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 .
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1, 2, 7-11, 13, 14 and 19-23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Neogi et. al. (US Pub. No. 20090116736A1 ) .
As per claim 1, Neogi teaches “A document index generating system comprising: at least one processor; and a memory storing a sequence of instructions which when executed by the at least one processor configure the at least one processor to:
preprocess a plurality of pages into a collection of data structures, each data structure comprising a representation of data for a page of the plurality of pages, the representation comprising at least one region on the page, the at least one processor configured to: (See paragraphs 58-61 “[0058] The job database 528 is used to receive, then process and finally post the user's job back to the content repository. A “job” is defined as the steps of automatically organizing the electronic document from their original scanned images. Module 649 can be file system storage, a relational database, XML document or a combination of these. In the current implementation, the system uses both file system storage to store large blob (binary large objects) and a relational database to store pointers to the blobs and other information pertinent to processing the job…. [0060]… Once a new job arrives, the SCM pre-processes the job by taking the electronic document and separating each image (or page) into its own bitmap image for further processing. For example, if an electronic document had 30 pages, the system will create 30 images for processing. Each job in the system is given a unique identity. Furthermore, each page is given a unique page identity that is linked to the job identity. After the SCM has created image files by pre-processing the document into individual pages, it transitions the state of each page to image processing.” See also paragraphs 113-145. Neogi)
“for each page, normalize the plurality of pages into a collection of images and a collection of plain text;” (See paragraphs 113-146 “[0118]… For the classification system to work well, document images must be binarized and the text must be readable… [0119] The preferred embodiment of the binarization system utilizes local threshholding where the threshold value varies based on the local content in the document image… [0120] The noise cleanup system removes dots, specks and blobs from documents…0121] The first technique starts with any small region of a binary image. The preferred implementation takes a 35×35 pixel region. In this region all background pixels are assigned value “0.” Pixels adjacent to background are given value “1.” A matrix is developed in this manner. In effect each pixel is given a value called the “distance transform” equal to its distance from the closest background pixel. The preferred implementation runs a smoothing technique on this distance transform. Smoothing is a process by which data points are averaged with their neighbors in a series… [0124] The orientation correction system aligns document images so that they can be most easily read… [0127]… Small samples are selected from a document and the confidence is averaged across the sample. The orientation that has the highest confidence determines the correct orientation of the document… [0129] The text line detection system implements a technique described by Okun et al. (reference: “Robust Text Detection from Binarized Document Images”) to identify candidate text segments blocks of consistent heights. For a page from a book, this method may identify a whole line as a block, while for a form with many boxes this method will identify the text in each box. [0130] The confetti generation module identifies all the coordinates of the blocks.” See also fig. 7. Paragraphs 144-146 also teaches normalization. Neogi)
“for each page, obtain vision features from the collection of images; and” (See paragraphs 133-145 “[0133] The feature identification system looks for point and line features. The preferred implementation performs image layout analysis using two image properties, the point of intersection of lines and edge points, as shown in FIG. 8, of text paragraphs. Every unique representation of points is referred as a unique class in the system and represents a unique point pattern in the system database. The preferred implementation uses a heuristically developed convolution method only on black pixels to perform a faster computation. The system identifies nine types of points: four T's, four L's, and one cross (X) using nine masks… [0137] The preferred implementation performs classification using both image-level features and textual features…” Neogi)
“for each page, process the collection of plain text;” (See paragraphs 125-145 “[0125] The first method detects blocks of text in the image and measures each with respect their block height and width… [0129]… For a page from a book, this method may identify a whole line as a block, while for a form with many boxes this method will identify the text in each box… [0137] The preferred implementation performs classification using both image-level features and textual features… [0139] The OCR system converts each confetti image into text. [0140] The text retrieval system presents the text to the key word identification system.” Neogi )
“classify each preprocessed page into at least one document type;” (See paragraphs 41, 62 and 140-150. “[0041] The production system classifies each of the pages in the document as one of a pre-identified set of types of documents. The production system organizes the classified pages per a predetermined scheme into a new, organized document… [0062] The classification system 556 recognizes the page as one of a pre-identified set of types of documents. ” Neogi)
“segment groups of classified pages into documents; and” (See paragraphs 41 and 58-61 “[0041] The production system classifies each of the pages in the document as one of a pre-identified set of types of documents. The production system organizes the classified pages per a predetermined scheme into a new, organized document. The production system stores the original scanned document and the organized document. The production system is described in greater detail below.” Neogi)
“generate a page and document index for the plurality of pages based on the classified pages and documents.” (See paragraphs 29, 48, 150-158 and 171. “[0029] Preferred embodiments of the present invention provide a method and system for converting paper and digital documents into well-organized electronic documents that are indexed, searchable and editable. The resulting organized electronic documents support more rapid and accurate data entry, retrieval and review than randomly sequenced sets of pages.” “[0048] The document management system (DMS) 514 and the business object layer capture the business entities. The DMS is generally a computer-based system or set of servers used to track and store electronic documents and/or images of paper documents. The DMS also commonly provide storage, versioning, metadata, security, as well as indexing and retrieval capabilities.” See also paragraphs 58-60. Neogi)
Claim 13 is rejected under the same analysis as claim 1.
