DETAILED ACTION
Notice to Applicant
The following is a NON-FINAL Office action upon examination of application number 18/768,655 filed on 07/10/2024. In response to the Election/Restriction requirement of 11/28/2025, Applicant, on 01/28/2026, elected Group I, claims 1-12 and 13-20, for examination. Claims 1-25 are pending in this application, of which claims 1-20 have been examined on the merits discussed below.
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
3. The information disclosure statement (IDS) filed on 09/18/2024 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
4. Applicant's election without traverse of the restriction dated 11/28/2025 in the reply filed on 01/28/2026 is acknowledged.
5. In response to the restriction requirement, Applicant elected Group I, claims 1-12 and 13-20. Claims 21-25 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 01/28/2026.
Claim Objections
6. Claims 1, 7, and 13 are objected to because the following informalities: typographical errors.
Claim 1 recites “to provide a web-based user interface to receive configuration settings from an administrator of the enterprise, the web server comprising a workflow generator to automatically generate an executable code based on the configurations settings received by the web-based user interface.” There is a typographical inconsistency (“configuration setting” vs. “configurations settings”). Claim 1 should recite “to provide a web-based user interface to receive configuration settings from an administrator of the enterprise, the web server comprising a workflow generator to automatically generate an executable code based on the configuration settings received by the web-based user interface.” Similarly, claims 7 and 13 recite “configurations settings.” Claims 7 and 13 should recite “configuration settings”. Appropriate correction is required.
Claim 5 recites “The system of Claim 1, wherein, upon receipt of updated configuration settings from the web-based user interface, the work flow generator is to automatically update the executable code.” There is an inconsistent spelling with “workflow generator” in claim 1. Claim 1 recites “a workflow generator,” but claim 5 refers to “the work flow generator.” Claim 5 should recite “The system of Claim 1, wherein, upon receipt of updated configuration settings from the web-based user interface, the workflow generator is to automatically update the executable code.” (i.e., no space between the words “work” and “flow”). Appropriate correction is required.
7. Claims 7-8 and 14 recite “API”. Claim 14 is objected to because the acronym “API” should first be introduced in its unabridged form in the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 112
8. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
9. Claims 3, 6-7, and 13-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
10. Claim 3 recites “wherein the executable code is written in a language selected from a group consisting of Python and JavaScript.” Claim 3 contains the trademarks/trade names “Python” and “JavaScript.” Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a communication and, accordingly, the identification/description is indefinite. Appropriate correction is required.
11. Claims 6 and 16 recite the phrase “the predetermined threshold of confidence” which lacks antecedent basis, and therefore renders the claims indefinite. Appropriate correction is required.
12. Claims 7 and 13 recite the phrase “the web-based user interface input” which lacks antecedent basis, and therefore renders the claims indefinite. Appropriate correction is required.
13. Claim 13 recites “receive, from the requesting application…” The phrase “the requesting application” lacks antecedent basis, and therefore renders the claim indefinite. Appropriate correction is required.
14. Claim 16 recites “the corresponding document classification type,” which lacks antecedent basis, and therefore renders the claim indefinite. Appropriate correction is required.
15. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
16. 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.
17. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-12) and method (claims 13-20) are directed to at least one potentially eligible category of subject matter (i.e., machine and process, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-20 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 since the claims set forth steps for managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) within the “Certain Methods of Organizing Human Activity” abstract idea grouping, and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: a data extraction server to extract data from documents; and a web server in communication with a requesting application of an enterprise, a downstream application of the enterprise, and the data extraction server, the web server to provide a web-based user interface to receive configuration settings from an administrator of the enterprise, the web server comprising a workflow generator to automatically generate an executable code based on the configurations settings received by the web-based user interface, the executable code to: receive, from the requesting application, an image of a document; classify the document as one of a plurality of document classification types; transmit the document and document classification type to the data extraction server; receive, from the data extraction server, extracted data based on the document and document classification type; and publish the extracted data via a subscription service to the downstream application. With respect to independent claim 13, the limitations reciting the abstract idea are indicated in bold below: receiving, from a web-based user interface of a web server, configuration settings comprising a plurality of document classification types and a predetermined confidence threshold for each document classification type; and automatically generating, by a workflow generator of the web server, an executable code for a workflow based on the configurations settings received by the web-based user interface input, the executable code for the workflow to: receive, from the requesting application, an image of a document; classify the document as one of said plurality of document classification types; transmit, to a data extraction server, the image of the document and document classification type; receive, from the data extraction server, extracted data from the document; and publish the extracted data via a subscription service to a downstream application. Considered together, these steps set forth an abstract idea of that falls under the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings set forth in MPEP 2106. The steps related to receiving a document, determining its classification type, extracting relevant data, and providing that information to another application correspond to observations, evaluations, and judgments that can be performed in the human mind or with the aid of pen and paper. Thes activities also reflect the organization and management of business information within an enterprise, which is a form of organizing human activity.