PATENT CLAIM ANALYSIS

Application Number: 16059399
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2020-02
Patent Classification: ["null", "null"]

Abstract:
An example method described herein involves receiving a data input; identifying a plurality of topics in the data input; determining an underrepresented set of data for a first set of topics of the plurality of topics based on a plurality of knowledge graphs associated with the first set of topics; calculating a score for each topic of the first set of topics based on a representative learning technique; determining that the score for a first topic of the first set of topics satisfies a threshold score; selecting a topic specific knowledge graph based on the first topic; identifying representative objects that are similar to objects of the data input based on the topic specific knowledge graph; generating representation data that is similar to the data input based on the representative objects to balance the underrepresented set of data with a set of data associated with a second set of topics of the plurality of topics; and performing an action associated with the representation data.

Claim (Index 20):
A non-transitory computer-readable medium storing instructions, the instructions comprising:\n one or more instructions that, when executed by one or more processors, cause the one or more processors to:\n receive a data input; \n determine a represented set of data for a first set of topics of a plurality of topics of the data input based on a domain knowledge graph of the plurality of topics; \n determine an underrepresented set of data for a second set of topics of the plurality of topics based on a representative learning technique,\n wherein the underrepresented set of data is underrepresented relative to the represented set of data, and \n wherein the representative learning technique is machine learning; \n \n determine a distance between an identified topic of the data input and a first topic in the domain knowledge graph; \n calculate a score for the first topic based on the distance; \n determine that the score for Hall the first topic satisfies a threshold score; \n determine that the first topic of the plurality of topics is one of the second set of topics; \n select a topic specific knowledge graph based on the first topic being one of the second set of topics; \n identify representative objects that are similar to objects of the data input based on the topic specific knowledge graph; \n generate, based on the representative objects, representation data that is of similar data type to the data input and increases an amount of data associated with the underrepresented set of data,\n wherein the one or more instructions to generate the representation data cause the one or more processors to:\n substitute one of the representative objects with a corresponding object of the data input based on an edge distance of the one of the representative objects from the object of the data input in the topic specific knowledge graph; \n \n \n generate a representation knowledge graph based on the representation data,\n wherein the representation knowledge graph includes a new topic that is associated with the underrepresented set of data; and \n \n store the representation knowledge graph in a knowledge graph data structure,\n wherein the knowledge graph data structure stores the domain knowledge graph and the topic specific knowledge graph.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 96.0
- Lexical Diversity: 3.14545
- Patent Class: nan
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14928210', '15687114', '15404932', '15040972', '14793033']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5294699217519845
- 35 USC 102 Novelty (BERT): 0.5001045863337074
- Combined Prediction Score: 0.5265333882101568
- Mean Citation Score: 224.936594
- Max Citation Score: 234.10284
- Similarity Product: 151.52906246374368

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

Dataset: test