User interface for predictive model generation
First Claim
1. A method performed by at least one computer processor, the method comprising:
- (A) searching a first dataset for elements matching inclusion set criteria to identify an inclusion set, wherein the inclusion set comprises a first subset of the first dataset;
(B) searching the dataset for elements matching exclusion set criteria to identify an exclusion set, wherein the exclusion set comprises a second subset of the first dataset;
(C) identifying a set of unique content elements selected from the inclusion set and the exclusion set;
(D) sorting the set of unique content elements to produce a sorted set of unique content elements;
(E) filtering, from the sorted set of unique content elements, all but the first N elements in the sorted set of unique content elements to produce a filtered set of unique content elements;
(F) excluding at least one content element from the filtered set of unique content elements to produce a final set of unique content elements; and
(G) producing a predictive model based on the final set of unique content elements;
wherein (D) comprises, for each of the unique content elements E;
(D)(1) identifying a percentage IP of records in the inclusion set containing element E;
(D)(2) identifying a percentage EP of records in the exclusion set containing element E;
(D)(3) identifying an absolute value |IP−
EP| of a difference between IP and EP; and
(D)(4) sorting the set of unique content elements in descending order by the absolute value |IP−
EP| of the unique content elements in the set of unique content elements to produce the sorted set of unique content elements.
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Accused Products
Abstract
A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.
17 Citations
28 Claims
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1. A method performed by at least one computer processor, the method comprising:
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(A) searching a first dataset for elements matching inclusion set criteria to identify an inclusion set, wherein the inclusion set comprises a first subset of the first dataset; (B) searching the dataset for elements matching exclusion set criteria to identify an exclusion set, wherein the exclusion set comprises a second subset of the first dataset; (C) identifying a set of unique content elements selected from the inclusion set and the exclusion set; (D) sorting the set of unique content elements to produce a sorted set of unique content elements; (E) filtering, from the sorted set of unique content elements, all but the first N elements in the sorted set of unique content elements to produce a filtered set of unique content elements; (F) excluding at least one content element from the filtered set of unique content elements to produce a final set of unique content elements; and (G) producing a predictive model based on the final set of unique content elements; wherein (D) comprises, for each of the unique content elements E; (D)(1) identifying a percentage IP of records in the inclusion set containing element E; (D)(2) identifying a percentage EP of records in the exclusion set containing element E; (D)(3) identifying an absolute value |IP−
EP| of a difference between IP and EP; and(D)(4) sorting the set of unique content elements in descending order by the absolute value |IP−
EP| of the unique content elements in the set of unique content elements to produce the sorted set of unique content elements. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable medium comprising computer program instructions which are executable by at least one computer processor to perform a method, the method comprising:
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(A) searching a first dataset for elements matching inclusion set criteria to identify an inclusion set, wherein the inclusion set comprises a first subset of the first dataset; (B) searching the dataset for elements matching exclusion set criteria to identify an exclusion set, wherein the exclusion set comprises a second subset of the first dataset; (C) identifying a set of unique content elements selected from the inclusion set and the exclusion set; (D) sorting the set of unique content elements to produce a sorted set of unique content elements; (E) filtering, from the sorted set of unique content elements, all but the first N elements in the sorted set of unique content elements to produce a filtered set of unique content elements; (F) excluding at least one content element from the filtered set of unique content elements to produce a final set of unique content elements; and (G) producing a predictive model based on the final set of unique content elements; wherein (D) comprises, for each of the unique content elements E; (D)(1) identifying a percentage IP of records in the inclusion set containing element E; (D)(2) identifying a percentage EP of records in the exclusion set containing element E; (D)(3) identifying an absolute value |IP−
EP| of a difference between IP and EP; and(D)(4) sorting the set of unique content elements in descending order by the absolute value |IP−
EP| of the unique content elements in the set of unique content elements to produce the sorted set of unique content elements. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
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Specification