PATENT CLAIM ANALYSIS

Application Number: 15952833
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2019-05
Patent Classification: ["706", "012000"]

Abstract:
Machine-learning models and behavior can be visualized. For example, a machine-learning model can be taught using a teaching dataset. A test input can then be provided to the machine-learning model to determine a baseline confidence-score of the machine-learning model. Next, weights for elements in the teaching dataset can be determined. An analysis dataset can be generated that includes a subset of the elements that have corresponding weights above a predefined threshold. For each overlapping element in both the analysis dataset and the test input, (i) a modified version of the test input can be generated that excludes the overlapping element, and (ii) the modified version of the test input can be provided to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score. A graphical user interface can be generated that visually depicts the test input and various elements' effects on the baseline confidence-score.

Claim (Index 1):
A method for visualizing machine-learning model behavior, the method comprising:\n teaching, by a processing device, a machine-learning model using a teaching dataset that has pieces of input data correlated to output categories, wherein each piece of input data is correlated to a particular output category and is formed from multiple elements; determining, by the processing device, a plurality of weights for a plurality of elements in the teaching dataset, wherein each weight in the plurality of weights corresponds to a respective element in the plurality of elements and is determined based on a relationship between (i) a first value indicating a number of pieces of input data in the teaching dataset that both include the respective element and belong to a specific output category, and (ii) a second value indicating a proportion of a total number of pieces of input data in the teaching data set that have the respective element; generating, by the processing device, an analysis dataset that includes a subset of the plurality of elements that have corresponding weights that are above a predefined threshold; providing, by the processing device, a test input to the machine-learning model to determine a baseline confidence-score of the machine-learning model, the test input having one or more elements in the analysis dataset; for each overlapping element in both the analysis dataset and the test input:\n generating, by the processing device, a modified version of the test input that excludes the overlapping element; \n providing, by the processing device, the modified version of the test input to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score; and \n adding, by the processing device, the overlapping element to (i) a first dataset in response to the effect of the overlapping element on the baseline confidence-score being positive, or (ii) a second dataset in response to the effect of the overlapping element on the baseline confidence-score being negative; and \n generating, by the processing device, a graphical user interface that visually depicts:\n the test input; \n the first dataset as having a positive effect on the baseline confidence-score; and \n the second dataset as having a negative effect on the baseline confidence-score.

Metadata:
- Claim Count in Document: 62.0
- Percentile: 91.0
- Lexical Diversity: 2.42647
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15927610', '15927654', '14834365', '15085038', '15164909']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3874846477872004
- 35 USC 102 Novelty (BERT): 0.4945972421245567
- Combined Prediction Score: 0.3981959072209361
- Mean Citation Score: 190.753098
- Max Citation Score: 211.03601
- Similarity Product: 135.4580657053125

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

Dataset: test