Searching methods

• US 20080055049A1
• Filed: 07/24/2007
• Published: 03/06/2008
• Est. Priority Date: 07/28/2006
• Status: Abandoned Application

• Current Assignee: Social Fabric Corporation
• Original Assignee: Social Fabric Corporation
• Inventors: Giaccherini, Thomas, Tuck, Edward, Sturza, Mark, Weill, Lawrence
• Application Number:
  US11/881,153
• Publication Number:
  US 20080055049A1
• Time in Patent Office: Days
• US Class Current: 340/10.31
• CPC Class Codes: G06F 16/24 Querying

Abstract

Methods and apparatus for using an energy emanating device that finds a person (17a,b) object or system based on preselected attributes (33) stored in the energy emanating device (10) are disclosed. Searching Methods are used to compare the attributes (33) of individuals, and a match is determined based upon the correlation of these attributes (33). The matching is accomplished using a variety of algorithms, including a “Distance Formula.” In alternative embodiments, the invention may be used in a search engine.

Claims

1. An apparatus comprising:

a first transceiver (10a);

said first transceiver (10a) including a first memory (32a);

said first memory (32a) for storing a first attribute (33a) selected by a first user (17a);

said first transceiver (10a) including a first match indicator (16a);

a second transceiver (10b);

said second transceiver (10b) including a second memory (32b);

said second memory (32b) for storing a second attribute (33b) selected by a second user (17b);

said second transceiver (10b) including a second match indicator (16b);

said first transceiver (10a) for emitting a first interrogation signal (110);

said first interrogation signal (110) being received by said second transceiver (10b);

said first match indicator (16a) on said first transceiver (10a) being activated when said first interrogation signal (110) finds a match between said first attribute set (33a) stored in said first memory (32a) in said first transceiver (10a) and said second attribute set (33b) stored in said second memory (32b) in said second transceiver (10b);

a wireless network (50);

a first network radio (52a);

said first network radio (52a) being co-located with said first transceiver (10a);

said first network radio (52a) being used to communicate over said wireless network (50); and

said match being determined using a distance formula.

2. An apparatus as recited in claim 1, in which said distance formula is used to correlate said first attribute set (33a) and said second attribute set (33b).

3. An apparatus as recited in claim 1, in which said distance formula is stored in said first and said second memory (32a, 32b).

4. An apparatus as recited in claim 1, in which said distance formula is incorporated in software that runs on a server that enables the operation of an Internet dating website.

5. An apparatus as recited in claim 1, in which a correlation between said first and said second attribute sets (33a, 33b) may be computed using the expression

$d$

( F , M ) = ∑

n = 1 N ⁢

( F n - M n ) 2

where an individual possessing the first set of attributes is identified as F;

an individual possessing the second set of attributes (x₂,y₂) is identified as M;

d(F, M) is a distance between F and M;

N is a number of attributes;

F₁, F₂, . . . , F_N represent a plurality of attribute values for F; and

M₁, M₂, . . . , M_N represent a plurality of attribute values for M.

6. An apparatus as recited in claim 1, in which a correlation between said first and said second attribute (33a, 33b) may be computed using the expression

$d$

( F , M ) = ∑

n = 1 N ⁢

w n ⁢



F n - M n 

where | | denotes absolute value; and

w_n is a number between 0 and 1 which assigns a weight to an nth attribute.

7. An apparatus as recited in claim 6, in which a value for a relative weight is computed using the expression:

$w_n=\frac{W_n}{\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>W_k}$

where W_n is an initially chosen weight for the n^th attribute.

8. An apparatus as recited in claim 1, in which a distance from a first desired attribute set to a second existing attribute set is computed using the expression:

$d<br><br>\left(\mathrm{FD},\mathrm{ME}\right)=\underset{n=1}{\overset{N}{\sum <br><br>}}<br><br>w_n<br><br><br><br>{\mathrm{FD}}_n-{\mathrm{ME}}_n<br><br>$

where FD is a desired attribute set of a first individual; and

ME is an existing attribute set of a second individual.

