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 11):
A text dialogue system comprising:\n a conversational agent comprising at least a processor and a memory to receive one or more user messages from a client device over a network and send agent messages in response to the one or more user messages; a dialogue database comprising historical data representing a plurality of text dialogues, each text dialogue comprising a sequence of exchanged user and agent messages, each message comprising text data; a template database comprising response templates for use by the conversational agent to generate agent messages; a predictive model that takes as input data derived from text data from a text dialogue and outputs an array of probabilities, a probability in the array of probabilities being associated with a response template from the template database; a clustering engine comprising at least a processor and a memory to group agent messages within the dialogue database into a set of clusters, wherein grouping the agent messages into the set of clusters 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; and \n filtering clusters from the first set of clusters based on the group similarity metric values to generate a second set of clusters, 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, the text dialogue system further comprising: \n a response template generator comprising at least a processor and a memory to access data indicative of a set of clusters from the clustering engine and generate response templates for respective clusters in the set of clusters based on the text data of agent messages within each cluster, wherein generating response templates comprises generating response templates for respective clusters in the second set of clusters; a training data generator comprising at least a processor and a memory configured to:\n selectively assign response templates from the response template generator to agent messages in the dialogue database based on a similarity metric; and \n for an agent message with an assigned response template, pair text data for messages prior to the given agent message within the dialogue database with data indicating the assigned response, \n wherein the training data generator is configured to output a plurality of data pairs as training data for use in training the predictive model.

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): 0.2545496963527631
- 35 USC 102 Novelty (BERT): 0.5643577595645851
- Combined Prediction Score: 0.2855305026739453
- Mean Citation Score: 252.664168
- Max Citation Score: 449.48755
- Similarity Product: 401.7056854866386

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