PATENT CLAIM ANALYSIS

Application Number: 15885807
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-08
Patent Classification: ["704", "009000"]

Abstract:
A personality model is created for a population and used as an input to a text generation system. Alternative texts are created based upon the emotional effect of the generated text. Certain words or phrases are “pinned” in the output, reducing the variability of the generated text so as to preserve required information content, and a number of tests provide input to a discriminator network so that proposed outputs both match an outside objective regarding the information content, emotional affect, and grammatical acceptability. A feedback loop provides new “ground truth” data points for refining the personality model and associated generated text.

Claim (Index 8):
A method for maintaining specific semantic content in a generated natural language text, the method comprising:\n identifying words or phrases essential to the semantic content of the source text; performing an evaluation of the source text, wherein the evaluation includes a summarization procedure, followed by a first measurement of the relative order and precedence of the essential words and phrases in the source summarization result; encoding the source text in an a neural sequence-to-sequence encoder to generate the code; sampling from the distribution implied by the code to create a candidate generated text; performing an evaluation of the generated text, wherein the evaluation of the generated text includes a summarization procedure, followed by a second measurement of the relative order and precedence of the essential words and phrases in the generated summarization result; computing a first difference between the first measurement and the second measurement; if the first difference is greater than an essential similarity threshold, providing a negative feedback response to the encoder and decoder; and if the first difference is less than the essential similarity threshold, providing a positive feedback response to the encoder and decoder; and updating the internal weights associated with the encoder and decoder in response to the feedback.

Metadata:
- Claim Count in Document: 31.0
- Percentile: 86.0
- Lexical Diversity: 1.59155
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15815686', '15407713', '15147222', '15817161', '15408526']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2107653570543351
- 35 USC 102 Novelty (BERT): 0.4883107987870749
- Combined Prediction Score: 0.2385199012276091
- Mean Citation Score: 162.37153600000005
- Max Citation Score: 193.36888
- Similarity Product: 139.37204446564672

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