WO1999050759A1

WO1999050759A1 - Method and apparatus for using ideas and concepts within computer programs

Info

Publication number: WO1999050759A1
Application number: PCT/US1999/006935
Authority: WO
Inventors: Kevin A. P. Kirchman
Original assignee: Worldfreenet Inc
Current assignee: Worldfreenet Inc
Priority date: 1998-03-30
Filing date: 1999-03-29
Publication date: 1999-10-07
Anticipated expiration: 2000-09-30
Also published as: BR9909292A; AU3216699A; EP1073970A1; JP2003526130A; CA2326385A1

Abstract

A method and apparatus that allows computer programs to define ideas and concepts symbolically is provided. The method and apparatus include a grammar that may be used to represent any concept. Sentences are parsed, using this grammar, into their component parts. As part of the parsing process, each word is compared to the contents of a dictionary database. The dictionary database and a set of tense-mood tables are used to identify individual words as concepts, entities, actions or qualifiers. The parsing process creates a data structure (200) for each sentence. The data structure organizes the sentence into its component parts, such as an ID field (202) and POC fields (204). The data structures for different sentences can be compared to determine matching or similarity. The data structures can also be processed to accomplish more advanced ends, such as reasoning systems or expert systems.

Description

Method And Apparatus For Using Ideas and Concepts Within Computer

Programs

FIELD OF THE INVENTION This application relates generally to the use of human concepts and ideas

within interactive computer applications. More specifically, the present invention

includes a method and apparatus that allows ideas to be defined symbolically for

processing and matching with other ideas.

BACKGROUND OF THE INVENTION The inability of computer applications to process human ideas and concepts

has many disadvantages. As an example, consider the case where a person wants

an answer for the question: "what is the largest dinosaur?" In today's world, it is

increasingly common to look for the answer to such questions on the World Wide

Web. This type of searching is typically done by formulating and submitting queries

to the many search engines available on the web. Queries of this type are

formulated using keywords along with connectors and qualifiers.

Unfortunately, the use of the queries means that the search engine has no

real way of knowing what the user is actually searching for. The search engine never

sees or appreciates the question: "what is the largest dinosaur?" As a result, the

search engine is limited in the type of search it can perform. It can look for web sites

that contain the specified keywords modifies by the specified connectors and

qualifiers. It cannot compare the content of web sites to determine if there is a match

for the largest dinosaur question (the search engine is also unaware of the meaning

of that content). This makes the searching process rather hit and miss. In many

1 cases, search engines users scan many sites to find the content they are looking for.

In other cases, the desired content is never found.

This failure to understand ideas and concepts also complicates the interaction

between users and search engines (and other applications). The user is forced to

translate the question "what is the largest dinosaur?" into an acceptable query. This

is much more difficult than simply typing in the original question. The arcane nature

of these queries often means that the user will make several attempts before

formulating an acceptable query.

The web searching example is, of course, a highly limited example. The

failure to understand ideas and concepts extends to an almost unlimited number of

different applications in an almost unlimited number of ways. For these reasons, a

need exists for solutions that allow computers and their applications to more fully

understand and process human ideas and concepts.

SUMMARY OF THE INVENTION An embodiment of the present invention includes a method and apparatus

that allows computer programs to define ideas and concepts symbolically. The

symbolic ideas and concepts may be processed and matched with other

symbolically defined ideas.

The present invention defines a grammar that may be used to represent any

concept. Proper concepts (the equivalent of complete sentences) form the highest

level of this grammar. Entities, and actions (the equivalent of individual words) form

the lowest level of the grammar. Proper concepts are defined as recursive

combinations of concepts, entities, actions and qualifiers. Sentences are parsed, using this grammar, into their component parts. As

part of the parsing process, each word is compared to the contents of a dictionary

database. The dictionary database and a set of tense-mood tables are used to

identify individual words as concepts, entities, actions or qualifiers.

The parsing process creates a data structure for each sentence. The data

structure organizes the sentence into its component parts. The data structures for

different sentences can be compared to determine matching or similarity. The data

structures can also be processed to accomplish more advanced ends, such as

reasoning systems or expert systems.

Advantages of the invention will be set forth, in part, in the description that

follows and, in part, will be understood by those skilled in the art from the description

herein. The advantages of the invention will be realized and attained by means of

the elements and combinations particularly pointed out in the appended claims and

equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part

of this specification, illustrate several embodiments of the invention and, together

with the description, serve to explain the principles of the invention.

Figure 1 is a block diagram of a host computer system shown as an

exemplary environment for an embodiment of the present invention.

Figure 2 is a block diagram of a concept data structure as used by an

embodiment of the present invention. Figure 3 is a block diagram of a concept represented as a combination of two

concept data structures.

Figure 4 is a block diagram of a possible implementation of the concept data

structure as used by an embodiment of the present invention.

Figure 5 is a second block diagram of a concept represented as a

combination of two concept data structures.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now by made in detail to preferred embodiments of the

invention, examples of which are illustrated in the accompanying drawings.

Wherever convenient, the same reference numbers will be used throughout the

drawings to refer to the same of like parts.