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: a data extraction server, a web server in communication with a requesting application of an enterprise, a downstream application of the enterprise, and the data extraction server, the web server to provide a web-based user interface, a workflow generator, and the executable code (claim 1); a web-based user interface of a web server, a workflow generator of the web server, the executable code, the requesting application, a data extraction server, and a downstream application (claim 13). These elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the transmitting and receiving steps are not deemed part of the abstract idea, these steps are at most directed to insignificant extra-solution activity, which is not sufficient to amount to a practical application. See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: a data extraction server, a web server in communication with a requesting application of an enterprise, a downstream application of the enterprise, and the data extraction server, the web server to provide a web-based user interface, a workflow generator, and the executable code (claim 1); a web-based user interface of a web server, a workflow generator of the web server, the executable code, the requesting application, a data extraction server, and a downstream application (claim 13). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment) and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification acknowledges that the claimed invention relies on nothing more than a general purpose computer executing instructions to implement the invention (Specification at paragraph [0099]). Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Even if the transmitting and receiving steps are not deemed part of the abstract idea, these steps are at most directed to insignificant extra-solution activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Similarly, with respect to the “extracting” step, even if considered as an additional element, when evaluated under Step 2A Prong Two and Step 2B, amounts to insignificant extra-solution activity, which does not amount to a practical application (MPEP 2106.05(g)), nor add significantly more because such activity has been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d). “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition).” See MPEP 2106.05(d).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-12 and 14-20 recite the same abstract ideas as recited in the independent claims by reciting steps/details for managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion). For example, dependent claims 2-12 and 14-20 recite the limitations “wherein the executable code comprises containerized code,” “wherein the executable code is written in a language selected from a group,” “storing the configuration settings, and wherein the configuration settings comprise extraction rules for the document classification type,” “wherein, upon receipt of updated configuration settings, automatically update the executable code,” “wherein the configuration settings comprise a predetermined confidence threshold for each document classification type, and wherein the executable code is to: compare a threshold of confidence for the classification of the document to the predetermined confidence threshold for the document classification type transmitted; transmit a human-in-the-loop request based on the threshold of confidence subceeding the predetermined threshold of confidence; and receive user validation of the classification,” “automatically generate an endpoint based on the configurations settings input,” “transmit a confirmation response to the upon receipt of the document and publish the extracted data via the subscription service,” “upload the image of the document and metadata associated with the document,” “a task queue, and wherein, for each document uploaded, classify the document as one of N different document classification types, where N is an integer greater than 1; transmit the image of the document and document classification type; receive extracted data based on the document; and publish the extracted data via the subscription service,” “wherein the extracted data is published in real time,” “extract data from the document based on the configuration settings,” “wherein automatically generating an executable code based on the configuration settings further comprises automatically generating a plurality of endpoints for the workflow,” “extract data from the document received; and receiving real-time feedback selected from a group consisting of acceptance feedback and rejection feedback,” “wherein the configuration settings comprise a predetermined confidence threshold for each document classification type, and automatically generate the executable code to: compare a threshold of confidence to the predetermined confidence threshold for the corresponding document classification type; and transmit a human-in-the-loop request based on the threshold of confidence subceeding the predetermined threshold of confidence,” “further comprising receiving a user validation of extracted data,” “further comprising, receiving a user validation of the document classification,” “further comprising processing the extracted data to conform to the downstream application,” and “visually identify data from the image of the document based on positioning of the data relative to an anchor location,” however, these limitations fall under the same “Mental Processes” and “Certain Methods of Organizing Human Activity” abstract idea groupings by describing additional details for organizing human activity and mental activity that can be accomplished via human observation, judgment, or evaluation. The containerized code (claim 2), Python and JavaScript (claim 3), a memory (claim 4), the work flow generator (claim 5), API and wherein the executable code comprises executable code for the API endpoint (claim 7), the executable code comprising executable code for a first API endpoint and a second API endpoint (claim 8), a document upload tool (claim 9), a bulk file upload tool (claim 10), a plurality of API endpoints (claim 14) have been evaluated as an additional element as well. However, these elements are recited at a high level of generality and fail to yield any discernible improvement to the computer or to any technology, nor set forth any additional function or result that provided meaningful limitation beyond linking the abstract idea to a particular technological environment (i.e., automated/computing environment), and thus fail to integrate the abstract idea into a practical application.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
18. 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 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.
