as their primary filter. This approach can confound a search, for this reason a more robust Telecommunication model is required. Based on U.S. Pat. No. 6,614,893 and its continuations, the Geodata (w, x, y, z) model was incorporated and represents geospatial data in a hierarchical format. Where w=Country, x=State, y=City, and z=Zip Codes that are within a radius of several miles of the latitude and longitude of the City and belong to the same LATA Area or State or Region. To further illustrate the advantages of the Geodata over Zip Code based search the model do the following: 1) Determines how a request affects a response is based on the logical geospatial hierarchical partitioning of the environment. Each a country partitioned into a plurality of Regions, LATA Areas (continental USA if required), NPA or Area Code, NXX or Exchanges and then Zip Codes. Example: A person lives in New Jersey one mile away from New York and the valid geospatial circle radius is fifteen miles, the Zip Code based on State, Area Code, and/or Exchange are worthless, while LATA and Region may yield significant and real results. 2) Geodata assigns probabilities and distances to each plausible match and thus removes all the associated confounding elements with the search. 3) Finds the missing gaps of information Super Sites and Super Pages. The primed information gathered in steps 1 to 3 is inserted into the {State}, {City}, {ANT}, {Zip Code} and {IP Address} Super Glyphs or collections of known facts. For this reason raw Zip Code should never be used as a filter for ‘Boolean Algebra’ search. The Geodata model is a significant improvement over Zip Code or City Name based searches, since it does not work with raw data. Instead the system does the following steps 1) primes the information to remove confounding data, 2) uses ‘Deductive Reasoning’ to solve for missing data and 3) insert the correct data associated to the search. To those readers in the telecommunication field in particular when billing, the confounding elements are similar to the infamous USOC.

(31) FIG. 11: Cholti Software Symbols: can divided into four categories: Column (1) measures the quality of the request, where 0 is gray, 1 is violet, 2 is blue, 3 is green, 4 is light green, 5 is yellow, 6 is orange, and 7 is red. Column (2) measures the level of accuracy, based on the quality level as follows: [0] has 0% to 30% accuracy or outer circle, [1 to 2] has 31% to 60% accuracy or acceptable circle, [3 to 4] has 61% to 85% accuracy or traditional ‘Boolean Algebra’ circle, [5] has 86% to 95% accuracy or improved circle, [6] has 96% to 99% accuracy or inner and [7] is certain or 100% accuracy or ‘Cherry picking’ circle and thus the red color. Column (3) represents unloaded Cholti Glyphs and Column (4) represents loaded Cholti Glyphs. When identifying the optimal result the unloaded and loaded Cholti Glyphs are used to represent Super Glyph objects. A Super Glyph object is a dynamic object that has a plurality of vector components that are used to measures hierarchical sets.

(32) FIG. 12: Hot & Cold Algorithm: once Cholti has measured a plurality of requests in a session see FIG. 14 the program is able to use the Hot and Cold Algorithm.

(33) The Hot Algorithm measure trends and the Cold Algorithm measures ‘Boolean Algebra’ pitfalls. In the diagram the system has identified a plurality of keywords that are used in a session consisting of a plurality of requests. Cholti identified each keyword as follows: “American” or [E1], “Civil” or [E2], “War” or [E3], “Blue” or [E4], “Gray” or [E5], “Hotel” or [E6], “Lee” or [E7], “Meade” or [E8], “1863” or [M1], “July” or [M2], “Sheraton or [M3], Confederates [M4] or [E11], “Gettysburg” or [G1], “Pennsylvania” or [G2], “USA” or [G3], “North” or [G4], {Zip Code} loaded Cholti or [C1] and {ANI} loaded Cholti or [C2]. Note: When Cholti assigns a geospatial keyword to belong on the [L] left side instead of the right side like “American” or [G1] it set the Geodata to null, otherwise the geospatial information may dramatically dilutes the environment in a confounded manner. When a geospatial keyword is assigned in the [L] left side of the brain it can be referenced as a [M] or mixed keyword.

(34) Since the session had a plurality of requests and thus all keywords are not at room temperature. Cholti determines the following keywords: [E1], [E2], [E3], [E6], [M3], [M4], & [G1] as Hot. Cholti determines the following keywords: [E4], [E5], [E7], [E8], [E9], [E10], [M1], [M2], [G2], [G3], & [G4] as Cold. [C1] and [C2] are valid and reasonable Super Glyphs. Red colored Super Glyph are certain and are fully loaded always and thus Hot words.

(35) Once the optimal button is pressed in the browser, based on the consecutive related request, the system can create the appropriate hierarchical set, and then filter with geospatial information to ‘Cherry Pick’ using deductive reasoning the optimal result.

