PATENT CLAIM ANALYSIS

Application Number: 15984224
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2019-09
Patent Classification: ["358", "001900"]

Abstract:
The present application relates to a method performed at an electronic device for parsing tables in a PDF document. The method includes the following steps: receiving the PDF document containing a table area; extracting horizontal lines, vertical lines and text blocks in the table area; determining the types of tables in the table area according to the extracted horizontal lines and vertical lines; if the table is a quasi full-line table, determining the structure of the quasi full-line table in the table area according to the horizontal lines and the vertical lines in the table area with the assistance of the text blocks in the table area; and if the table is a quasi non-line table, determining the structure of the quasi non-line table in the table area according to the text blocks in the table area with the assistance of the horizontal lines and/or the vertical lines in the table area.

Claim (Index 8):
The method according to  claim 1 , wherein the step of determining the structure of the quasi non-line table in the table area comprises:\n clustering the text blocks belonging to the same row in the table area into a text row; for each text row, determining the number of individual text blocks in the text row and the number of columns of the merged text blocks in the text row according to the text blocks in the text row and the horizontal lines and/or the vertical lines in the table area, taking the sum of the two numbers as the number of columns in the text row, and retaining the text row with the largest number of columns; if there are a plurality of text rows with the largest number of columns, sorting the text rows according to the y coordinates in an order from small to large, performing horizontal projection clustering on the text blocks in the adjacent text rows in sequence, taking the minimum value on the horizontal direction as the left frame of the column interval, taking the maximum value as the right frame of the column interval, taking the upper and lower frames of the table area respectively as the upper and lower frames of the column interval, and determining the clustered column interval; and if there is only one text row with the largest number of columns, directly determining the column interval by using the coordinates of the text blocks in the text row; for each text row, performing vertical projection on the text blocks in the text row, taking the number of projection areas as the number of rows of the text row, and retaining the text row with the largest number of rows; if there are a plurality of text rows with the largest number of rows, sorting the text rows according to the x coordinates in an order from small to large, sequentially performing vertical projection clustering on the text blocks in adjacent text rows, taking the minimum value on the vertical direction as the upper frame of a row interval, taking the maximum value as the lower frame of the row interval, taking the left and right frames of the table area respectively as the left and right frames of the row interval, and determining the clustered row interval; and if there is only one text row with the largest number of rows, directly perform vertical projection on the text row to determine the row interval; and determining the row-and-column index information and the row-and-column merging information of the cells according to the determined row intervals and column intervals so as to obtain the structure of the quasi non-line table.

Metadata:
- Claim Count in Document: 26.0
- Percentile: 93.0
- Lexical Diversity: 3.17647
- Patent Class: 358.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['13871862', '12414365', '14778155', '15175712', '15136674']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5205166107586174
- 35 USC 102 Novelty (BERT): 0.4958158642987255
- Combined Prediction Score: 0.5180465361126283
- Mean Citation Score: 179.56158
- Max Citation Score: 200.38649
- Similarity Product: 135.0843991037637

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

Dataset: test