PATENT CLAIM ANALYSIS

Application Number: 15891607
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-08
Patent Classification: ["702", "019000"]

Abstract:
A visualization system comprising a persistent memory, storing a dataset, and a non-persistent memory implements a pattern visualizing method. The dataset contains discrete attribute values for each first entity in a plurality of first entities for each second entity in a plurality of second entities. The dataset is compressed by blocked compression and represents discrete attribute values in both compressed sparse row and column formats. The discrete attribute values are clustered to assign each second entity to a cluster in a plurality of clusters. Differences in the discrete attribute values for the first entity across the second entities of a given cluster relative to the discrete attribute value for the same first entity across the other clusters are computed thereby deriving differential values. A heat map of these differential values for each first entity for each cluster is displayed to reveal the pattern in the dataset.

Claim (Index 26):
A method for visualizing a pattern in a discrete attribute value dataset, the method comprising:\n at a computer system comprising a persistent memory and a non-persistent memory: storing the discrete attribute value dataset in persistent memory, wherein\n the discrete attribute value dataset comprises a corresponding discrete attribute value for each first entity in a plurality of first entities for each respective second entity in a plurality of second entities, and \n the discrete attribute value dataset redundantly represents the corresponding discrete attribute value for each first entity in the plurality of first entities for each respective second entity in the plurality of second entities in both a compressed sparse row format and a compressed sparse column format in which first entities for a respective second entity that have a null discrete attribute data value are discarded, and \n the discrete attribute value dataset is compressed in accordance with a blocked compression algorithm; \n clustering the discrete attribute value dataset using the discrete attribute value for each first entity in the plurality of first entities, or principal components derived therefrom, for each respective second entity in the plurality of second entities thereby assigning each respective second entity in the plurality of second entities to a corresponding cluster in a plurality of clusters, wherein\n each respective cluster in the plurality of clusters consists of a unique different subset of the second plurality of entities, and \n the clustering loads less than the entirety of the discrete attribute value dataset into the non-persistent memory at any given time during the clustering; \n computing, for each respective first entity in the plurality of first entities for each respective cluster in the plurality of clusters, a difference in the discrete attribute value for the respective first entity across the respective subset of second entities in the respective cluster relative to the discrete attribute value for the respective first entity across the plurality of clusters other than the respective cluster, thereby deriving a differential value for each respective first entity in the plurality of first entities for each respective cluster in the plurality of clusters; and displaying in a first panel a heat map that comprises a representation of the differential value for each respective first entity in the plurality of first entities for each cluster in the plurality of clusters thereby visualizing the pattern in the discrete attribute value dataset.

Metadata:
- Claim Count in Document: 22.0
- Percentile: 88.0
- Lexical Diversity: 2.36923
- Patent Class: 702.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13716910', '12761315', '12868631', '13149132', '13363688']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1786113609938843
- 35 USC 102 Novelty (BERT): 0.5062688194017594
- Combined Prediction Score: 0.2113771068346718
- Mean Citation Score: 244.001136
- Max Citation Score: 265.97464
- Similarity Product: 195.0264337737132

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