19. 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 of this title, 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.
20. 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.
21. 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.
22. Claims 1, 4-5, 7-9, 11-14, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramaniyan et al., Pub. No.: US 2025/0316105 A1, [hereinafter Balasubramaniyan], in view of Kalicharan, Pub. No.: US 2009/0172517 A1, [hereinafter Kalicharan].
As per claim 1, Balasubramaniyan teaches a system (paragraph 0008: “At least one aspect of the technical solutions described herein is directed to a system. The system includes one or more processors, coupled with memory.”; paragraph 0044), comprising:
a data extraction server to extract data from documents (paragraph 0044, discussing an example system that facilitates templated document data extraction according to one or more aspects of the technical solutions described. The system includes a data processing system, a database, at least one client device, a server, and a network. The data processing system can include an application, a pre-processor, a query generator, a data extractor, a validator module, a model trainer, or a data repository; paragraph 0045, discussing that the data processing system can include at least one logic device such as a server...; paragraph 0060, discussing that the application can convert a format of the document. The application displays, an output of the data extractor 150 (e.g., the extracted data). The extracted data can be data extracted from the portion of the document; paragraph 0209, discussing that the subject matter described can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or a combination of one or more such back end, middleware, or front end components...); and
a web server in communication with a requesting application of an enterprise, a downstream application of the enterprise, and the data extraction server, the web server to provide a web-based user interface to receive configuration settings, the web server comprising a workflow generator to automatically generate an executable code based on the configurations settings received by the web-based user interface, the executable code (paragraph 0053, discussing that the application is any platform for performing various tasks associated with the organization, such as a software-as-a-service platform (SaaS), web application, web browser, desktop application, among others; paragraph 0057, discussing that the data processing system includes one or more microservices configured to be executed by the one or more processors of the data processing system. Each microservice communicates with the other microservices to perform a function. In some embodiments, each microservice is located on a separate server; paragraph 0086, discussing that an ontology library is any memory, storage, or cache for storing predetermined terms for specific entities within a domain. By templating the output of the second trained machine learning model according to an ontology library, the technical solutions described address the technical problems of error propagation and lack of consistency...By implementing ontology libraries, the data extractor ensures consistency and accuracy of the second trained machine learning model outputs from a plurality of client devices…By utilizing ontological libraries, the data extractor increases the efficiency and accuracy of templated document data extraction; paragraph 0095, discussing an exemplary cloud computing environment…The cloud computing environment includes cloud resources that are made available to client devices via a network, such as the Internet. Cloud resources can be on a single network or a distributed network. Cloud resources can be distributed across multiple cloud computing systems or individual network enabled computing devices. Cloud resources include a variety of hardware or software computing resources, such as servers, databases, storage, networks, applications, and platforms that perform the functions provided including storing code…; paragraph 0179, discussing that the document classifier classifies the document or the portion of the document received from the client device. The document classifier interfaces with the model artifact store 610 to determine a model (e.g., a machine learning model or an attention embedded transformer network model) to use to determine a classification of the document; paragraph 0209, discussing that the subject matter described can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or a combination of one or more such back end, middleware, or front end components...; paragraph 0087) to:
receive, from the requesting application, an image of a document (paragraph 0059, discussing identifying types of documents….In some aspects of the technical solutions described, the document is an image…The application receives an image from a client device; paragraph 0069, discussing that in an illustrative example, the document of the first type is an image of a W-2 form; paragraph 0142, discussing that the data processing system can receive the document in real-time, via a data stream, periodically; paragraph 0126);
classify the document as one of a plurality of document classification types (paragraph 0059, discussing identifying types of documents…In some aspects of the technical solutions described, the first type of the document can be selected from a predetermined list by the client device. In some aspects of the technical solutions described, the document is an image. In some aspects of the technical solutions described, the type of document identified by the application corresponds to a domain of a plurality of domains. For example, the application receives an image from a client device. The application identifies a first type corresponding to the image…The first type can correspond to a domain of a plurality of domains and can be selected from a predetermined list or can be input via the client device. For example, the predetermined list can include: W-2, Form 1040, Schedule A, Schedule B, Schedule C, Schedule D,…, Form 1099, Wage and Tax Statement,…; paragraph 0060, discussing that the application identifies a document of a first type received from a client device; paragraph 0073, discussing that the query generated by query generator can include a classification of the document. The classification can be: of the first type of the document, of a domain of a plurality of domains corresponding to the first type of the document, or a format of the document, among others…; paragraphs 0074, 0146);
