PATENT CLAIM ANALYSIS

Application Number: 15888385
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-06
Patent Classification: ["704", "009000"]

Abstract:
Topics are determined for short text messages using an unsupervised topic model. In a training corpus created from a number of short text messages, a vocabulary of words is identified, and for each word a distributed vector representation is obtained by processing windows of the corpus having a fixed length. The corpus is modeled as a Gaussian mixture model in which Gaussian components represent topics. To determine a topic of a sample short text message, a posterior distribution over the corpus topics is obtained using the Gaussian mixture model.

Claim (Index 18):
The system of  claim 13  wherein the estimating the plurality of Gaussian components further comprises estimating means, covariances and mixture weights for each Gaussian component using an expectation-maximization algorithm.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 88.0
- Lexical Diversity: 1.68421
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14519427', '15401446', '13433111', '12907219', '13364535']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3043438504331153
- 35 USC 102 Novelty (BERT): 0.6049998925710054
- Combined Prediction Score: 0.3344094546469043
- Mean Citation Score: 355.57666200000006
- Max Citation Score: 533.1731
- Similarity Product: 491.68929018220894

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