PATENT CLAIM ANALYSIS

Application Number: 15920723
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-07
Patent Classification: ["709", "206000"]

Abstract:
Large batches of social media communications may be automatically annotated. This provides techniques to create large labeled datasets without the assistance of human labelers. For instance, social media communications may be fetched and annotated as actionable or noise for a given account (e.g., a brand handle on Twitter®) without human review. Social media communications from users who are attempting to engage with a brand or entity on a social media platform may be annotated as actionable, whereas other communications may be labeled as noise.

Claim (Index 13):
The computer program of  claim 12 , wherein for all users to which the brand has previously responded, the program is further configured to cause the at least one processor to:\n run a one-class classifier (OCC) built from the collection of actionable social media communications on noise social media communications; for each social media communication in a noise collection, check whether the social media communication is predicted as an outlier by the OCC; when the social media communication is not predicted as an outlier by the OCC, drop the social media communication; and for each social media communication that is predicted as an outlier by the OCC, add the social media communication to the collection of noise social media communications.

Metadata:
- Claim Count in Document: 16.0
- Percentile: 90.0
- Lexical Diversity: 1.63158
- Patent Class: 709.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['13913173', '13594842', '14020674', '14040565', '13587928']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.237153663452259
- 35 USC 102 Novelty (BERT): 0.4926425033735732
- Combined Prediction Score: 0.2627025474443904
- Mean Citation Score: 160.893904
- Max Citation Score: 169.76009
- Similarity Product: 101.93817971111176

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