(36) FIG. 13: Improved Glyphs Overview: Using Cholti (1000) the end user types the following keywords (200) “American Civil War Battlefield Gettysburg”. This request is measured to be certain (400) or has an overall accuracy of 100% and (500) quality level of 7. Respectively, (300) shows the relative accuracy of [A]=97%, [B]=97%, [C]=97%, [D]=97% the ‘Boolean Search’ Engines, and [E]=100% or certain for Cholti or ‘Deductive Reasoning Search’ Engine.

(37) Using ‘Before’ or Ax from FIG. 5. The ‘Before’ request is converted into (100) “American”=[G1], “Civil”=[E1], “War”=[E2], “Battlefield”=[E3], and “Gettysburg”=[G2]. The system selects “American Civil War” (600) or ([G1]+[E1]+[E2]) as the left side of the brain Superset (i). The system selects “USA” (601) and assigned an unloaded Cholti as the right side of the brain Superset (i). The system selects “Battlefield” (700) or [E3] as the left side of the brain Set (i, j). The system selects “Pennsylvania” (701) and assigns an unloaded Cholti Glyph as the right side of the brain Set (i, j). The system selects “Gettysburg” (801) or [G2] as the right side of the brain Subset (i, j, k).

(38) Using ‘After’ or Bx from FIG. 8. The ‘After’ request is converted into (610) “American Civil War” or (“[G1]+[E1]+[E2]”) or C1 represented as a load Cholti as the left side of the brain Superset (i). The system selects “USA” (611) and assigns a loaded Cholti as the right side of the brain Superset. The system selects “Battlefield” (710) or [E3] as the left side of the brain Set (i, j). The system selects “Pennsylvania” (711) with a loaded Cholti Glyph as the right side of the brain Set (i, j). The system selects “Gettysburg” (811) or [G2] with loaded Super Glyph {Zip Code} (812), unloaded Super Glyph {IP Address} (813) and unloaded Super Glyph {ANI} (814) as the right side of the brain Subset (i, j, k).

(39) When the end user clicks into optimal button, the program will display the Super Glyph equation used to determine certainty.

(40) FIG. 14: ‘Deductive Reasoning’ Search: is a diagram that illustrates the left side of the page (1000) with four request sessions. [1], [2], [3] and [4] use ‘Boolean Algebra’, and [1A], [2A], [3A] and [4A] use ‘Deductive Reasoning’ The right side of the page as per FIG. 12 shows how the results of the Hot and Cold Algorithms. As Hot & Cold Algorithm keywords and Super Glyphs values are calibrated Cholti is able to rewrite the request of existing ‘Boolean Algebra’ search engines.

(41) As more significant difference events are encountered and check mate combinations are detected in [4A] Cholti is able to ‘Cherry pick’ the optimal result. Should a [5] and [5A] request exists the Hot & Cold algorithms keyword values will change, and based on the significant changes of the last request a new valid Super Glyph equation will be created. Consequently, a session can have multiple valid equations and all are taken into account when determining the final one. Super Glyphs are objects that measure events within valid parameters. In a session common denominator Super Glyphs will have a more volatile range of parameters.

(42) Note: In all the FIG using Cholti the ‘After’ or Bx accuracy levels represents the quality of existing ‘Boolean Algebra search engines [A], [B], [C], and [D] that represent 95% of the US marketing in 2008.

(43) FIG. 15: Super Glyph Overview: this diagram (1000) represents the final resultant Super Glyph Search equation that is comprised on five pairs of Super Glyphs. From top to bottom: The (1) first pair represents the ‘end user’ satisfaction and corporate media of particular typed keywords. The second (2) pair represents the (yellow) Mulligan keywords belonging to unrelated partial environments and all the auxiliary keywords belonging to related partial environments. The third (3) pair represents valid (green) geospatial common denominator filters. The fourth (4) pair represents valid (blue) English language common denominator filters. (3) Is used to create Geodata (w, x, y, z) arrays. (4) is used to create hierarchical sets consisting of a plurality of valid Superset(i), Set(I,J) and Subset(I,J,K). The fifth (5) pair represents reasonable or certain element of the Super Glyph equation.

(44) Note: concerning all the figures. The actual prototype uses the Superset (i), Set (i, j) and Subset (i, j, k) within the environmental domains. Thus, the system identifies optimal configuration of clusters and keyword that will be used to filter the size of the environment. Since, the Basic Glyphs are broad and the Advance Glyphs are narrow filtering patterns the order of Superset (i), Set (i, j) and Subset (i, j, k) may differ. In this case, the Basic Glyph controls the hierarchical set. How it branches into a plurality of Sets and Subsets is done by correlating both by weight, where smaller environment sizes have a greater weight and probability. Z_Price Bitmap stores exact matches and dynamic matches of Basic and Advanced Glyph search patterns. Exact matches have a higher degree of accuracy since they are a one to one relationship, whereas Dynamic matches possess a many to one.

