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 15):
A non-transitory computer-readable medium storing one or more instructions, the one or more instructions comprising:\n one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:\n identify a plurality of samples of textual content; \n process the plurality of samples of textual content to generate a respective plurality of tokenized samples of numerical values corresponding to segments of the plurality of samples,\n wherein the processing is performed 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 wherein the vocabulary set that is specific to the domain includes information indicating relatedness of a first product and a second product,\n the first product and second product having different names; \n \n \n determine groupings of attributes of the respective plurality of tokenized samples using a convolutional neural network,\n the groupings of attributes of the respective plurality of tokenized samples being passed to a bidirectional long short-term memory (LSTM) layer; \n \n determine context relationships between the groupings of attributes using a bidirectional LSTM technique; \n select 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 or a 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 \n provide information identifying the predicted label to a user; \n receive 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 \n update 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): 0.2477006307230913
- 35 USC 102 Novelty (BERT): 0.4957574416014348
- Combined Prediction Score: 0.2725063118109256
- Mean Citation Score: 168.27526399999996
- Max Citation Score: 186.96086
- Similarity Product: 113.25964698745966

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