PATENT CLAIM ANALYSIS

Application Number: 15907230
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-10
Patent Classification: ["715", "255000"]

Abstract:
Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.

Claim (Index 11):
The method of  claim 1 , wherein the reporting comprises a text report describing which variable combinations have a largest estimated impact.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 88.0
- Lexical Diversity: 1.61702
- Patent Class: 715.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14672019', '14672026', '14672027', '14672031', '14672017']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4610799839068039
- 35 USC 102 Novelty (BERT): 0.5512153277624574
- Combined Prediction Score: 0.4700935182923693
- Mean Citation Score: 437.98548
- Max Citation Score: 454.83963
- Similarity Product: 329.3709532215678

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test