(45) Example #1: XCommerce finds missing gaps of information. End User's Request: “A”+“B”+“C” with the following known clusters:

(46) 1. (A+B+I) 67%, Value=4, Weight=2.67.

(47) 2. (J+B) 50%, Value=3, Weight=1.50.

(48) 3. (K+B+C) 67%, Value=5, Weight=3.33.

(49) 4. (L+C) 50%, Value=5, Weight=2.50.

(50) 5. (A+B+C) 100%, Value=5, Weight=10.0.*

(51) In this case, the request has four known clusters 1-5 with “A”, “B”, & “C” as independent variables and “I”, “J”, “K”, & “L” are Auxiliary Variables or AV. Cholti assigns to each AV a probability. Each Cluster is measured in the scale of 1 to 5. A keyword has a value of 1-3 depending on their raw ‘Boolean Algebra’ page results. In this case cluster (A+B+C) or “American Civil War” is broad and thus has a value of 1. A cluster possesses the value of Minimum (Sum (Keyword)+1, 5). “A”, “C”, “I” and “J” has a value of 1, “B” and “K” has a value of 2, and “L” has a value of 3. Weight is calculated by multiplying the probability*value. *If a cluster is an exact match the Weight is multiplied*2. As a SWAG accuracy=Minimum (Maximum (Weight—AV, 1), 10). {circumflex over ( )} denotes logical OR, + or & denotes logical AND.

(52) Cholti creates the following hierarchical set:

(53) Superset (i)=(A+B+C) Value=5, Weight=10 & Accuracy=10.

(54) Set (i, 1)=(A+B+I) Value=5, Weight=6 & Accuracy=5.

(55) Set (i, 2)=(J+B) Value=4, Weight=4 & Accuracy=3.

(56) Set (i, 3)=(K+B+C) Value=5, Weight=8 & Accuracy=7.

(57) Set (i, 4)=(L+C) Value=5, Weight=4 & Accuracy=3.

(58) Set (i, 5)=(A+B+C)+J{circumflex over ( )}K{circumflex over ( )}L Value=5, Weight=10 & Accuracy=10.

(59) Example #2 Basic Glyphs:

(60) End User's Request: “D”+“A”+“B”+“C”

(61) “D” is the anchor

(62) “A+B+C” is the Superset (i)

(63) “D” is the Set (i, j)

(64) In this case “D+A+B+C” is the request and has four independent variables “A”, “B”, “C”, and “D”. “A”, “B” and “C” or “American Civil War” have a value of 1, and “D” or “Battlefield” has a value of 2. “D” is the strongest Keyword and is considered the Anchor. When considering the Superset (i) Cholti determines from Example #1 that “A+B+C” is an exact cluster with a value of 5. Consequently, “A+B+C” becomes the Superset (i) and “D” the Set (i, j) used as anchor.

(65) Cholti creates the following hierarchical set:

(66) Superset (i)=(A+B+C) Value=12, Weight=5 & Accuracy=10.

(67) Set (i, j)=“D” Value=10, Weight=10 & Accuracy=10.

(68) Subset (i, 1, k)=“D” & (B+C){circumflex over ( )}I{circumflex over ( )}C Value=10, Weight=7 & Accuracy=4.

(69) Subset (i, 2, k)=“D” & (A{circumflex over ( )}C{circumflex over ( )}J){circumflex over ( )}“B” Value=10. Weight=7 & Accuracy=5.

(70) Subset (i, 3, k)=“D” & (A{circumflex over ( )}K{circumflex over ( )}B){circumflex over ( )}“C” Value=10, Weight=7 &Accuracy=6.

(71) Subset (i, 4, k)=“D” & (A{circumflex over ( )}B{circumflex over ( )}L){circumflex over ( )}C Value=10. Weight=7. Accuracy=5.

(72) Subset (i, 5, k)=“D” & (A{circumflex over ( )}B{circumflex over ( )}C) Value=10. Weight=10 & Accuracy=10.

(73) * Each Subset (i, j, k) used has a plurality of AV & DV available to further distill and shrink the size of the environment.

