PATENT CLAIM ANALYSIS

Application Number: 15893273
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-08
Patent Classification: ["704", "009000"]

Abstract:
Certain examples described herein provide methods and systems for implementing a conversational agent, e.g. to train a predictive model used by the conversational agent. In examples, text data representing agent messages from a dialogue database are clustered and the clusters are used to generate response templates for use by the conversational agent. The predictive model is trained on training data generated by selectively assigning response templates to agent messages from text dialogues. Examples enable a predictive model to be trained on high quality data sets that are generated automatically from a corpus of historical data. In turn, they enable a natural language interface to be efficiently provided.

Claim (Index 7):
The method of  claim 6 , wherein filtering at least agent messages from the first set of text dialogues comprises:\n comparing a number of assigned out-of-dataset response templates to a predefined threshold value; and removing the text dialogue from the first set of text dialogues responsive to the number of assigned out-of-dataset response templates being above the predefined threshold value, wherein remaining text dialogues form the second set of text dialogues.

Metadata:
- Claim Count in Document: 10.0
- Percentile: 88.0
- Lexical Diversity: 1.78462
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15816282', '14617305', '15840295', '15823271', '15587183']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2853095832158294
- 35 USC 102 Novelty (BERT): 0.5477260800467633
- Combined Prediction Score: 0.3115512328989228
- Mean Citation Score: 252.664168
- Max Citation Score: 449.48755
- Similarity Product: 258.9372622166186

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

Dataset: test