transmit the document and document classification type to the data extraction server (paragraph 0080, discussing that the data extractor is any combination of hardware and software for extracting data based on the document being of the first type. For example, the data extractor generates an output of a second trained machine learning model based of the portion of the document input to the trained machine learning model; paragraph 0179, discussing that the document classifier sends the classification to the document extractor. In some embodiments of the technical solutions described, the document classifier sends a query to the document extractor. The query can include a natural language text component, key value pairs, or schema specifying a format of the data extraction, among others; paragraph 0290);
receive, from the data extraction server, extracted data based on the document and document classification type (paragraph 0060, discussing that the application can convert a format of the document. The application displays, an output of the data extractor 150 (e.g., the extracted data). The extracted data can be data extracted from the portion of the document; paragraph 0067, discussing that the data processing system includes a query generator designed, constructed, and operational to generate a query using the portion of the document. The query generator designs the query to facilitate an extraction of data from the portion of the document. Specifically, the query generator designs the query to extract data based on the document being of the first type. The query generator is any combination of hardware or software for generating queries to facilitate data extraction based on a type of a document…; paragraph 0074, discussing that the query facilitates an extraction of data from the document based on the document being of the first type. The query facilitates the extraction of data by specifying a class of data for the second trained machine learning model to extract from the portion of the document…The class of data includes a classification of a type of the document (e.g., a specific document type, such as a W-2, or a Form 1040, among others), a classification of a domain of a plurality of domains corresponding to the document of the first type,…, among others; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document (e.g., a name of an employee or employer on a W-2), or extracted results of the portion of the document; paragraph 0180, discussing that the document classifier sends the classification to the document extractor. In some embodiments of the technical solutions described, the document classifier sends a query to the document extractor. The query can include a natural language text component, key value pairs, or schema specifying a format of the data extraction, among others); and
publish the extracted data to the downstream application (paragraph 0082, discussing that the output can be an extraction of data from the document; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document (e.g., a name of an employee or employer on a W-2), or extracted results of the portion of the document).
Balasubramaniyan does not explicitly teach receive configuration settings from an administrator of the enterprise; and publish the extracted data via a subscription service to the downstream application. However, Kalicharan in the analogous art of document parsing systems teaches these concepts. Kalicharan teaches:
receive configuration settings from an administrator of the enterprise (paragraph 0008, discussing parsing the text document to extract text based on rules entered by the user through a web-based graphical user interface; paragraph 0011, discussing enabling the user to specify rules computer implementable to extract textual information from the electronic document; paragraph 0019, discussing presenting to the user a graphical user interface (GUI) to interact with a server over the Internet using a web browser; paragraph 0021, discussing enabling the user to specify rules computer implementable to extract textual information from the electronic document. Enabling the user typically means that software installed on the server presents a graphical user interface to the user in which the rules for extracting the textual information from the electronic document would be searched for and modified or reused as is, or formulated from scratch; paragraph 0023); and
publish the extracted data via a subscription service to the downstream application (paragraph 0030, discussing that the method includes delivering the extracted textual information to the user. Delivery of the extracted textual information would typically involve the transfer of the electronic file containing the information. All manner of delivery is possible using the system. The extracted textual information may be delivered in any format sought by the user. Examples of such formats are extensible Markup Language (XML), Structured Query Language (SQL) statements for populating any database systems, character delimited files, MICROSOFT ACCESS, MICROSOFT EXCEL, and seamless integration with any remote custom application systems or providing accessibility through remote web service invocation, such as a software system designed to support interoperable Machine to Machine interaction over a network using SOAP (Simple Object Access Protocol) standard; paragraph 0036, discussing that user registration provides a means to identify the user, assign a username and password, log the preferences of the user, for example for delivery of extracted textual information, and to arrange for payment information to be entered by the user; paragraph 0038, discussing enabling processing a text document and delivery of extracted textual information to the user; paragraph 0050).