ENVIRONMENT

In Figure 1 , a computer system 100 is shown as a representative environment

for the present invention. Structurally, computer system 100 includes a processor, or

processors 102, and a memory 104. An input device 106 and an output device 108

are connected to processor 102 and memory 104. Input device 106 and output

device 108 represent a wide range of varying I/O devices such as disk drives,

keyboards, modems, network adapters, printers and displays. Each node 102 may

also includes a disk drive 110 of any suitable disk drive type (equivalent^, disk drive

110 may be any non-volatile mass storage system such as "flash" memory).

GRAMMAR

An embodiment of the present invention includes a method and apparatus

that allows computer programs to define and manipulate concepts symbolically. The

symbolic concepts may be processed and matched with other symbolically defined

concepts. Part of the method and apparatus of the present invention are a grammar

that defines how concepts and ideas can be symbolically represented. A basic

premise of this grammar is the notion that any concept may be representing in

cannonical form. One possible cannonical form, referred to as the general form of a

concept, is: C ≡ £(Q) A(Q) E(Q)

where

C ≡ Concept (Complex)

E ≡ Concept of Entity

E ≡ Concept of Action

Q ≡ Concept of Qualification

The symbol '≡ establishes identity.

Concept entities and concept actions are words, concept qualifiers are further

defined as any subset of:

Q ≡ £( ) A(Q) E(Q) or

Q ≡ Q Q Q ...

where the concept of action can be a noun in the statement. An indirect object is

considered as qualifying the concept of action. It is important to recognize that

subordinate, coordinate and independent clauses are being represented as

qualifying the entity or action that they modify and can be considered as limitations

upon the sets of referents of those terms.

Subordinating qualifiers are of the form:

Q(Q)

while coordinating qualifiers are of the form:

QQ

A Proper Concept, the equivalent of a complete sentence, is either one of the forms:

E(Q) A(Q)

E(Q) A(Q) E(Q)

ooC ooC

where Q is optional and

oo = Concept of Connectivity

The last concept above is a compound concept, representing the combination

of two or more complex concepts with optional connectives, such as 'if, 'and' or

'that'. Compound concepts include conditional statements, intentional statements

and lists.

EXAMPLES OF CONCEPTS REPRESENTED SYMBOLICALLY

The following examples serve to demonstrate the use of the previously

defined grammar. To use the grammar, words within a sentence being represented

are identified with reference to a dictionary database (within the following examples,

this is represented by subscripted letters). The ending of each word is compared to a

tense-mood table and categorized as one of E, A or Q.

Example 1 : The wood was burnt.

E: wood the

A: burn past

E:

E_p (Q_in) A_x (Q_rt)

The general form of this concept is: C ≡ E_p (Q_in) A_x (Q_rt)

In this case the subject 'wood' is not qualified with respect to quantity because no

quantity is stated.

Example 2: Those men skillfully produced blue houses from that red wood.

E: human male plural

A: produced imperfect skillfully from wood that

E: hose plural blue

C E_h [Q_ιn Q_ge Q_ιn]

Ax[Q_rtQ_rqQ_S0[E_p(Q,_nQ_qu)]]

E_p [Q_nu eg

Example 3: It is raining.

The indefinite pronoun here must be made explicit. Rephrased:

The weather is the state of raining.

C - E_p (CO A_x (Q_qu) E_p (Q_st) Example 4: The pretty ladies danced gracefully before the guests.

C ≡ E_h [Q_ge Q_ιn Q_nu Q_rq]

A_h[Q_rtQ_rqQ_rp[E_hQ_caQ_ιnQ_n ]

Example 5: Fred, the builder who lives in the woods, fabricated a cart for

hauling his tools.

C ≡ E_h [Q_ιn [Q_ιn Q^ [A_h [Q_rt Q_rp [Ep Qin]]]]]

A_h [Q_rt Q_mo [A_h [Q_rt E_p [Q_nu Q_po]]]] E_p

Example 6: He cooled the hot metal by pouring cold water on it.

C ≡ E_h [Q_ιn Q J

A_p [Q_rt Q_ma [A_n [Q_rt Q_rp [E_p [Q_ιn Q_rq]]] E_p [Q_rq]]] E_p [Q_ιn Q_rq]

Example 7: The present king of France is bald.

C ≡ E_h [Q_ca [Q_S0] Q_ιn Q_rt] A_x E_h Q_qu]

Example 8: 'It was to their native law that sixteenth century Scottish Jurists

applied most successfully the humanistic methods first developed on the continent in

the study of Roman Law'

(sixteenth century Scottish Jurists)

(applied most successfully) (to their native law)

(the humanistic methods which were first developed on the continent in the

study of Roman Law)

C E_h [Q^ Q_so Q_rt Q_no]

Ax [Q_ro [Q_π] Q_re [E_m [Q_so Q_po]]]

Em [Q_ιn Q_nu Q_qu A_x [Q_rt Q_rp [E_p [Q ] Q_ma [A_h [E_m Q_so]]]]]

The word 'law' refers to as concepts (though recorded as physical entities)

and thus are Mental Entities (EJ. The word 'methods' also refers to concepts. The

words 'were first' are considered together as a claim of Relative Temporality,

10 although 'first' might also be considered as a qualification of Source. The words 'in

the study of are considered to 'by means of studying'.