(74) Example #3 Advanced Glyphs:

(75) End User's Request: “D”+“A”+“B”+“C”+“E”

(76) “D” is the anchor

(77) (“A+B+C”)+[DV1]+[DV2] is the Superset (i)

(78) “D”+[DV3]+[DV4]+Set (i, j)

(79) “E” Geodata (w, x, y, z)

(80) In this case “D”, “A”, “B”, “C” & “E” is the request and has five independent variables “D”, “A”, “B”, “C” & “E”. “A” & “C” have a value of 1, “B” & “D” have a value of 2. “E” or “Gettysburg” is also a Geodata (w, x, y, z) and has a value of 5. “E” is the strongest Keyword but is geospatial and is not the anchor, leaving “D” or “Battlefield” as the Anchor. When considering the Superset (i) Cholti determines from Example #1 that “A+B+C” or “American Civil War” is an exact cluster with a value of 5. Consequently, “A+B+C” becomes the Superset (i) and “D” or “Battlefield” the Set (i, j) used as anchor.

(81) “E” is the Geodata (w, x, y, z), where w=US, x=Pennsylvania, y=a plurality of valid Zip Codes, z=Gettysburg is the center of a circle with a radius of n miles that contains all the y collection of Zip Codes. “US” or w is associated with existing toll free Area Codes of 800 and 888 et al that are national in scope. “Pennsylvania” or x associated with the LATA where the Gettysburg is located.

(82) In example #2, Cholti measured the value of [L] left side of the brain information. In this case there is also [R] right side of the brain information. When calculating checkmate combination the system determines if a 10 exists on both sides for an optimal result, otherwise if [L] side value+[R] right side value are greater than 10 it will display the improved result.

(83) In this case there is no optimal result with a check mate value of 20. When determining Subsets “E” & “D” are matched/merged to “X”.

(84) Superset (i)=(A+B+C*) Value=6, Weight=6, Accuracy=10.

(85) Set (i, j)=(A+B+C+D*) Value=8, Weight=8, Accuracy=10.

(86) Subset (i, 1, k)=(D*+A+B){circumflex over ( )}(I{circumflex over ( )}C) Value=12, Weight=10, Accuracy=9.

(87) Subset (i, 2, k)=(D*+(A{circumflex over ( )}C{circumflex over ( )}J)+B) Value=12, Weight=8, Accuracy=6.

(88) Subset (i, 3, k)=(D*+(A{circumflex over ( )}K){circumflex over ( )}(B{circumflex over ( )}C) Value=12, Weight=10, Accuracy=9.

(89) Subset (i, 4, k)=(D* & (A{circumflex over ( )}B{circumflex over ( )}L){circumflex over ( )}C Value=12. Weight=8. Accuracy=6.

(90) Subset (i, 5, k)=(D* & (A{circumflex over ( )}B{circumflex over ( )}C) Value=12, Weight=10, Accuracy=10.

(91) Note: The probability was modified to square root (p(x)). The Weight was rounded to its highest integer value, and it can not exceed 10. Thus 0.5 was modified to 0.7 and 0.67 to 0.8. Each Subset (i, j, k) used the Geodata (w, x, y, z) to further distill and shrink the size of the environment.

(92) Example #4 Super Glyphs:

(93) End user's requests are identified, validated and verified into keywords. A keyword can be a ‘Zero Cluster’ and thus become a (yellow) Super Glyph. Geodata (w, x, y, z) hierarchy filters can become a (green) Super Glyphs. Exact language based clusters that are used as filters becomes (blue) a Super Glyph, corporate information becomes (red) a Super Glyph and personal information becomes a (purple) Super Glyph.

(94) An end user is random surfing the Internet and after several requests the system has determined that a plurality of valid Advanced Glyphs exists.

(95) 1. “War Gettysburg*” ([E1]+[G1]).

(96) 2. “Civil War Gettysburg*” ([E2]+[E1]+[G1]).

(97) 3. “American Civil War Pennsylvania*” ([G2]+[E2]+[E1]+[G3]).

(98) 4. “Robert Lee” ([E3]+[E4]).

(99) 5. “General” ([E5]).

(100) 6. “General Robert Lee” ([E5]+[E3]+[E4]).

(101) 7. “Historical Battle Map USA*” ([E6]+[E7]+[E8]+[G4]).

(102) 8. “American Civil War Battle Gettysburg*” ([G2]+[E2]+[E1]+[E7]+[G1]).

(103) From this information “USA”=[G4], “Pennsylvania”=[G3], “American”=[G2] and “Gettysburg”=[G1] are Geodata (w, x, y, z) and are represent with (green) Super Glyphs. The system has identified valid (blue) English Keywords and Clusters “War”=[E1], “Civil War”=([E2]+[E1), “American Civil War”=([G2]+[E2]+[E1]), “General”=[E5], “Historical Map”=([E6]+[E8]), “Battle Map’=([E7]+[E8]) and “Historical Battle”=([E6]+[E7]). The system identifies a plurality of purple or personal Super Glyphs such as “General Robert Lee”=[P1], “Abraham Lincoln”=[P2], “General Meade”=[P3], “General Hancock”=[P4], “General Stuart”=[P5], “General Longstreet”=[P6], “General Ulysses Grant”=[P7] and “Robert Lee”=[P8].

