PATENT CLAIM ANALYSIS

Application Number: 16404789
Application Type: Utility
Filing Date: 2019-05
Publication Date: 2019-10
Patent Classification: ["706", "012000"]

Abstract:
A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix used to select a label for each observation. Unique combinations of observations selected from the unlabeled observations are defined. A marginal distribution value is computed from the label distribution probability matrix. A joint distribution value is computed between observations included in each combination. A mutual information value is computed for each combination as a combination of the marginal distribution value and the joint distribution value computed for the respective combination. A predefined number of observation vector combinations is selected from the combinations that have highest values for the computed mutual information value. Labeled observation vectors are updated to include each observation vector included in the selected observation vector combinations with a respective obtained label.

Claim (Index 11):
The non-transitory computer-readable medium of  claim 1 , wherein the mutual information value is computed using M(x i ,x j ,x k )=H(x i )+H(x j )+H(x k )\u2212[H(x i ,x k )+H(x i ,x j )\u2212H(x i ,x j ,x k )] for the group size of each combination equal to two and to three, where H(x i ) is the marginal distribution value computed for an i th  observation vector of the plurality of unlabeled observation vectors, H(x j ) is the marginal distribution value computed for a j th  observation vector of the plurality of unlabeled observation vectors, H(x k ) is the marginal distribution value computed for a k th  observation vector of the plurality of unlabeled observation vectors, H(x i ,x 1 ) is the joint distribution value computed for the respective combination of the i th  observation vector and the j th  observation vector, H(x i ,x k ) is the joint distribution value computed for the respective combination of the i th  observation vector and the k th  observation vector, and H(x i ,x j ,x k ) is the joint distribution value computed for the respective combination of the i th  observation vector, the j th  observation vector, and the k th  observation vector.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 100.0
- Lexical Diversity: 2.3871
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16400157', '16162794', '16108293', '15686863', '15335530']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4752051845657194
- 35 USC 102 Novelty (BERT): 0.5416608608039399
- Combined Prediction Score: 0.4818507521895414
- Mean Citation Score: 366.952896
- Max Citation Score: 414.58194
- Similarity Product: 284.9749945342219

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

Dataset: test