As per claim 2, Neogi teaches “The document index generating system as claimed in claim 1, wherein to preprocess the collection of plain text, the at least one processor is configured to at least one of: (i) perform optical character recognition (OCR) to the collection of plain text; (ii) extract a word list, a word index list and a word boundary box list from the collection of plain text; or (iii) generate arrays of entries comprising, for each indexed item in the word list, word index list and word boundary box list, an indexed word, an indexed word index and an indexed word boundary box; and pass the arrays of entries to a transformer to extract text features.” (See paragraphs 136-142, an OCR is performed on the collection of text, it at least teaches one of the presented limitations. See also paragraphs 11, 19 and 48. Neogi)
Claim 14 is rejected under the same analysis as claim 2.
As per claim 7, Neogi teaches “The document index generating system as claimed in claim 1, wherein preprocessing the plurality of pages into a collection of data structures comprises: for each page in the plurality of pages: converting that page to a bit map file format;” (See paragraphs 60 “[0060] In preferred implementations the SCM subscribes to events for each new incoming job that need to be processed. Once a new job arrives, the SCM pre-processes the job by taking the electronic document and separating each image (or page) into its own bitmap image for further processing.” Neogi) “determining regions on that page based on at least one of: the location of the region on the page; the content in the region; or the location of the region in relation to other regions on the page;” (See paragraphs 60-62 “[0061]… The image processing system performs connected component analysis and, utilizing a line detection system, creates “confetti” images which are small sections of the complete page image. Under preferred embodiments, the confetti images are accompanied the coordinates of the image sub-section.” See also paragraphs 125-141 ) “converting each region of that page into a machine-encoded content; collecting the regions and corresponding content for that page into a data structure for that page;” “and merging the page data structures into the collection of data structures” (See paragraphs 136-157, The confetti regions are converted into text and used as vectors and strings which are used for organizing the collection of pages into documents. Neogi )
Claim 19 is rejected under the same analysis as claim 7.
As per claim 8, Neogi teaches “The document index generating system as claimed in claim 7, wherein determining regions on that page comprises: searching sections of the page for text or other items, the section comprising at least one of a top third of the page; a middle third of the page; a bottom third of the page; a top quadrant of the page; a bottom 15 percent of the page; a bottom right corner of the page; a top right corner of the page; or the full page.” (See paragraphs 129-141 “[0133] The feature identification system looks for point and line features. The preferred implementation performs image layout analysis using two image properties, the point of intersection of lines and edge points, as shown in FIG. 8, of text paragraphs. Every unique representation of points is referred as a unique class in the system and represents a unique point pattern in the system database. The preferred implementation uses a heuristically developed convolution method only on black pixels to perform a faster computation The system identifies nine types of points: four T's, four L's, and one cross (X) using nine masks.” See also fig. 8 it shows the searching sections of the page which shows the presented limitations. See also paragraphs 60-62. Neogi)
Claim 20 is rejected under the same analysis as claim 8.
As per claim 9, Neogi teaches “The document index generating system as claimed in claim 1, wherein classifying each preprocessed page into at least one document type comprises: determining candidate document types for the page for each page in the collection of data structures.” (See paragraphs 41, and 60-62. “0041] The production system classifies each of the pages in the document as one of a pre-identified set of types of documents. The production system organizes the classified pages per a predetermined scheme into a new, organized document. The production system stores the original scanned document and the organized document. The production system is described in greater detail below.” “[0062] The classification system 556 recognizes the page as one of a pre-identified set of types of documents… In one domain, multiple systems that categorize income tax documents such as W-2, 1099-INT, K-1 and other forms have experienced poor accuracy because of the thousands of variations of tax documents. The preferred implementation uses a combination of image pattern recognition and text analysis to distinguish documents and machine learning technology to scale to large numbers of documents…” The candidate document types are the pre-identified types. See also paragraphs 137-138. “0137] The preferred implementation performs classification using both image-level features and textual features. The key challenge in classification architecture is defining the classifier appropriate to the domain. Many forms and documents, such as business letters, tax forms, mortgage applications, health insurance forms, etc., have structural layouts and associated text, each of which have important domain information.” See also paragraphs 150-157. Neogi)
Claim 21 is rejected under the same analysis as claim 9.
