PATENT CLAIM ANALYSIS

Application Number: 16019021
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2019-11
Patent Classification: ["704", "009000"]

Abstract:
A method performed by a device may include identifying a plurality of samples of textual content; performing tokenization of the plurality of samples to generate a respective plurality of tokenized samples; performing embedding of the plurality of tokenized samples to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network; determining context relationships between the groupings of attributes using a bidirectional long short term memory (LSTM) technique; selecting predicted labels for the plurality of samples using a model, wherein the model selects, for a particular sample of the plurality of samples, a predicted label of the predicted labels from a plurality of labels based on respective scores of the particular sample with regard to the plurality of labels and based on a nonparametric paired comparison of the respective scores; and providing information identifying the predicted labels.

Claim (Index 1):
A method performed by a device, comprising:\n identifying a plurality of samples of textual content; performing tokenization of the plurality of samples of textual content to generate a respective plurality of tokenized samples using a domain-specific corpus,\n the domain-specific corpus being based on a vocabulary set that is specific to a domain associated with the plurality of samples,\n the vocabulary set including company-specific terms or jargon, \n \n the domain-specific corpus including information indicating relatedness of a first product and a second product,\n the first product and second product having different names, \n \n the tokenization including converting the plurality of samples to a numerical sequence associated with the respective plurality of tokenized samples, and \n the domain-specific corpus to identify a mapping of one or more terms from the plurality of samples to the respective plurality of tokenized samples; \n performing embedding of the respective plurality of tokenized samples using the domain-specific corpus to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network,\n the groupings of attributes of the sample matrix being passed to a bidirectional long short-term memory (LSTM) layer; \n determining context relationships between the groupings of attributes using a bidirectional LSTM technique associated with the bidirectional LSTM layer; selecting a predicted label of a plurality of predicted labels for the plurality of samples using a model,\n wherein the predicted label is selected based on the context relationships, the groupings of attributes, and/or the plurality of samples, \n wherein the model selects, for a particular sample of the plurality of samples, one of the plurality of predicted labels based on respective scores of the particular sample with regard to the plurality of predicted labels and based on a nonparametric paired comparison of the respective scores; \n providing information identifying the predicted label to a user for classification; receiving feedback associated with the particular sample from the user,\n the feedback including one or more of:\n information indicating whether the predicted label associated with the particular sample is correct, or \n information indicating a selected label when no significant label is identified for the particular sample; and \n \n updating the model based on the feedback from the user.

Metadata:
- Claim Count in Document: 56.0
- Percentile: 94.0
- Lexical Diversity: 2.31343
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13971092', '15994278', '15886873', '15670886', '14720113']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2493766274908366
- 35 USC 102 Novelty (BERT): 0.4870026279391726
- Combined Prediction Score: 0.2731392275356702
- Mean Citation Score: 168.27526399999996
- Max Citation Score: 186.96086
- Similarity Product: 124.65476302339076

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