Concepts and ideas are recursively parsed using the grammar. During the

parsing process, a data structure is created to represent the concept or idea being

parsed. The data structure is a tree-shaped hierarchy. Each node in the tree is a

data structure of the form shown as concept data structure 200 of Figure 2. Concept

data structure 200 includes an ID 202 and three POC fields 204a through 204c. ID

202 allows each concept data structure 200 to be uniquely identified. The inclusion

of three POC fields 204 follows the general form for concepts described above. Each

POC field 204 can represent an action or entity. POC fields can also point to other

concept data structures 200. This allows POC fields 204 to point to other concepts.

This is shown, for example, in Figure 3 where the concept "That the stock went up is

a good sign" is shown as a combination of two concept data structures 200a and

200b.

Figure 4 shows one possible implementation for concept data structure 200.

Concept data structure 200 of Figure 4 includes fields for ID, Word, Wordtype, Q1 ,

Q2, Q3, Q4, Qualified List Pointer, Subordinate Concept Pointer, PrevPointer,

PostPointer, Program and Referents. The implementation of Figure 4 is intended to

be representative. Other implementations may be used without departing from the

spirit of the present invention.

Figure 5 shows a concept that has been parsed into the tree data structure

described in regard to Figures 2 though 4. In this case, the concept includes an

initial entity ("elword") followed by an action ("alword) followed by a second entity

11 ("e2word). The initial entity is qualified by a second concept ("Cword"). The second

concept is composed of still more concepts.

Data structures of the type described in Figures 2 through 5 are recursively

constructed for each proper concept (sentence) that is represented using the

method of the present invention. Once represented as a data structure of this type, a

sentence or concept can be manipulated within computer programs. In particular,

matching between concepts can be determined by comparing their data structures.

For matching, data structures are examined to determine if they have the same (or

similar structures). The contents of the data structures are also compared to

determine sameness or similarity.

Other embodiments be apparent to those skilled in the art from consideration

of the specification and practice of the invention disclosed herein. It is intended that

the specification and examples be considered as exemplary only, with a true scope

of the invention being indicated by the following claims and equivalents.

12

Claims

WHAT IS CLAIMED IS:

1. A method for creating a symbolic representation of a sentence, the

method comprising:

recursively parsing the sentence to isolate one or more concepts, each

concept being a cannonical arrangement of entities, actions and qualifiers;

creating concept data structures to represent each respective concept

that is isolated; and

linking the concept data structures to form a hierarchical data structure

representing the sentence.

2. A method as recited in claim 1 wherein each concept is a cannonical

arrangement of the form E(Q) A(Q) E(Q) where E is an entity, A is an action and Q is

a qualifier.

3. A method as recited in claim 2 wherein each qualifier is a list of

qualifiers or is a cannonical arrangement of the form E(Q) A(Q) E(Q).

4. A method as recited in claim 1 further comprising the step of

comparing at least one word in the sentence to the contents of a dictionary database

to determine if the word is a qualifier, entity or action.

5. A method as recited in claim 4 wherein the dictionary database

includes tense-mood tables.

6. A method for creating a symbolic representation of a sentence, the

method comprising:

13 using a dictionary database to categorize words in the sentence as

qualifiers, entities or actions;

grouping the qualifiers entities and actions into concepts, each concept

being a cannonical arrangement of entities, actions and qualifiers;

creating concept data structures to represent each respective concept

that is created; and

linking the concept data structures to form a hierarchical data structure

representing the sentence.

7. A method as recited in claim 6 wherein each concept is a cannonical

a qualifier.

8. A method as recited in claim 7 wherein each qualifier is a list of

qualifiers or is a cannonical arrangement of the form E(Q) A(Q) E(Q).

9. A method as recited in claim 6 further comprising the step of

to determine if the word is a qualifier, entity or action.

10. A method as recited in claim 9 wherein the dictionary database

includes tense-mood tables.

11. A computer program product comprising a computer usable medium

having computer readable code embodied therein, the computer readable program

14 code devices configured to cause a computer system to perform a method for

creating a symbolic representation of a sentence, the method comprising:

rendering the image portion at a higher resolution to create a texture;

retrieving the rendered image portion as a texture;

filtering the texture to produce a minified texture; and

applying the minified texture to a quadrilateral within the graphics

image.

12. A computer program product as recited in claim 11 wherein each

concept is a cannonical arrangement of the form E(Q) A(Q) E(Q) where E is an

entity, A is an action and Q is a qualifier.

13. A computer program product as recited in claim 12 wherein each

qualifier is a list of qualifiers or is a cannonical arrangement of the form E(Q) A(Q)

E(Q).

14. A computer program product as recited in claim 11 wherein the

method further comprises the step of comparing at least one word in the sentence to

the contents of a dictionary database to determine if the word is a qualifier, entity or

action.

15. A computer program product as recited in claim 14 wherein the

dictionary database includes tense-mood tables.

15