(104) Geodata (green) and English language clusters (blue) consists of “Gettysburg” or [G1] is assumed to appear in all, “Pennsylvania” or [G3] in all except request 7, and “USA” or [G4] in all except requests 3 and 7. “American Civil War” or ([G2]+[E2]+[E1]) has a weight of 10, “Civil War” or ([E2]+[E1) has a weight of 5, and “War” or [E1] has a weight of 1.

(105) Personal (purple) Super Glyph consists of “General Robert Lee” or [P1] has a weight of 10, “Robert Lee” or [P8] has a weight of 5, and “General” or [E5] has a weight of 1. “Historical Battle Map” or ([E6]+[E7]+[E8]) has a weight of 10, “Historical Battle” or ([E6]+[E7]) has a weight of 3, and “Battle Map” or ([E7]+[E8]) has a weight of 3.

(106) “War” or [E1] is the most frequent keyword appearing in request 1, 2, 3, and 8, and “Battle” or [E7] is the hottest keyword appearing in 7 and 8. “Robert Lee” or [P8] are the coldest keyword since the last request they appear is in 6 and for the first time in 4.

(107) XCommerce using the Hot & Algorithm parameters determines optimal Super Glyph combinations that will yield the reasonable results. XCommerce identifies “Gettysburg” or [G1] as the primary Geodata (w, x, y, z) filter. The system selects “American Civil War” or ([G2]+[E1]+[E2]) as the anchor primary cluster or Superset (i), and “General Robert Lee” or [P1] as the secondary cluster or Set (i, j) and “Historical Battle” as the tertiary cluster or Subset (i, j, k).

(108) The common related Superset (i) environment size is 8 million W_Rank Basis, distributed in the following manner Superset (1) or “American Civil War” has ( 10/16)*8 million or 5 million W_Rank Basis, Superset (2) or “Civil War” has ( 5/16)*8 million or 2.5 million W_Rank Basis, and “War” the remainder 500,000 W_Rank Basis.

(109) The common related Set (i, j) environment size is 800,000 or 800K W_Rank basis, distributed in the following manner Set (i, 1) “General Robert Lee” has ( 10/16)*800K or 500K W_Rank basis, Set (i, 2) or “Robert Lee” has ( 5/16)*800K or 250KW Rank basis, and Set (i, 3) or “General” the remainder 50K W_Rank Basis.

(110) The common related Subset (i, j, k) environment size is 80,000 or 80K W_Rank Basis, distributed in the following manner Subset (i, j, 1) “Historical Battle Maps” has ( 10/16)*80K or 50K W_Rank Basis, Subset (i, j, 2) or “Historical Battle” and Subset (i, j, 3) “Battle Map” both have ( 3/16)*80K or 15K W_Rank Basis.

(111) The following personal Super Glyphs have the following weights:

(112) [P1] or “General Robert Lee”=100, [P2] or “Abraham Lincoln”=70, [P3] or “General Meade”=70, [P4] or “General Hancock”=80, [P5] or “General Stuart=70, [P6] or “General Longstreet”=90, [P7] or “Ulysses Grant”=80, and [P8] or “Robert Lee”=95. The strongest keyword is “Longstreet” and “General” the lowest.

(113) The following geospatial Super Glyphs have the following weights:

(114) [G1] or “USA”=100, [G2] or “Pennsylvania”=90, [G1+G2] or “USA+Pennsylvania”=70, [G3] or “Gettysburg”=40, [G1+G3] or “USA+Gettysburg=35, and [G1+G2+G3] or “USA+Pennsylvania+Gettysburg”=20.

(115) The system measures by filtering Superset (1) “American Civil War” then Set (i, 2) or Set (i, 3) “Robert Lee” I General then Subset (i, 2, 2) or Subset(i, 3, 3) “Battle Map” to derive the TOP 1000 W_Rank basis. Then the top 1000 W_Rank Basis are filtered against Geodata (w, x, y, z) filter to determine the TOP 100. Finally, the system derives the TOP 10 W_Rank Basis by comparing Super Site and personal Super Glyphs. Cholti ‘cherry picks’ the Top 1 or optimal based upon the highest Super Page weighted page rank. In this example the method was explained step by step, the computer performs all these calculation in one shot up to the TOP 100, then it executes the resource intensive algorithm of ‘triangulation deductive reasoning’ to solve for the optimal solution.

(116) Example #5: Related Requests.

