DETAILED ACTION
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 .
Claims 1-29 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/20/2023 and 08/19/2025 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 19 and 29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent claims 1, 19 and 29
Step 1 -- whether the claim falls within any statutory category. See MPEP 2106.03
Claim 1 is drawn to a system claim, claim 19 is drawn to a method claim and claim 29 is drawn to computer program product claim. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding claim 1 is directed to a system for optimized training of a neural network model for data extraction, the system comprising the limitations of: generate …the entity text; generate N-grams by analyzing … in a predefined order; compare the generated N-grams … a field name; tokenize each word … assigning a token marker. These limitations are directed towards the abstract idea which is performed manually by a human being and/or by paper and pen. These limitations are directed towards the abstract idea of a mathematical relationship, specially organizing information and manipulation information through mathematical correlations.
Independent claim 19 is a method claim reciting similar limitations of claim 1 and is directed towards the abstract idea for similar reasons.
Independent claim 29 is a product claim reciting similar limitations of claim 1 and is directed towards the abstract idea for similar reasons.
Step 2A Prong 2 -- whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Regarding independent claim 1, the claim recites additional elements of “memory”, “processor”, “data extraction engine” and “train neural network model”.
These limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). The additional limitations “memory”, “processor”, and “data extraction engine” amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It recites a generic computer or generic computer components that merely act as a tool on which the method operates, and thus, fails to integrate the exception into a practical application. In addition, it recites using neural network (NN) model for extracting data from documents and train the neural network model based on the extracted data. A human being can extract data from a document, and observe a model with the extracted data, and thus, fails to integrate the exception into a practical application.
Regarding independent claim 19, this claim is drawn to a method claim reciting similar limitations of claim 1 and is rejected under the same rationale. Claim 19 also recites additional elements of “memory”, “processor”, “data extraction engine” and “train neural network model”.
These limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). The additional limitations “memory”, “processor”, and “data extraction engine” amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It recites a generic computer or generic computer components that merely act as a tool on which the method operates, and thus, fails to integrate the exception into a practical application. In addition, it recites using neural network (NN) model for extracting data from documents and train the neural network model based on the extracted data. A human being can extract data from a document, and observe a model with the extracted data, and thus, fails to integrate the exception into a practical application.
Regarding independent claim 29, this claim is drawn to a product claim reciting similar limitations of claim 1 and is rejected under the same rationale. Claim 29 also recites additional elements of “memory”, “processor”, “data extraction engine” and “train neural network model”.
These limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). The additional limitations “memory”, “processor”, and “data extraction engine” amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It recites a generic computer or generic computer components that merely act as a tool on which the method operates, and thus, fails to integrate the exception into a practical application. In addition, it recites using neural network (NN) model for extracting data from documents and train the neural network model based on the extracted data. A human being can extract data from a document, and observe a model with the extracted data, and thus, fails to integrate the exception into a practical application.
Claim Rejections - 35 USC § 103
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Appalaraju et al. (Appalaraju), US Patent No. US 11,893,012 B1, and further in view of Zuev et al. (Zuev), US Patent Application Publication No. US 2019/0385054 A1, and further in view of Booth, US Patent Application Publication No. US 2022/0382983 A1.
comprising:
As to independent claim 1, Appalaraju discloses a system for optimized training of a neural network model for data extraction, the system comprising:
a memory storing program instructions (Figure 10 and col. 22, line 46 – col. 23, line 4: system memory may be configured to store instructions);
a processor executing instructions stored in the memory (Figure 10 and col. 22, line 46 – col. 23, line 4: system memory may be configured to store instructions and data accessible by processor(s)); and
a data extraction engine executed by the processor and configured to:
generate an input document by extracting words from the input document along with coordinates corresponding to each word, wherein the extracted words include entity text and neighboring words associated with the entity text (Figure 3 and col. 12, line 54 – col. 13, line 8: preliminary content extraction may first be performed on a scanned/photographed target document, the target document may be provided as input to one or more machine learning models such as optical character recognition (OCR) models, and output comprising detected characters, words, numbers and the like, along with their respective bounding boxes such as location information (coordinates); col. 11, line 55 and col. 12, line 35 and Figure 2: the extracted content elements contain keys such as name, date of birth, contact information and these are considered as entity text, wherein some keys such as “contact information” may comprise several lower-level keys (neighboring words) such as Street , City, state, and the like);
generate N-grams by analyzing the neighboring words associated with the entity text present in the predetermined format type of the document based on a threshold measurement criterion and combining the extracted neighboring words in a pre-defined order (col. 11, lines 18-41: the corresponding content elements may be identified using n-gram analysis in some implementations, in which n-grams of a chosen token length, e.g, 2 words or tokens, or 3 words/tokens, or n-gram occurs such as nearby words may be performed);
compare the generated N-grams with the coordinates corresponding to the words for labelling the N-grams with a field name (col. 11, lines 18-41: the objects being analyzed are text-containing documents, the structural comparison may comprise a preliminary step of identifying corresponding content elements, the corresponding content elements may be identified using n-gram analysis in which n-grams of a chosen token length that are present within both the target document and the reference document are found);
each word in the N-gram identified by the field name in accordance with a location of each of the words relative to a named entity (NE) (col. 19, lines 33-55: verifying the n-grams representing keys (field name) match the n-grams in the document, and the number of verified matching keys may be used as the key location similarity metric to select the best matched of the reference document); and
train a neural network model based on the tokenized words in the N-gram, wherein the trained neural network model is implemented for extracting data from documents (col. 9, line 63 – col. 10, line 25: the preliminary content extraction tools/models may include one or more machine learning models trained to perform optical character recognition (OCR) and the related-content group descriptor (RGD) extracted from the target document image may comprise text tokens, e.g., characters or words and their bounding boxes (coordinates).
