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 23):
A method of predicting occurrence of an event or classifying an object using semi-supervised data to label unlabeled data in a dataset, the method comprising:\n reading, by a computing device, a plurality of labeled observation vectors from a dataset; reading, by the computing device, a plurality of unlabeled observation vectors from the dataset; defining, by the computing device, a plurality of combinations of observation vectors selected from the plurality of unlabeled observation vectors, wherein each combination of the plurality of combinations of observation vectors is unique from the others, wherein a group size of each combination is greater than one and less than or equal to a predefined number of observations to include in a combination; (A) training, by the computing device, a labeling model using the read plurality of labeled observation vectors; (B) executing, by the computing device, the trained labeling model with each observation vector of the plurality of unlabeled observation vectors to define a label distribution probability matrix, wherein the label distribution probability matrix defines a label probability for each label of a label set for each observation vector of the plurality of unlabeled observation vectors; (C) selecting, by the computing device, a label for each observation vector of the plurality of unlabeled observation vectors as the label associated with a maximum label probability value in the label distribution probability matrix for a respective observation vector of the plurality of unlabeled observation vectors; (D) computing, by the computing device, a marginal distribution value from the label distribution probability matrix for each respective observation vector of the plurality of unlabeled observation vectors; (E) computing, by the computing device, a joint distribution value between observation vectors included in each combination of the plurality of combinations of observation vectors using a joint distribution function; (F) computing, by the computing device, a mutual information value for each combination of the plurality of combinations of observation vectors, wherein the mutual information value is computed as a combination of the marginal distribution value computed for each observation vector of a respective combination and the joint distribution value computed for the respective combination; (G) selecting, by the computing device, a predefined number of observation vector combinations from the plurality of combinations of observation vectors, wherein the predefined number of observation vector combinations selected have highest values for the computed mutual information value; (H) obtaining, by the computing device, a label for each observation vector included in the selected observation vector combinations; (I) updating, by the computing device, the plurality of labeled observation vectors to include each observation vector included in the selected observation vector combinations with a respective obtained label for each respective observation vector; (J) removing, by the computing device, each observation vector included in the selected observation vector combinations from the plurality of unlabeled observation vectors; (K) repeating, by the computing device, operations (A) to (J) a predefined number of times; and after the predefined number of times, outputting, by the computing device, the label selected for each observation vector of the plurality of unlabeled observation vectors to a second dataset.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 100.0
- Lexical Diversity: 2.3871
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16400157', '16162794', '16108293', '15686863', '15335530']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4428183229282678
- 35 USC 102 Novelty (BERT): 0.5440110344144862
- Combined Prediction Score: 0.4529375940768896
- Mean Citation Score: 366.952896
- Max Citation Score: 414.58194
- Similarity Product: 343.321270502851

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