9. An apparatus as recited in claim 1, in which a bidirectional match may be computed using the expression

$d_{\mathrm{BDE}}$

( F , M ) = d ⁡

( FD , ME ) + d ⁡

( MD , FE ) 2

where FD is a desired attribute of a first individual;

ME is an existing attribute of a second individual;

MD is a desired attribute of said second individual; and

FE is an existing attribute of said first individual.

10. An apparatus as recited in claim 1, in which a relative weight w_n for all attributes may be computed using the expression:

$w_n=\{\begin{array}{cc}<br><br>1& <br><br>\mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>n\in <br><br>R\\ <br><br>\frac{W_n}{\underset{k\in <br><br>A-R}{\sum <br><br>}<br><br>W_k}& <br><br>\mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>n\in <br><br>A-R\end{array}$

Where ε

means “

is contained in”

; and

A-R denotes a set of attributes that are in A but not in R.

11. An apparatus as recited in claim 1, in which said distance is converted to a correlation using the expression

c(F,M)=1−

d(F,M).

16. A method as recited in claim 2, in which a correlation between said first and said second attribute sets (33a, 33b) may be computed using the expression

$d$

( F , M ) = ∑

n = 1 N ⁢

( F n - M n ) 2

where an individual possessing the first set of attributes is identified as F;

an individual possessing the second set of attributes (x₂,y₂) is identified as M;

d(F, M) is a distance between F and M;

N is a number of attributes;

F₁, F₂, . . . , F_N represent a plurality of attribute values for F; and

M₁, M₂, . . . , M_N represent a plurality of attribute values for M.

12. A method comprising the steps of:

providing a device (10);

said device (10) including a memory (32) and a radio (28);

storing a distance formula in said memory (32);

storing an attribute in said memory (32); and

using said device (10) to find a good match based on a correlation produced by said distance formula.

13. A method as recited in claim 12, in which said distance formula is used to correlate said first attribute (33a) and said second attribute (33b).

14. A method as recited in claim 12, in which said distance formula is stored in said first and said second memory (32a, 32b).

15. A method as recited in claim 12, in which said distance formula is incorporated in software that runs on a server that enables the operation of an Internet dating website.

17. A method as recited in claim 12, in which a correlation between said first and said second attribute sets (33a, 33b) may be computed using the expression

$d$

( F , M ) = ∑

n = 1 N ⁢

w n ⁢



F n - M n 

where | | denotes absolute value; and

w_n is a number between 0 and 1 which assigns a weight to an nth attribute.

18. A method as recited in claim 17, in which a value for a relative weight is computed using the expression:

$w_n=\frac{W_n}{\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>W_k}$

where W_n is an initially chosen weight for the n^th attribute.

19. A method as recited in claim 12, in which a distance from a first desired attribute set to a second existing attribute set is computed using the expression:

$d<br><br>\left(\mathrm{FD},\mathrm{ME}\right)=\underset{n=1}{\overset{N}{\sum <br><br>}}<br><br>w_n<br><br><br><br>{\mathrm{FD}}_n-{\mathrm{ME}}_n<br><br>$

where FD is a desired attribute of a first individual; and

ME is an existing attribute of a second individual.

20. A method as recited in claim 12, in which a bidirectional match may be computed using the expression

$d_{\mathrm{BDE}}$

( F , M ) = d ⁡

( FD , ME ) + d ⁡

( MD , FE ) 2

where FD is a desired attribute of a first individual;

ME is an existing attribute of a second individual;

MD is a desired attribute of said second individual; and

FE is an existing attribute of said first individual.

21. A method as recited in claim 12, in which a relative weight w_n for all attributes may be computed using the expression:

$w_n=\{\begin{array}{cc}<br><br>1& <br><br>\mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>n\in <br><br>R\\ <br><br>\frac{W_n}{\underset{k\in <br><br>A-R}{\sum <br><br>}<br><br>W_k}& <br><br>\mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>n\in <br><br>A-R\end{array}$

Where ε

means “

is contained in”

; and

A-R denotes a set of attributes that are in A but not in R.