Appalaraju discloses extracting content from an image of a document. Appalaraju, however, does not disclose the input document is a predetermined format type.
In the same field of endeavor, Zuev discloses providing mechanisms for identification of text fields in electronic documents using neural networks, wherein the mechanisms can automatically detect text fields contained in an electronic document and associates each of the text fields with a field type (paragraph [0017]). Zuev further discloses electronic document may refer to a file comprising one or more digital content items that may be visually rendered to provide a visual representation of the electronic documents, and an electronic document may conform to any suitable file format, such as PDF, DOC, ODT, etc. (paragraph [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Appalaraju to include an electronic document in any suitable file format, as taught by Zuev for the purpose of providing classifications of any type of documents to train a neural network (Zuev, paragraph [0019]).
Appalaraju and Zuev, however, do not disclose tokenize each word for assigning a token maker.
Booth discloses receiving text and processing the text into natural language processing (NLP) tokens, wherein an NLP token is generally a single word or a single character and is assigned several properties such as an identifier (token maker) (paragraph [0023] and [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the systems of Appalaraju and Zuev to include assigning a token marker for tokenized each word, as taught by Booth for the purpose of indicating a position of the token in the input text and identifying toke relationships based on token properties such as dependency label.
As to dependent claim 2, Appalaraju and Zuev disclose wherein the input document is a structured or a semi-structured document and is in a pre-defined format including a Portable Document Format (PDF), or an image format, and wherein the predetermined format type of the input document is an XML file (Zuevl, paragraph [0018]).
As to dependent claim 3, Appalaraju discloses wherein the data extraction engine comprises an annotation unit executed by the processor and configured to render a Graphical User Interface (GUI) via a input unit for carrying out an annotation operation on the predetermined format type of the document, and wherein the annotation unit generates annotation data by copying text from a relevant field in the predetermined format type or the pre-defined format of the document and selecting a text field corresponding to the relevant field for pasting the copied data by using a rubber band technique, and wherein the rubber band technique is used to determine coordinates corresponding to the text field with the copied data, which is stored by the annotation unit in a database, and wherein the annotation data is used for generation of N-grams (col. 5, line 64 – col. 6, line 54; col. 7, lines 12-18).
As to dependent claim 4, Appalaraju and Zuev disclose wherein the data extraction engine comprises an N-gram generation and labelling unit executed by the processor and configured to determine entity text by analyzing neighboring words corresponding to the entity text from left, top right and bottom of the entity text present in the predetermined format type for generating N-grams, and wherein the neighboring words of the entity text are analyzed by applying a threshold distance measurement criterion from the entity text (Appalaraju, col. 4, lines 46 – col. 5, line 7; Zuevl, paragraph [0018]).
As to dependent claim 5, Appalaraju discloses wherein the N-gram generation and labelling unit changes the threshold distance to value -1, in the event it is determined that the neighboring words associated with the entity text are not available at the threshold distance from the entity text, to avoid blank spaces between the neighboring words and entity text (col. 13, line 45 – col. 14, line 2 and col. 15, lines 4-26).
As to dependent claim 6, Appalaraju discloses wherein the N-gram generation and labelling unit extracts five neighboring words from the left of the entity text, three neighboring words from the top of the entity text, two neighboring words from right of the entity text, and three neighboring words from bottom of the entity text (col. 11, lines 18-41).
As to dependent claim 7, Appalaraju and Zuev disclose wherein the N-gram generation and labelling unit is configured to determine one or more entity text features from the pre-determined format type of the documents for generation of N-grams, and wherein the text features include position of the entity text in the predetermined format type of the documents, and format of the entity text (Appalaraju, col. 4, lines 46 – col. 5, line 7; Zuevl, paragraph [0018]).