As per claim 10, Neogi teaches “The document index generating system as claimed in claim 9, wherein determining the candidate document type for the page comprises: determining confidence score values for each candidate document type based on at least one of: a presence of a combination of regions on the page; or content in at least one of: a region category types for each region on the page; a title of the page; an origin of the page; a date of the page; or a summary of the page.” (See paragraphs 137-157 “[0145] The vector space creation system stores in a table the priority of each word in the form. A vector is described as (a1, a2, . . . ak). Where a1, a2 . . . ak are the magnitude in the respective dimensions. For example, for input words and corresponding line heights of a W-2 tax form, the following are word-priority vectors are stored… [0146] In such a vector space, the words with larger font size or higher frequency will have higher priority. [0147] The ranking system calculates the cosine distance of two vectors V1 and V2 as: cos θ=(V1.V2)/(|V1|*|V2|) where V1.V2 is the dot product of two vectors and |V| represents the magnitude of the vector. When the cosine distance nears 0, that means the vectors are orthogonal and when it nears 1 it means the vectors are in the same direction or similar. [0148] The class which has the maximum cosine distance with the form is the class to which the form should be classified, and is shown by module 965. [0149] The class identification system performs point pattern matching based on the image features collected during image processing. As mentioned earlier, the point pattern matching of documents is performed by creating a string from the points detected in the image and then using Levenshtein distance to measure the gap between the trained set with the input image.” See also paragraphs 121-136. See also paragraphs 107- 112. “[0156] The summary page system creates a summary of key document data. In the preferred implementation, the summary includes a table of contents, the date and time the document was processed, the name of the job, etc.” Neogi )
Claim 22 is rejected under the same analysis as claim 10.
As per claim 11, Neogi teaches “The document index generating system as claimed in claim 1, wherein segmenting groups of pages into documents comprises: clustering contiguous pages based on at least one of: similar document types; similar document titles; or sequential page numbers.” (See paragraphs 150-157 “[0151] FIG. 10 is a system diagram of the organized document development system. System 564 has a business rules database 1010, a business rules engine 1020, a bookmark and tab library 1040, a summary page system 1042 and a database update system 1044. [0152] The business rules database stores rules that determine the ordering of documents for a given domain. For example, a simple business rule for organizing tax documents is to organize all wage related documents like W-2's first, followed by interest income documents, etc.” “[0155] The bookmark and tag library creates an organized electronic document based on the outputs of the business rules engine.” See also fig. 10 and fig. 11. See also paragraphs 48 and 41-45. Neogi )
Claim 23 is rejected under the same analysis as claim 11.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Neogi in view of Badjativa et. al. (US Pub. No. 20220245391 A1) .
As per claim 3, Neogi teaches “The document index generating system as claimed in claim 1, wherein to obtain vision features of the collection of images, the at least one processor is configured to…”, however Neogi does not teach “pass images of the plurality of pages through a convolution neural network.”
Badjatiya teaches “pass images of the plurality of pages through a convolution neural network.” (See paragraphs 36-38, 97 and 110 “097] Example 5 includes the subject matter of any of Examples 1-4, wherein the first NN is an image encoding convolutional NN (CNN) and the method further comprises: generating the first visual feature vector as an output of a first group of layers of the image encoding CNN, the first group of layers including a convolutional layer, a batch normalization layer, and a max pooling layer; and generating the second visual feature vector as an output of a second group of layers of the image encoding CNN, the second group of layers in series with the first group of layers and including a convolutional layer, a batch normalization layer, and a max pooling layer.” Badjatiya)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Neogi with the teachings of Badjatiya to pass images through a convolutional neural network to obtain vision features. The modification would have been motivated by the desire to have more attetntiveness to context features as well as more efficient searching and better accuracy, therefore it is an improvement, as suggested by Badjatiya (See paragraphs 36-39 and 97 “[0037] To this end, techniques are provided herein for text-conditioned image search based on transformation, aggregation, and composition of visio-linguistic features from both the given image and the given text query to generate improved context aware features for image retrieval, as will be explained in greater detail below. The techniques provide an improvement in searching efficiency and accuracy over existing technical solutions, which fail to capture and utilize detailed and potentially complex user requirements.”. See also paragraphs 42, 16 and 18 Badjatiya)
Claim 15 is rejected under the same analysis as claim 3.
