Feature Type Spectrum Technique
Objects in a sample set, and/or data representing those objects, are analyzed to determine whether the objects have features in a feature set. Each object may be analyzed more than once to produce multiple determinations of whether the object has features in the feature set. The frequencies with which features in the feature set are observed in the objects in the object set may be used to produce output representing the frequencies of observation. An example of such output is a bar chart representing the frequency of observation of features in the feature set in a particular object. The feature output may be used to identify one or more obscure (i.e., low frequency) features in the particular object. Various operations performed by the system may be performed by computers, humans, or a combination thereof.
1 . A method performed by at least one computer processor executing computer program instructions stored on a non-transitory computer-readable medium, the method comprising:
(A) generating, for each feature F in a plurality of features, a plurality of frequencies of observation of feature F in an object O; and
(B) generating output representing the plurality of frequencies of observation of feature F in object O.
2 . The method of claim 1 , wherein (A) comprises:
(A) (1) generating, for each feature F in the plurality of features, a first indication of whether object O was observed to have feature F, thereby generating a first plurality of indications for object O;
(A) (2) generating, for each feature F in the plurality of features, a second indication of whether object O was observed to have feature F; thereby generating a second plurality of indications for object O; and
(A) (3) generating the plurality of frequencies of observation of feature F in object O based on the first and second pluralities of indications for object O.
3 . The method of claim 1 , wherein the output representing the plurality of frequencies of observation of feature F in object O comprises a chart representing the plurality of frequencies of observation of feature F in object O.
4 . The method of claim 3 , wherein the chart comprises a bar chart.
5 . The method of claim 3 , wherein the chart comprises a pie chart.
6 . The method of claim 1 , further comprising:
(C) identifying, based on the plurality of frequencies of observation of feature F in object O, a first subset of the plurality of features having frequencies satisfying a low frequency criterion.
7 . The method of claim 6 , wherein the low frequency criterion comprises a maximum value, and wherein the first subset comprises features in the plurality of features having frequencies less than the maximum value.
8 . The method of claim 6 , further comprising:
(D) identifying, based on the plurality of frequencies of observation of feature F in object O, a second subset of the plurality of features having frequencies satisfying a high frequency criterion.
9 . The method of claim 8 , wherein the high frequency criterion comprises a minimum value, and wherein the second subset comprises features in the plurality of features having frequencies greater than the minimum value.
10 . The method of claim 6 , further comprising:
(D) generating output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion.
11 . The method of claim 10 , wherein the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion includes output representing the frequencies satisfying the low frequency criterion.
12 . The method of claim 10 , where the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion comprises a chart.
13 . The method of claim 12 , wherein the chart comprises a bar chart.
14 . The method of claim 12 , wherein the chart comprises a pie chart.
15 . The method of claim 10 , wherein the output representing the plurality of frequencies of observation of feature F in object O includes the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion.
16 . The method of claim 15 , wherein the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion comprises output emphasizing the first subset of the plurality of features.
17 . A non-transitory computer-readable medium comprising computer program instructions executable by at least one computer processor to perform a method, the method comprising:
(A) generating, for each feature F in a plurality of features, a plurality of frequencies of observation of feature F in an object O; and
(B) generating output representing the plurality of frequencies of observation of feature F in object O.
18 . The non-transitory computer-readable medium of claim 17 , wherein the output representing the plurality of frequencies of observation of feature F in object O comprises a chart representing the plurality of frequencies of observation of feature F in object O.
19 . The non-transitory computer-readable medium of claim 18 , wherein the chart comprises a bar chart.
20 . The non-transitory computer-readable medium of claim 17 , wherein the method further comprises:
(C) identifying, based on the plurality of frequencies of observation of feature F in object O, a first subset of the plurality of features having frequencies satisfying a low frequency criterion.
21 . The non-transitory computer-readable medium of claim 20 , wherein the low frequency criterion comprises a maximum value, and wherein the first subset comprises features in the plurality of features having frequencies less than the maximum value.
22 . The non-transitory computer-readable medium of claim 20 , wherein the method further comprises:
(D) identifying, based on the plurality of frequencies of observation of feature F in object O, a second subset of the plurality of features having frequencies satisfying a high frequency criterion.
23 . The non-transitory computer-readable medium of claim 22 , wherein the high frequency criterion comprises a minimum value, and wherein the second subset comprises features in the plurality of features having frequencies greater than the minimum value.
24 . The non-transitory computer-readable medium of claim 6 , wherein the method further comprises:
(D) generating output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion.
25 . The non-transitory computer-readable medium of claim 24 , wherein the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion includes output representing the frequencies satisfying the low frequency criterion.
26 . The non-transitory computer-readable medium of claim 24 , wherein the output representing the plurality of frequencies of observation of feature F in object O includes the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion.
27 . The non-transitory computer-readable medium of claim 26 , wherein the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion comprises output emphasizing the first subset of the plurality of features.
28 . A method performed by at least one computer processor executing computer program instructions stored on a non-transitory computer-readable medium, the method comprising:
(A) generating, for each feature F in a plurality of features, a plurality of frequencies of observation of feature F in an object O;
(B) identifying, based on the plurality of frequencies of observation of feature F in object O, a first subset of the plurality of features having frequencies satisfying a low frequency criterion;
(C) generating output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion.
29 . The method of claim 28 , wherein the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion includes output representing the frequencies satisfying the low frequency criterion.
30 . The method of claim 28 , where the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion comprises a chart.
31 . The method of claim 30 , wherein the chart comprises a bar chart.
32 . A non-transitory computer-readable medium comprising computer program instructions executable by at least one computer processor to perform a method, the method comprising:
(A) generating, for each feature F in a plurality of features, a plurality of frequencies of observation of feature F in an object O;
(B) identifying, based on the plurality of frequencies of observation of feature F in object O, a first subset of the plurality of features having frequencies satisfying a low frequency criterion;
(C) generating output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion.
33 . The non-transitory computer-readable medium of claim 32 , wherein the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion includes output representing the frequencies satisfying the low frequency criterion.
34 . The non-transitory computer-readable medium of claim 32 , where the output representing the first subset of the plurality of features having frequencies satisfying the low frequency criterion comprises a chart.
35 . The non-transitory computer-readable medium of claim 34 , wherein the chart comprises a bar chart.