22. A method as recited in claim 12, in which said distance is converted to a correlation using the expression

c(F,M)=1−

d(F,M)

23. A method comprising the steps of:

accessing a master set (MS);

said master set (MS) including a plurality of sources of information (S1;

S2, S3, S4, S5, S6, S7, . . . S{N});

generating a set of attributes (54);

said set of attributes (54) including a plurality of individual attributes (54A, 54B, 54C . . . 54D) and collectively describing a target (56);

providing a distance formula (Expression

     1);

computing a set of relevance distances (58);

said set of relevance distances (58) including a plurality of individual distances (58A, 58B, 58C, 58D) between each of said individual attributes (54A, 54B, 54C . . . 54D) and said plurality of sources of information (S1;

S2, S3, S4, S5, S6, S7, . . . S{N});

selecting a subset of sources of information (S1′

, S2′

, S3′

); and

ranking said subset of sources of information (S1′

, S2′

, S3′

) in the general order of shortest relevance distances (58) measured between said subset of sources of information and said set of attributes (54).

24. A method as recited in claim 23, further comprising the step of:

performing a page rank search plurality of individual attributes (54A, 54B, 54C . . . 54D) to reduce the total number of sources of information (S1;

S2, S3, S4, S5, S6, S7, . . . S{N}) prior to using said distance formula (Exp.

     1) to measure said relevance distances (58).

25. A method as recited in claim 23, in which said plurality of sources of information includes a web page.

26. A method as recited in claim 23, in which said plurality of sources of information includes a record.

27. A method as recited in claim 23, in which said plurality of sources of information includes a document.

28. A method as recited in claim 23, in which said plurality of sources of information includes an address of an RFID device.

29. A method as recited in claim 23, in which the method step of ranking said subset of sources of information (S1′

, S2′

, S3′

) in the general order of shortest relevance distances (58) measured between said subset of sources of information and said set of attributes (54) is used to create a website for finding generic products.

30. A method as recited in claim 23, in which the method step of ranking said subset of sources of information (S1′

, S2′

, S3′

) in the general order of shortest relevance distances (58) measured between said subset of sources of information and said set of attributes (54) is used to create a website for finding a replacement part.

31. A method of searching for a match comprising the steps of:

providing a first set (F,MS) of data identifiers (F_n,SI);

generating a second set of user-selected data identifiers (M_n,54) relating to a target (M,56);

computing a set of relevance distances (58) comprising a plurality of individual distances (58A-D) between respective data identifiers of the first and second sets;

providing a distance formula which is a function which combines the plurality of individual distances; and

applying the formula to determine an overall degree of match.

32. A method according to claim 31, wherein weighting factors W_n are assigned to the identifiers.

33. A method according to claim 31, wherein the data identifiers (F_n) of the first set relate to a first entity (F) and the data identifiers (M_n) of the second set relate to a second entity (M), whereby the formula determines an overall degree of match between the entities.

34. A method according to claim 33, wherein the distance formula provided is:

$d<br><br>\left(F,M\right)=\sqrt{\underset{n=1}{\overset{N}{\sum <br><br>}}<br><br>{\left(F_n-M_n\right)}^2}$

where an entity possessing the first set of attributes is identified as F;

an entity possessing the second set of attributes is identified as M;

d(F, M) is a distance between F and M;

N is a number of attributes;

F₁, F₂, . . . , F_N represent a plurality of attribute values for F; and

M₁, M₂, . . . , M_N represent a plurality of attribute values for M.

35. A method according to claim 34, wherein the distance formula provided is:

$d<br><br>\left(F,M\right)=\underset{n=1}{\overset{N}{\sum <br><br>}}<br><br>w_n<br><br><br><br>F_n-M_n<br><br>$

where an entity possessing the first set of attributes is identified as F;

an entity possessing the second set of attributes is identified as M;

d(F, M) is a distance between F and M;

N is a number of attributes;

F₁, F₂, . . . , F_N represent a plurality of attribute values for F;

M₁, M₂ . . . M_n represent a plurality of attribute values for M;

| | denotes absolute value; and

w_n is a number between 0 and 1 which assigns a weight to an nth attribute.

