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 15):
A non-transitory, computer-readable medium comprising computer program instructions that, when executed by a processor, cause the processor to:\n obtain 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; cluster agent messages within the historical data, wherein clustering agent messages comprises:\n converting agent messages into numeric arrays; \n clustering the numeric arrays into a first set of clusters; \n computing values for a group similarity metric for respective clusters, the group similarity metric representing a similarity of agent messages in a cluster, wherein the group similarity metric comprises a mean string similarity of unique unordered pairs of agent messages in each cluster, each agent message being represented as string data; and \n filtering clusters from the first set of clusters based on the group similarity metric values to generate a second set of clusters; \n generate 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, wherein generating response templates comprises generating response templates for respective clusters in the second set of clusters; assign response templates to agent messages in the plurality of text dialogues based on a similarity metric; filter at least agent messages from the first set of text dialogues based on the similarity metric values determined during the assigning to generate a second set of text dialogues; and group 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, instructions to cause the processor to generate data for use as a training example by pairing text data for messages in a text dialogue prior to the given agent message with data indicating a response template assigned to the given agent message.

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): 0.2591263370194603
- 35 USC 102 Novelty (BERT): 0.5522310875657964
- Combined Prediction Score: 0.288436812074094
- Mean Citation Score: 252.664168
- Max Citation Score: 449.48755
- Similarity Product: 310.0133348206043

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