PATENT CLAIM ANALYSIS

Application Number: 15900176
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-11
Patent Classification: ["704", "009000"]

Abstract:
An artificial intelligence based method and apparatus for generating information are disclosed. The method in an embodiment includes: segmenting a to-be-processed text into characters to obtain a character sequence; determining a character vector for each character in the character sequence to generate a character vector sequence; generating a plurality of character vector subsequences by segmenting the character vector sequence based on a preset vocabulary; for each generated character vector subsequence, determining a sum of character vectors composing the character vector subsequence as a target vector, and inputting the target vector into a pre-trained first neural network to obtain a word vector corresponding to the each character vector subsequence, the first neural network used to characterize a correspondence between the target vector and the word vector; and analyzing the to-be-processed text based on the obtained word vector to generate an analysis result. This embodiment improves the adaptability of text processing.

Claim (Index 11):
The apparatus for generating information according to  claim 7 , wherein the operations further comprise training the first neural network, and the training the first neural network comprises:\n extracting a preset first training sample, wherein the first training sample includes a plurality of first preset words and word vectors of the first preset words in the plurality of first preset words; determining, for each of the plurality of first preset words in the first training sample, character vectors for character composing the first preset word, to generate a character vector sequence corresponding to the first preset word; and training and obtaining the first neural network, by using a machine learning method, and by assigning the character vector sequence corresponding to the first preset word as an input, and the word vector of the first preset word in the first training sample as an output.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 88.0
- Lexical Diversity: 2.31884
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15900166', '15407713', '15408526', '15426727', '12707283']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2975838102264
- 35 USC 102 Novelty (BERT): 0.4995480723764576
- Combined Prediction Score: 0.3177802364414058
- Mean Citation Score: 241.890776
- Max Citation Score: 267.73505
- Similarity Product: 207.1239631182344

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test