Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 28, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-14 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Semenov et. al. (US Patent 2022/0198182 A1).
Regarding claim 1, Semenov et. al. discloses a layout analysis system, comprising at least one processor configured to: detect a cell of each of a plurality of scales from in a document image showing a document including a plurality of components (Figure 2, 4A, 6, 8); acquire cell information relating to the cell of each of the plurality of scales (Figure 7, [0042] a heat map is generated to correspond with elements within the document being analyzed); and analyze a layout relating to the document based on the cell information on each of the plurality of scales (Figure 5, [0049]-[0050] an internal field format evaluation 500 is used in the system which uses Byte Pair Encoding (BPE) tokens that represent an input text of detected fields on the document images).
Regarding claim 2, Semenov et. al. discloses the layout analysis system according to claim 1, wherein at least one processor is configured to analyze the layout based on a learning model which has learned a for-training layout relating to a for-training document (Figures 2, 6-8).
Regarding claim 3, Semenov et. al. discloses the layout analysis system according to claim 2, wherein at least one processor is configured to analyze the layout by arranging the cell information on each of the plurality of scales under a predetermined condition, inputting the arranged cell information to the learning model, and acquiring a result of analysis of the layout by the learning model (Figure 2, learning stage 220, “cell information” corresponds with document information based on markup region in pixels, [0040] and regions can have a specific geometric shape, [0037] the word selection sub unit 330 of system 300 is a submodule that uses a heuristic algorithm to analyze document text).
Regarding claim 4, Semenov et. al. discloses the layout analysis system according to claim 3, wherein the learning model is a Vision Transformer-based model (Figure 5, [0049]-[0050] an internal field format evaluation 500 is used in the system which uses Byte Pair Encoding (BPE) tokens that represent an input text of detected fields on the document images).
Regarding claim 5, Semenov et. al. discloses the layout analysis system according to claim 3[[ or 4]], wherein at least one processor is configured to analyze the layout by inputting, to the learning model, input data obtained by arranging a plurality of pieces of cell information on a first scale under a predetermined condition and then arranging a plurality of pieces of cell information on a second scale under a predetermined condition ([0034], Figure 2 flow diagram of the method 200 for field detection in a document, learning stage 220 is the first step, and the second step is the input document field detection stage 230, and heat maps are generated for each reference element as shown in Figure 4A).
Regarding claim 6, Semenov et. al. discloses the layout analysis system according to claim 3[[ or 4]], wherein the at least one processor is configured to arrange the cell information on each of the plurality of scales in the order in input data in which a data size of each of the plurality of scales is defined such that when a scale size is smaller, the data size is larger, and to input the arranged input data to the learning model (Figure 7, [0042] A heat map is generated to represent each of the training document images, and the cell is filled with a value that equals to the fraction of the area occupied by the region for the document field contained within the cell).
Regarding claim 7, Semenov et. al. discloses the layout analysis system according to claim 2,wherein when a total size of the cell information on each of the plurality of scales is less than a standard size determined for input data for the learning model, the at least one processor is configured to add padding to the input data to make up for a shortfall in the total size from the standard size, to arrange the cell information on each of the plurality of scales in the order in the padded input data, and to input the arranged input data to the learning model ([0024], The training phase may involve generating heat maps corresponding to a plurality of document image pixels with a specified cell size from a rectangular grid to identify candidate regions that satisfy a threshold condition based on a pre-defined share of pixels, [0025-0026] the extracted content of each document field can be evaluated using BPE tokens by evaluating the differences between each to produce more accurate field detection in documents based on a threshold).
Regarding claim 8, Semenov et. al. discloses the layout analysis system according to claim 1,wherein at least one processor is configured to acquire, from among the plurality of scales, the cell information on a scale in which a plurality of words is a unit of the cell based on any one of the plurality of words (Figure 7, [0037] the world selection subunit 330 of system 300 is a submodule that uses a heuristic algorithm to analyze document text. The text can be analyzed for selection of words on document layout based on character types, such as letters, numerals, separators, etc.).
Regarding claim 9, Semenov et. al. discloses the layout analysis system according to claim 1,wherein at least one processor is configured to detect the cell of each of the plurality of scales such that at least one of the plurality of components is included in a cell having a different scale from other cells [0046, Figure 4A heat maps are generated for a chosen reference element and the grid size is a hyperparameter with different values such as 64 x64 pixels or 32 x 32 px, etc.).
Regarding claim 10, Semenov et. al. discloses the layout analysis system according to claim 1, wherein at least one processor is configured to: divide the document image into a plurality of small areas based on division positions determined in advance, and to acquire small area information relating to each of the plurality of small areas, and analyze the layout based on the cell information on each of the plurality of scales and the small area information on each of the plurality of small areas (Figure 6 shows the document markup, and Figure 4A shows a representative heatmap generated with certain color elements).
Regarding claim 11, Semenov et. al. discloses the layout analysis system according to claim 1,wherein the plurality of scales includes a token level in which a token including a plurality of words is a unit of the cell and a word level in which a word is a unit of the cell ([0048] the system uses BPE tokens which refers to a numeric vector representing an input text).
Regarding claim 12, Semenov et. al. discloses the layout analysis system according claim 1,wherein at least one processor is configured to detect the plurality of cells by executing optical character recognition on the document image ([0022-0025], a neural network is trained using a small number of marked-up documents as training documents, Figure 1 field detection engine 122; the selected candidate regions may then be treated as the positions of the corresponding fields by applying OCR techniques to the image fragments lying within the candidate regions).
Regarding claim 13, the rejection of claim 1 is incorporated herein. Semenov et. al. also discloses a layout analysis method, comprising: detecting a cell of each of a plurality of scales from in a document image showing a document including a plurality of components; acquiring cell information relating to the cell of each of the plurality of scales; and analyzing a layout relating to the document based on the cell information on each of the plurality of scales (Figure 2, 6, [0022] an document can be an invoice with Company Name, Total, Due Date, etc. with various reference elements then represented as metadata indicating the positions and names of the document fields).
Regarding claim 14, the rejection of claim 1 is incorporated herein. Semenov et. al. also discloses a non-transitory computer-readable information storage medium for storing a program for causing a computer to detect a cell of each of a plurality of scales from in a document image showing a document including a plurality of components; acquire cell information relating to the cell of each of the plurality of scales; and analyze a layout relating to the document based on the cell information on each of the plurality of scales (Figure 8 shows a computer readable medium for storing the computer readable program).
Conclusion
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 January 16, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668