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 10):
A computer-implemented method for generating training data for a conversational agent, the method comprising:\n obtaining historical data representing a first set of text dialogues, each text dialogue comprising a sequence of messages exchanged between a user and an agent, each message comprising text data; clustering agent messages within the historical data; generating response templates for respective clusters based on the text data of agent messages within each cluster, a response template comprising text data for use by the conversational agent to generate agent messages; assigning response templates to agent messages in the plurality of text dialogues based on a similarity metric; filtering at least agent messages from the first set of text dialogues based on values of the similarity metric determined during the assigning to generate a second set of text dialogues; grouping text data in text dialogues in the second set of text dialogues to generate training data for the conversational agent, including, for a given agent message in a text dialogue in the second set of text dialogues, generating data for use as a training example by pairing text data for messages in the text dialogue prior to the given agent message with data indicating a response template assigned to the given agent message; and training a predictive model using the training data, wherein the conversational agent is configured to apply the predictive model to messages within a text dialogue to predict a response template to use to respond to the messages, the method further comprising, at the conversational agent: receiving a set of messages from a user as part of a new text dialogue; applying the predictive model to text data from the set of messages; and responsive to an output of the predictive model indicating an out-of-dataset response template as having a largest probability value, requesting that a human operator take over the text dialogue.

Metadata:
- Claim Count in Document: 10.0
- Percentile: 88.0
- Lexical Diversity: 1.78462
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15816282', '14617305', '15840295', '15823271', '15587183']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2562828197794661
- 35 USC 102 Novelty (BERT): 0.5597570610231969
- Combined Prediction Score: 0.2866302439038392
- Mean Citation Score: 252.664168
- Max Citation Score: 449.48755
- Similarity Product: 366.8564751878202

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