Claims 6, 12, 18 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Neogi in view of Zovic et. al. (WO Pub. NO. 2020243846 A1) .
As per claim 6, Neogi already teaches “The document index generating system as claimed in claim 1, wherein to preprocess the collection of plain text, the at least one processor is configured to extract a word list, a word index list and a word boundary box list from the collection of plain text, wherein the at least one processor is configured to generate a first set of arrays of entries comprising, for each indexed item in the word list and word boundary box list, an indexed word and an indexed word boundary box;…” (See paragraphs 137-157 and 125-136 “[0129] The text line detection system implements a technique described by Okun et al. (reference: “Robust Text Detection from Binarized Document Images”) to identify candidate text segments blocks of consistent heights. For a page from a book, this method may identify a whole line as a block, while for a form with many boxes this method will identify the text in each box…[0130] The confetti generation module identifies all the coordinates of the blocks. ” “0143] The key word prioritization system, in the preferred implementation, calculates the priority of each word as function of line height (LnHt) of the word, partial of full match (PFM) with form name and total number of words in that form (N)… ” See also paragraphs 144- 149 along with the tables which also shows a word list, word index list and word boundary box list. “[0145] The vector space creation system stores in a table the priority of each word in the form. A vector is described as (a1, a2, . . . ak). Where a1, a2 . . . ak are the magnitude in the respective dimensions. For example, for input words and corresponding line heights of a W-2 tax form, the following are word-priority vectors are stored:” ), however Neogi does not teach “generate a second set of arrays of entries comprising, for each indexed item in the word list and word index list, an indexed word and an indexed word index; pass the first set of arrays to a transformer; pass the second set of arrays to a pre-trained BERT model; and merge the results from the transformer and the pre-trained BERT model.”,
Zovic teaches “generate a first set of arrays of entries comprising, for each indexed item in the word list and word boundary box list, an indexed word and an indexed word boundary box; generate a second set of arrays of entries comprising, for each indexed item in the word list and word index list, an indexed word and an indexed word index; (See paragraph 72-78 “[0077] Once groups of data are smoothed out and organize, the data may be fed into a document list generation function to output a page and document index structure (e.g., docList). In some embodiments, document list generation comprises i) completing a candidate list and indexing the candidates, ii) generating a document structure/outline based on the likeliest page, date, title, and origin, iii) creating a list generator which feeds off of the clustering algorithm and itemizes a table of contents (i.e., after clustering all pages into documents and extracting all meta information for these documents, then these meta information and page ranges of documents can be listed in a table of contents), and iv) taking the table of contents and converting it into a useable document format for the user (i.e., adding the generated index/table of contents to the original PDF file). [0078] FIG. 7 illustrates, in a flowchart, an example of a method of generating an index (or a table of contents) 700 from the output of the classification component, in accordance with some embodiments. The method comprises sorting the .sup.'documents.sup.' key by indexed pages 710, extracting the top candidate for .sup.'date.sup.', .sup.'title.sup.' and .sup.'origin.sup.', and the earliest indexed page for each entry in .sup.'documents.sup.' 720, and formatting the resulting list 730 (for example as a PDF, possibly with hyperlinks to specified page indices). Other steps may be added to the method 700.” See also paragraphs 88-92, “One way is to tokenize the document into sentences or fragments, and group the number of sentences or fragments by their indices. Another way to group a number of sentences and/or fragments by their correlation/relation/relevance (e.g., two or more fragments or sentences comprise a chunk). It should be noted that a different number of fragments and/or sentences can comprise a chunk…” ) “pass the first set of arrays to a transformer; pass the second set of arrays to a pre-trained BERT model; and merge the results from the transformer and the pre-trained BERT model.” (See paragraphs 86-94 and 75 “Graph-based clustering may be used to determine similarities or relations between BERT based vectors and encoded sentences or“chunks” of contnet. In some embodiments, BERT-based vectors may be used to assist with computing the graph community and extracting the most important sentences and chunks with a graph algorithm (e.g., PageRank)… [0088]… The method 800 obtaining a document 802, dividing or splitting the document into groupings of content (i.e.,“chunks”) 804, encoding the chunks into a natural language processing format (e.g., word2vec or BERT-based vectors) into the chunks 806, clustering the encoded chunks 808 into groupings based on their encodings, determining the most central points (e.g., closest chunk to the centroid of the clustered chunks) 810 of the clustered chunks, and generating a summary 812 for the document based on the most central points (e.g., closest chunk) Other steps may be added to the method 800. It should be noted that a “chunk” comprises a