US2007271261A1PendingUtilityA1
Data ranking with a lorentzian fuzzy score
Est. expirySep 12, 2021(expired)· nominal 20-yr term from priority
Y10S707/99935G06F 16/2468Y10S707/99936
32
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Cited by
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Claims
Abstract
The present invention relates to a method for searching a document database such as the Internet and ranking the results obtained from such a search. The invention also relates to ranking of a set of numerical data according to a set of user specified preferences, including target range, fuzziness and bias. A fuzzy score is calculated for each database record satisfying a query and the results ranked according to fuzzy score. The fuzzy score is calculated using a Lorentzian fuzzy score formula.
Claims
exact text as granted — not AI-modified1 . A method for ranking a plurality of elements, wherein a plurality of numerical parameters characterizes each element, the method comprising:
prior to ranking receiving an indication of at least one of fuzziness and bias as inputs to ranking; ranking a plurality of elements based at least in part on an aggregate score;
the aggregate score based at least in part on a plurality of Lorentzian fuzzy scores;
each Lorentzian fuzzy score based at least in part on a numerical parameter characterizing each element, wherein the formula used to calculate each Lorentzian fuzzy score is determined at least in part based on the polarity of the bias input.
2 . The method of claim 1 , wherein at least one of the numerical parameters is a target range having a minimum numeric value and a maximum numeric value.
3 . The method of claim 2 , wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
1
+
(
x
-
x
min
a
1
)
2
x
<
x
min
1
+
β
*
(
x
-
x
min
)
x
max
-
x
min
x
min
≤
x
≤
x
max
1
+
β
1
+
(
x
-
x
max
a
2
)
2
x
max
<
x
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
4 . The method of claim 2 , wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
-
β
1
+
(
x
-
x
min
a
1
)
2
x
<
x
min
1
+
β
*
(
x
-
x
max
)
x
max
-
x
min
x
min
≤
x
≤
x
max
1
1
+
(
x
-
x
max
a
2
)
2
x
max
<
x
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
5 . The method of claim 1 , wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
1
+
(
x
-
x
_
a
1
)
2
x
<
x
_
1
x
=
x
_
1
+
β
1
+
(
x
-
x
_
a
2
)
2
x
>
x
_
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
6 . The method of claim 1 , wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
-
β
1
+
(
x
-
x
_
a
1
)
2
_
x
<
x
_
1
x
=
x
_
1
1
+
(
x
-
x
_
a
2
)
2
x
>
x
_
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
7 . The method of claim 1 wherein the aggregate score is based at least in part on summing the plurality of Lorentzian fuzzy scores.
8 . The method of claim 1 wherein after calculating each Lorentzian fuzzy score, the aggregate score is based at least in part on weighted summing of the plurality of Lorentzian fuzzy scores.
9 . The method of claim 8 further comprising:
prior to ranking, receiving an indication of weight for at least one parameter.
10 . The method of claim 1 wherein at least one indication is quantitative.
11 . The method of claim 1 wherein at least one indication is qualitative.
12 . The method of claim 11 wherein each qualitative indication is mapped to a quantitative indication.
13 . A method for ranking a plurality of elements, wherein a numerical parameter characterizes each element, the method comprising:
prior to ranking receiving an indication of at least one of fuzziness and bias as inputs to ranking; ranking a plurality of elements based at least in part on a Lorentzian fuzzy score based at least in part on a numerical parameter characterizing each element, wherein the formula used to calculate each Lorentzian fuzzy score is determined at least in part based on the polarity of the bias input.
14 . The method of claim 13 , wherein at least one of the numerical parameters is a target range having a minimum numeric value and a maximum numeric value.
15 . The method of claim 14 , wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
1
+
(
x
-
x
min
a
1
)
2
x
<
x
min
1
+
β
*
(
x
-
x
min
)
x
max
-
x
min
x
min
≤
x
≤
x
max
1
+
β
1
+
(
x
-
x
max
a
2
)
2
x
max
<
x
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
16 . The method of claim 14 , wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
-
β
1
+
(
x
-
x
min
a
1
)
2
x
<
x
min
1
+
β
*
(
x
-
x
max
)
x
max
-
x
min
x
min
≤
x
≤
x
max
1
1
+
(
x
-
x
max
a
2
)
2
x
max
<
x
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
17 . The method of claim 13 , wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
1
+
(
x
-
x
_
a
1
)
2
_
x
<
x
_
1
x
=
x
_
1
+
β
1
+
(
x
-
x
_
a
2
)
2
x
>
x
_
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
18 . The method of claim 13 , wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:
S
(
x
)
=
1
-
β
1
+
(
x
-
x
_
a
1
)
2
_
x
<
x
_
1
x
=
x
_
1
1
+
(
x
-
x
_
a
2
)
2
x
>
x
_
where x represents any data values, i.e., x ε {x 1 , . . . , x k }.
19 . The method of claim 13 , wherein at least one indication is quantitative.
20 . The method of claim 13 , wherein at least one indication is qualitative.
21 . The method of claim 20 , wherein each qualitative indication is mapped to a quantitative indication.
22 . A method for ranking a plurality of elements, wherein a numerical parameter characterizes each element, the method comprising:
prior to ranking receiving an indication of at least one of fuzziness and bias, and a target range having a minimum numeric value and a maximum numeric value, as inputs to ranking; ranking a plurality of elements based at least in part on a Lorentzian fuzzy score based at least in part on a numerical parameter characterizing each element, wherein the formula used to calculate each Lorentzian fuzzy score is determined at least in part based on the polarity of the bias input and wherein when the fuzziness indication is set or preset to zero and the polarity of the bias input is positive, the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula: S ( x ) = 0 x < x min 1 + β * ( x - x max ) x max - x min x min ≤ x ≤ x max 0 x max < x where x represents any data values, i.e., x ε {x 1 , . . . , x k }, wherein when the fuzziness indication is set or preset to zero and the polarity of the bias input is negative, the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula: S ( x ) = 0 x < x min 1 + β * ( x - x min ) x max - x min x min ≤ x ≤ x max 0 x max < x where x represents any data values, i.e., X ε {x 1 , . . . , x k }.Join the waitlist — get patent alerts
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