36. A method according to claim 35, in which a value for a relative weight is computed using the expression:

$w_n=\frac{W_n}{\underset{k=1}{\overset{N}{\sum <br><br>}}<br><br>W_k}$

where W_n is an initially chosen weight for the n^th attribute.

37. A method according to claim 33, wherein a distance from a first desired attribute set to a second existing attribute set is computed using the expression:

$d<br><br>\left(\mathrm{FD},\mathrm{ME}\right)=\underset{n=1}{\overset{N}{\sum <br><br>}}<br><br>w_n<br><br><br><br>{\mathrm{FD}}_n-{\mathrm{ME}}_n<br><br>$

where FD is a desired attribute set of a first individual;

ME is an existing attribute set of a second individual;

| | denotes absolute value; and

w_n is a number between 0 and 1 which assigns a weight to an nth attribute.

38. A method according to claim 32, wherein a bidirectional match is computed using the expression:

$d_{\mathrm{BDE}}<br><br>\left(F,M\right)=\frac{d<br><br>\left(\mathrm{FD},\mathrm{ME}\right)+d<br><br>\left(\mathrm{MD},\mathrm{FE}\right)}{2}$

where FD is a desired attribute of a first individual;

ME is an existing attribute of a second individual;

MD is a desired attribute of said second individual; and

FE is an existing attribute of said first individual.

39. A method according to claim 33, wherein a relative weight w_n for all attributes is computed using the expression:

$w_n=\{\begin{array}{cc}<br><br>1& <br><br>\mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>n\in <br><br>R\\ <br><br>\frac{W_n}{\underset{k\in <br><br>A-R}{\sum <br><br>}<br><br>W_k}& <br><br>\mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>n\in <br><br>A-R\end{array}$

where ε

means “

is contained in”

; and

A-R denotes a set of attributes that are in A but not in R.

40. A method according to claim 33, wherein the distance is converted to a correlation using the expression

c(F,M)=1−

d(F,M).

41. A method according to claim 31, wherein the data, identifiers of the first set are sources of information (S1-S{N}) of a master set (MS) and the data identifiers of the second set are individual attributes (54A-54D) collectively describing the target (56), and wherein the relevance distances (58) computed in the computing step comprise a plurality of individual distances (58A, 58B, 58C, 58D) between each of said individual attributes (54A, 54B, 54C . . . 54D) and said plurality of sources of information (S1;

S2, S3, S4, S5, S6, S7, . . . S{N}).

42. A method according to claim 41, further comprising the steps of:

selecting a subset of the sources of information (S1′

, S2′

, S3′

) in the general order of shortest relevance distances (58) measured between said subset of sources of information and said set of attributes (54).

43. A method according to claim 41, further comprising the step of:

performing a page rank search plurality of individual attributes (54A, 54B, 54C . . . 54D) to reduce the total number of sources of information (S1;

S2, S3, S4, S5, S6, S7, . . . S{N}) prior to applying the distance formula to measure said relevance distances (58).

44. A method according to claim 40, further comprising the steps of:

identifying non-relevant linked attributes; and

excluding from the search sources of information containing said non-relevant linked attributes.

45. A method according to claim 31, wherein the at least one sources of information include a web page.

46. A method according to claim 31, wherein the sets of data identifiers are encoded into respective vectors and the computing step and the subsequent steps comprise:

evaluation of the metric between each vector and other vectors; and

decoding the metric.

47. A method according to claim 46, wherein the metric used is a sum of N components, where N is the dimension of the encoded vector space, each component being a specific function of a pair of corresponding values of two vectors.

48. A method according to claim 46, where the metrics are weighted using an N×

N weighting matrix.

50. Apparatus according to claim 47, wherein said first set of attribute values includes descriptive and/or functional words and/or phrases.

49. Apparatus for searching for a match comprising means for storing a first set of attribute values (F_n) of at least one first entity (F, S) means for inputting a user-selected second set of attribute values (M_n, 54) relating to a second entity (M, 56), means for storing a distance formula which is a function of the degree of match between respective values of sets of attribute values, and applying the formula to the first and second sets to determine an overall degree of match between the first entity and the second entity.