group of content such as, for example, a group of sentences and/or fragments, whether continuous or not in the original document…[0091] Referring back to FIG. 8A, BERT or other vectorizing or natural language processing methods may be applied to each chunk 806. Each chunk will be converted into a high dimensional vector. BERT and Word2Vec are two approaches that can convert words and sentences into high dimensional vectors so that mathematical computation can be applied to the words and sentences. For example, the system may generate a vocabulary for the entire context (based on trained model), and input the index of all words of sentences/chunks in the vocabulary to a BERT/Word2Vec based neural network, and output a high dimensional vector, which is the vector representation of the chunk. The dimension of the vector may be predefined by selecting the best tradeoff between speed and performance. ” See also paragraphs 66 “In some embodiments, a bidirectional encoder representations from transformers (BERT) language model may be used for the classification. It should be noted that the neural network may be updated automatically based on error correction 364. For example, parameters in the BERT and/or generative pretraining transformer 2 (GPT-2) algorithms may be fine-tuned with customized datasets and customized parameters. This will improve performance.” Zovic)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Neogi with the teachings of Zovic to include a BERT language model to merge the results of sets of indexed items. The modification would have been motivated by the desire to extract high importance items, correct errors as well as improving performance, therefore it is an improvement, as suggested by Zovic (See paragraphs 86, 66, 70-77, “[0086] Clustering may be applied for extractive summarization by finding the most important sentences or chunks from the document. In some embodiments, BERT-based sentence vectors may be used. Graph-based clustering may be used to determine similarities or relations between BERT based vectors and encoded sentences or“chunks” of contnet. In some embodiments, BERT-based vectors may be used to assist with computing the graph community and extracting the most important sentences and chunks with a graph algorithm (e.g., PageRank)” “[0073] Once pages are segmented 362, an initial grouping of characteristics by page and by document is provided. Error correction 364 may take place to backfill missing data from the previous step (e.g., a missing page number). Errors are identified and adjusted by a clustering algorithm. In some embodiment, based on the information in the key value structure, groups of pages that are together (diagnostics, etc.), groups of relevant content based on scoring, and groups of relevant forms can all be identified.” “[0066]… It should be noted that the neural network may be updated automatically based on error correction 364. For example, parameters in the BERT and/or generative pretraining transformer 2 (GPT-2) algorithms may be fine-tuned with customized datasets and customized parameters. This will improve performance…” See also paragraphs 102-103. Zovic )
Claim 18 is rejected under the same analysis as claim 6.
As per claim 12, Neogi already teaches “The document index generating system as claimed in claim 1, comprising: analyzing characteristics of the pages and documents…”, however Neogi does not teach “to update missing information in the page and document index”.
Zovic teaches “to update missing information in the page and document index”. (See paragraphs 71-77 “[0071]… The three individual documents may then be processed separately by the page classifier to predict the missing meta information… [0073] Once pages are segmented 362, an initial grouping of characteristics by page and by document is provided. Error correction 364 may take place to backfill missing data from the previous step (e.g., a missing page number).” See also paragraphs 107 and page 20 claim 18. “18. The method as claimed in claim 12, comprising: analyzing characteristics of the pages and documents to update missing information in the page and document index.” Zovic )
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to combine the teachings of Neogi with the teachings of Zovic to update missing information of the page and index by analyzing characteristics of the page. The modification would have been motivated by the desire to correct errors as well as improving performance, therefore it is an improvement, as suggested by Zovic (See paragraphs 66 and 70-77,” [0073] Once pages are segmented 362, an initial grouping of characteristics by page and by document is provided. Error correction 364 may take place to backfill missing data from the previous step (e.g., a missing page number). Errors are identified and adjusted by a clustering algorithm. In some embodiment, based on the information in the key value structure, groups of pages that are together (diagnostics, etc.), groups of relevant content based on scoring, and groups of relevant forms can all be identified.” “[0066]… It should be noted that the neural network may be updated automatically based on error correction 364. For example, parameters in the BERT and/or generative pretraining transformer 2 (GPT-2) algorithms may be fine-tuned with customized datasets and customized parameters. This will improve performance…” See also paragraphs 102-103. Zovic )
Claim 24 is rejected under the same analysis as claim 12.
Conclusion
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/DYLAN JOHN MENDEZ MUNIZ/Examiner, Art Unit 2675
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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