(117) 1. End User's [A], [B], [C], [D], [E], [F], & [G]. [A]=American, [B]=Civil, [C]=War, [D]=Robert, [E]=Lee, [F] Battlefield, [G]=Gettysburg.

(118) a) (A+B+C) is the Superset (i), (D+E) is the Set (i, j), [F] is the Subset (i, j, k), [G] Gettysburg is a Zero Cluster and the center to Geodata(w, x, y, z), [H]=USA, [I]=Pennsylvania, and [J]=Zip Code valid areas around Gettysburg, Pa. [A-F] consist of a left side check mate combination and [G] derives a right side check mate combination.

(119) b) [K]=Harrisburg (717), [L]=Altoona (814) or Williamsport (570), USA [M]=(800) or (888) toll free Area Code in ANI and are active. [K], [L] & [M] are Super Glyph filters. (717)=95, USA (800) or (888)=80, (570) or (814)=70. Not mentioned by being probable Allentown or (484), (610), (835)=60, Pittsburgh(412), (724), (878)=50, and Philadelphia (215), (267)=50. As you can see the telecommunication side of the equation can quickly become complicated, for simplicity only those that have a probability greater than 70 are used. Probabilities are assigned by distance to the center of the circle of calculation.

(120) 2. End User's request is [D], [E], [V], [W]. [D]=Robert, [E]=Lee, [V]=Union, [W] Confederates.

(121) 3. End User's request is [G], [X], [Y]. [G] is Gettysburg, [X] Historical, [Y] Maps.

(122) Requests 1 to 3 are valid and each has a separate Superset (i), Set (i, j) and Subset (i, j, k). Cholti determines the following (i) request 1 is directly related to request 2 via [D]+[E] “Robert Lee”, (ii) request 1 is directly related to request 2 via [G] Gettysburg, and (iii) request 2 is indirectly related 3 via transitive (1->2).

(123) The anchor Superset (1) is “Civil War”, Superset (2) is “American Civil War”, Superset (3) is “Robert Lee”, Superset (4) is “Historical Maps”. The anchor Superset (1) contains the other Superset (i) where i=2 to 4. The system assigns [F] Battlefield, [V] Union, & [W] Confederates as Set (i, j) filters. The Set (i, j) and Subsets (i, j, k) are created by the AV list of all the possible clusters of all the valid and related keyword of request 1-3. Cholti creates a Superset (i) environment of 1,000,000 W_Rank basis based on Civil War, where the “Historical Map” is the hottest filter and “American Civil War” is the coldest.

(124) In this case [G] Gettysburg is the only hot keyword, and the message the end user gives to Cholti is to filter with the [R] side of the brain or geospatial data.

(125) Example #6: Mulligan Requests.

(126) 1. End User's Request [A], [B], [C]. [A]=Blue, [B]=Gray, [C]=Gettysburg.

(127) 2. End User's Request [X1], [Y1], [Z1]. [X1]=US, [Y1]=History, [Z1]=Atlanta.

(128) 3. End User's Request [X2], [Y2], [Z2]. [X2]=Novel=[Y2] Newt, [Z2]=Gingrich

(129) 4. End User's [D], [E], [F], [G], [H], [I], & [C]. [D]=American, [E]=Civil, [F]=War, [G]=Robert, [H]=Lee, [I] Battlefield, [C]=Gettysburg.

(130) 5. (D+E+F) is Superset (i), (G+H) is Set (i, j), [I] is Subset (i, j, k), [C] Gettysburg is a Zero Cluster and the center to Geodata (w, x, y, z), [J]=USA, [K]=Pennsylvania, [L]=Zip Code valid areas with Gettysburg, Pa. [D-I] consist of a [L] side check mate combination. [C I Z1] derives a [R] side check mate combination.

(131) 6. [M]=(717), [N]=[814] or [570] & [O]=[800] or [888] toll free Area Code in ANI and are active. Only those area codes or NPA that are reasonable or have a probability greater than seventy percent are included in this calculation.

(132) 7. [P]=Atlanta (404) or (470) & [Q]=Greater Atlanta (678) or (770) are the Area Code in ANI ranges that become inactive via mulligan. Athens (706), Rome(762), Macon (478), Valdosta (229), and Savannah (912) are probabilistic.

(133) 8. [M], [N], [0], [P], & [Q] are Super Glyph filters. [X1] is validated as Geodata ([US], X, Y, Z). [Z1] is validated as Geodata ([US], [GA], [Atlanta], Z), (Y2+Z2) is a personal purple Super Glyph (for Alternate History Gettysburg Trilogy) and [Y1] [X2] are a yellow Super Glyphs.