As to dependent claim 8, Appalaraju discloses wherein the position-based features of the entity text include the entity text present at top-left of the document, the entity text present at top-right of the document, the entity text present at bottom-left of the document or the entity text present at bottom-right of the document (col. 4, line 46 – col. 5, line 7).
As to dependent claim 9, Appalaraju and Zuev disclose wherein an N-gram generation and labelling unit is configured to label the generated N-grams by carrying out a matching operation, and wherein the matching operation is carried out by identifying the N-grams using a field value associated with a particular field in the predetermined format type document and the one or more coordinates along with annotation data, which are stored in the database (Appalaraju, col. 6, lines 20-54; Zuevl, paragraph [0018]).
As to dependent claim 10, Appalaraju discloses wherein based on determination of a match, the N-gram generation and labelling unit labels the N-grams with a field name, and wherein the N-gram generation and labelling unit is configured to label all the unmatched N-grams as 'others' (Appalaraju, col. 6, lines 20-54).
As to dependent claim 11, Appalaraju and Zuev disclose wherein the data extraction engine comprises a post processing unit executed by the processor and configured to process the predetermined format type of the documents for converting all the numeric values present in the document to a machine-readable format (Appalaraju, col. 6, lines 20-54; Zuevl, paragraph [0018]).
As to dependent claim 12, Appalaraju, Zuev and Booth disclose wherein the data extraction engine comprises a tokenization unit executed by the processor and configured to process the generated and labelled N-grams for carrying out a tokenization operation for tokenizing each N-gram and classifying each token with the token marker (Booth, paragraph [0023] and [0037]).
As to dependent claim 13, Appalaraju discloses wherein the data extraction engine comprises a data extraction model training unit executed by the processor and configured to receive the tokenized words from a tokenization unit to train the neural network model (col. 9, line 63 – col. 10, line 25).
As to dependent claim 14, Appalaraju discloses wherein the data extraction model training unit is configured to convert the tokenized words in the N-gram into sequences and each tokenized word is assigned an integer, and wherein the sequence is padded such that each tokenized word is of a same length, and wherein the padded sequence of words is used as an input for training the neural network model for data extraction (col. 17, lines 20-29).
As to dependent claim 15, Appalaraju discloses wherein the data extraction engine comprises a model accuracy improvement unit executed by the processor and configured to communicate with a data extraction model training unit for improving accuracy of the neural network model in order to effectively extract data from documents, and wherein the model accuracy improvement unit is configured to receive inputs relating to the extracted data from a data extraction unit for improving accuracy of the neural network model (col. 4, lines 11-45).
As to dependent claim 16, Appalaraju and Zuev disclose wherein the model accuracy improvement unit is configured to generate negative N-grams by carrying out a comparison operation with the annotation data, and wherein the model accuracy improvement unit is configured to extract data fields present in the predetermined format type of the document using the trained neural network model and compare the extracted data fields with the annotated data stored in the database (Appalaraju, col. 4, lines 11-45; Zuevl, paragraph [0018]).
As to dependent claim 17, Appalaraju discloses wherein in the event the model accuracy improvement unit determines that field values associated with the data fields do not match with the annotated data, then the N-grams that are generated are determined as negative N-grams and are labelled as 'others', and wherein one or more criteria are employed for determining the match including determining if distance between extracted fields and annotated data is minimal or within a pre-defined threshold, then the N-grams are not labelled as 'others', and if one or more keywords associated with the fields in the document are present in negative N-grams, then such N-grams are not considered as 'others', thereby avoiding any positive N-grams being labelled as 'others', and wherein the model accuracy improvement unit is configured to up-scale the generated N-grams for each field except for N-grams labelled as ‘other’(col. 17, lines 20-29).
As to dependent claim 18, Appalaraju discloses wherein the model accuracy improvement unit is configured to determine a confidence score for the field values present in the document based on predictions made by the neural network model, and wherein in the event the neural network model predicts two or more values for a particular field in the document, then the model accuracy improvement unit is configured to filter the values based on the confidence score, and wherein the model accuracy improvement unit considers the values with maximum confidence score (col. 13, line 45 – col. 14, line 54).
Claims 19-28 are method claims that contain similar limitations of claims 1, 2, 4,5, 7, 9, 10, 14, 16 and 17, respectively. Therefore, claims 19-28 are rejected under the same rationale.
Claim 29 is a computer program product that contains similar limitations of claim 1. Therefore, claim 29 is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication should be directed to CHAU T NGUYEN at telephone number (571)272-4092. The examiner can normally be reached on M-F from 8am to 5pm (PT).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated-interview-request-air-form.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/CHAU T NGUYEN/Primary Examiner, Art Unit 2145