(134) The system is able to match/merge requests 1 to 3 as a single request that is linked to {circumflex over ( )}request 4. The keywords used in the match/merge process are assigned as (yellow) unused Super Glyphs. Thus the before request 1 to 3 looks like this [A], [B], [C], [X1], [Y1], [Z1], [X2], [Y2], & [Z2]. The ‘After’ looks like this [A], [B], [C] (yellow) Super Glyphs. [X1], [Y1], [Z1], [X2], [Y2], [Z2], & Geodata ([US], [GA], [Atlanta], Z) are valid Hot & Cold Algorithm variables values used for ‘Triangulation Deductive Reasoning’ or ‘Deductive Reasoning’ as used throughout this document when solving the optimal solution.

(135) Finally the system creates a resultant request based on 1 to 4 that are linked to a single request. [D], [E], [F] is converted to [W1], [G], [H] to [W2], and [Y2], [Z2] to [W3]. The final resultant is [W1]+[W2]+I [A] I [B])+(Geodata ([US], [GA], [Atlanta], Z)+Geodata ([US], [PA], [Gettysburg], Z)|([X1]|[Y1]|[Z1]|[X2]|[W3])).

(136) Example #7 ‘Triangulation Deductive Reasoning’ or Ex Optimal Requests:

(137) In Example #6, once the system creates the optimal Dx hierarchical set, the system is able to value and weight each Super Page and Super Site. As noted before Super Pages and Super Sites are dynamic objects that store a plurality of weights and probabilities for all the valid requests belonging to a session.

(138) The system determines the top 100 pages from the TOP 1,000 W_Rank page results. Then it compares by paragraph, which English language or [L] side of the brain matches have the highest values, and compares to the Super Page known valid Geodata (w, x, y, z). At this time the mulligan keywords in particular ‘Zero Clusters’ are finally used in a ‘Boolean Algebra’ approach to maximize a paragraph weight using the Ex value. The system determines the weight of page to equal the maximum of (Dx Super Page weight, Ex or Paragraph value*2).

(139) The system ‘cherry picks’ the highest value from top to bottom until the list of 100 pages is in ascending order. The highest value is the optimal result and the next nine results are also sent to the end user if the browser engine default result size is 10.

(140) Example #8 Cholti Method Overview:

(141) In Example #1 to #6 most of the queries begin with keyword and numeric combinations that are converted into a regular expression so that the search engine using ‘Boolean Algebra’ can find the best page ranks. Cholti calculates or supplies the preprocessed Ax and Bx values to shrink the size of the environment. The system then uses the Hot & Cold Algorithm parameters and Q(x, y, z) values as filtering means to further shrink each member of the hierarchical set to have the smallest possible size.

(142) The unrelated keywords and mulligan keywords are now used to parse and valorize paragraph by paragraph the content of each page. Then the system selects the highest valued as the optimal result. Once the optimal satisfying environment is known with the best results a mathematical equation is created that describes the hierarchical set characteristics and is then converted into Cholti style unloaded and loaded Glyphs with Q(x, y, z) and optionally with Q(w, x, y, z) values that replaces the end user's regular expression. This regular expression has the essence of the matter of Ax, Bx, Cx, and Dx samples, furthermore it has the Ex or paragraph values to solve for the end user's request. When a request has a common denominator Z_Price bitmap and a plurality of unused independent variables the preprocessed optimal environmental hierarchical set is known with the appropriate Q(w, x, y, z) values leaving the Ex calculation to be determined on the fly.

(143) The Cholti Glyph based mathematical equation when available will significantly reduce the processing power required to respond an end user request. The equation takes into account all the requests within a session to create a final hierarchical set.

(144) Instead of assuming erroneously that a ‘Boolean Algebra’ search always reaches with certitude the final destination. Cholti dynamically adjusts to the end user usage patterns and mimics the end user ongoing trial and error method to reach the final destination, by improving over time as better feedback is obtained.

(145) In a session certitude is achieved each time a significant difference event is detected, unfortunately ‘Boolean Algebra’ search engines consider all events important. Cholti knows betters and uses set theory rules of association and transitivity to link related and unrelated requests, and measures probabilistically if the change between two consecutives request matters.

(146) When the end user make a predefined ‘Z_Price Bitmap’ request that the environment has detected millions of similar request in a specific time frame, the results are always certain and up to date, since the AI Spider continuously in real time updates and improves the content of the Cholti mathematical equation optimal environment. Information that is not in real time is confounded. The greater the time the more unreliable the information becomes. In the case of Cholti real time means upon AI spider detection of significant information as it scans methodically the content of the Internet. Upon detect a new page in the environment XCommerce has the information ranked absent of having to perform the resource intensive and time consuming Master index recalculation for all page ranks.

(147) In this example collectively the improvements mentioned over existing ‘Boolean Algebra’ means is what permits Cholti to achieve certitude.

SUMMARY OF THE INVENTION

(148) We've overcome these “issues” or greatly improved the search optimally by doing the following:

(149) Generally stated end user's requests are converted into the Mayan style Glyphs that have left side and right side of the brain characteristics. The system understands that each request on its own can be optimally satisfying, and also knows that some require trial and error method. To solve this dilemma the optimizer creates Super Glyph that has weighted value for a plurality of instances within a session.

(150) At a minimum the system needs to be a large mainframe, a parallel distributed supercomputer or preferably a MPS™ supercomputer. The system has a large data warehouse means that store a ‘CORE List’ that consists of statistics for each keyword or cluster. Consequently, at its core the technology must perform the following steps:

(151) a) Identify each keyword interactively.

(152) b) Validate each keyword to belong to a group.

(153) c) Verify if a keyword will be an active participant in the process of reducing the size of the environment.

(154) d) Estimate the Ax or ‘Before’ sample environment size from the keywords typed by the end user.

(155) e) Determine if the end user's request is significant.

(156) f) Create Basic Glyphs that best reflects the essence of the Ax or ‘Before’ request that will permit the creation of a hierarchical set consisting of a plurality of valid Superset (i), Set (i, j) and Subset (i, j, k).

(157) g) Reorganize the end user's request to create Advanced Glyphs that further distills and shrinks the size of the environment using the Bx or ‘After’ request.

(158) h) Recognize Advanced Glyph so that the server can determine if it already exist in the ‘CORE List’. If the Advanced Glyph exists in the ‘CORE List’ the optimal response is readily available and preprocessed no further calculations are required. Otherwise, the system must continue with Cx or ‘Improved’ and Dx or ‘Optimal’ samples.

(159) i) Request the server to perform the ‘Improved’ sample by hierarchical distributing the search amongst subordinate based on ownership of the primary, secondary and tertiary keyword or cluster. The Basic and Advanced Glyphs are used to assign size parameter to each valid set of the hierarchical set.

(160) j) Adjust dynamically the value of each keyword and cluster.

(161) k) Exclude identified Zero Cluster keywords.

(162) l) Emphasize through rules of association and transitivity a plurality of request that are considered to have common denominator elements and are then correlated into a partial environment. The partial environment consists of a plurality of request. The partial environment retains the characteristic of each individual request.

(163) m) Deemphasize unrelated keywords to the last significant end user's request. This process is also known as Mulligan and is uses set theory to determine the relationship between a keyword and the last significant request.

(164) n) Maximize keyword values by using the Hot Algorithm that measures the usage pattern and significance of a keyword in a session.

(165) o) Minimize keyword values by using the Cold Algorithm that weights keyword irrelevancy. ‘Zero Clusters’ and unrelated keywords have a reasonable probability of hiding the optimal result.

(166) p) Correlate the plurality of partial environments into the Cx or ‘Improved Samples. This process draconically reduces the environment size using Hot & Cold Algorithm parameters and stores the essence of the matter into Super Sites.

(167) q) Assign a corporate signature to each Super Site.

(168) r) Pick the small Cx Sample top results of each hierarchical set to generate a collection of valid Super Pages.

(169) s) Distill the small Cx Sample using geospatial dimensions that have exact or estimated latitude and longitude components such as Country, Region, LATA, Zip Code, IP Address and ANI.

(170) t) Commercialize keywords by available Corporate Media resources and priorities.

(171) u) Decipher with reasoning the ‘Dx’ or optimal sample.

(172) v) Translate the end user's language based request into a Cholti language Super Glyph equation.

(173) w) Respond to the client software with the optimal response. The optimal results may be identified as already existing in the preprocessed ‘CORE List’ in step h) “recognize and all calculations are preprocessed. Alternatively when derived by using steps i) “request” to v) “translate”.

(174) x) Display to the end user the optimal request.

(175) y) Recalculate each time the “optimal button” is clicked in the web browser and significant difference event is estimated compared to the latest Super Glyph equation or partial environment.

(176) z) Consolidate a plurality of partial environment into a resultant environment that is contained with the valid environmental size of the hierarchical set.

(177) These and other aspects, features, and advantages of the present invention will become more readily apparent from the attached drawings and the detailed description of the preferred embodiments, which follow.

(178) The Internet search engine methods described herein mimick the function of the human brain by creating a language-based or left brain equation and a geospatial or right brain equation. The terms language-based and left brain are used interchangeably. The terms geospatial and right brain are used interchangeably.

(179) While the preferred embodiments of the invention have been described above, it will be recognized and understood that various modifications can be made in the invention and the appended claims are intended to cover all such modifications which may fall within the spirit and scope of the invention.