Balasubramaniyan is directed towards document data extraction. Kalicharan describes a document parsing method and system. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balasubramaniyan with Kalicharan because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying Balasubramaniyan to include Kalicharan’s features for including receiving configuration settings from an administrator of the enterprise, and publishing the extracted data via a subscription service to the downstream application, in the manner claimed, would serve the motivation of permitting greater efficiency gained by storing text extraction rules (Kalicharan at paragraph 0006); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 4, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. Balasubramaniyan further teaches wherein the web server further comprises a memory storing the configuration settings (paragraph 0086, discussing that an ontology library is any memory, storage, or cache for storing predetermined terms for specific entities within a domain. By templating the output of the second trained machine learning model according to an ontology library, the technical solutions described address the technical problems of error propagation and lack of consistency...By implementing ontology libraries, the data extractor ensures consistency and accuracy of the second trained machine learning model outputs from a plurality of client devices…By utilizing ontological libraries, the data extractor increases the efficiency and accuracy of templated document data extraction; paragraph 0179, discussing that the document classifier classifies the document or the portion of the document received from the client device. The document classifier interfaces with the model artifact store 610 to determine a model (e.g., a machine learning model or an attention embedded transformer network model) to use to determine a classification of the document; paragraph 0087), but it does not explicitly teach wherein the configuration settings comprise extraction rules for the document classification type. However, Kalicharan in the analogous art of document parsing systems teaches this concept. Kalicharan teaches wherein the configuration settings comprise extraction rules for the document classification type (paragraph 0008, discussing parsing the text document to extract text based on rules entered by the user through a web-based graphical user interface; paragraph 0011, discussing enabling the user to specify rules computer implementable to extract textual information from the electronic document; paragraph 0021, discussing enabling the user to specify rules computer implementable to extract textual information from the electronic document…; paragraph 0041, discussing creating rules implementable to extract textual information from the electronic document).
Balasubramaniyan is directed towards document data extraction. Kalicharan describes a document parsing method and system. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balasubramaniyan with Kalicharan because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying Balasubramaniyan to include Kalicharan’s feature for including wherein the configuration settings comprise extraction rules for the document classification type, in the manner claimed, would serve the motivation of permitting greater efficiency gained by storing text extraction rules (Kalicharan at paragraph 0006); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. Although not explicitly taught by Balasubramaniyan, Kalicharan in the analogous art of document parsing systems teaches wherein, upon receipt of updated configuration settings from the web-based user interface, the work flow generator is to automatically update the executable code (paragraph 0005, discussing that the invention also enables reuse of extraction logic by duplicating it and making appropriate changes; paragraph 0008, discussing parsing the text document to extract text based on rules entered by the user through a web-based graphical user interface; paragraph 0011, discussing enabling the user to specify rules computer implementable to extract textual information from the electronic document; paragraph 0019, discussing presenting to the user a graphical user interface (GUI) to interact with a server over the Internet using a web browser; paragraph 0021, discussing enabling the user to specify rules computer implementable to extract textual information from the electronic document. Enabling the user typically means that software installed on the server presents a graphical user interface to the user in which the rules for extracting the textual information from the electronic document would be searched for and modified or reused as is, or formulated from scratch; paragraph 0037, discussing a GUI sample capability to receive a sample rule or file from the user for testing to explore system functionality. This capability offers a user the means to test drive the system and the service it provides to see if it matches the user's needs. For maximum user satisfaction, this sample testing capability would typically permit a user to engage all system activities except those involving the actual delivery of the electronic file; paragraph 0043, discussing that a GUI rule-alteration capability to copy and alter a stored rule; paragraph 0044, discussing a rule-testing capability to test, alter and validate the rules to extract the textual information. A user (212) can run the rules on an electronic document to see the results of the rules created, that is, to see the extracted textual information or any information obtained from the extracted textual information. If the rules work for the test document as intended, the user can then apply the rules to that document and others that maybe uploaded. If the rules do not work as intended, then the user can immediately alter the rules and validate the revised rules for use on the electronic document, or create a brand new set of rules).
Balasubramaniyan is directed towards document data extraction. Kalicharan describes a document parsing method and system. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balasubramaniyan with Kalicharan because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying Balasubramaniyan to include Kalicharan’s feature for including wherein, upon receipt of updated configuration settings from the web-based user interface, the work flow generator is to automatically update the executable code, in the manner claimed, would serve the motivation of permitting greater efficiency gained by storing text extraction rules (Kalicharan at paragraph 0006); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 7, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. Balasubramaniyan further teaches wherein the workflow generator is to automatically generate an API endpoint based on the configurations settings to the web-based user interface input, and wherein the executable code comprises executable code for the API endpoint (paragraph 0159, discussing that the system includes a dashboard, a document labeling user interface (UI), a training/retraining pipeline, a model artifact store, an application program interface (API), a user administration database, a model health check module, a model usage/performance database, or a developer client device. The document labeling UI communicates with the API, the user administration database, and the training/retraining pipeline. The training/retraining pipeline communicates with the document labeling UI and the model artifact store. The model artifact store communicates with the training/retraining pipeline and the API. The API communicates with the model artifact store, the document labeling UI, and the model health check module; paragraph 0166, discussing that the API sends data about the models it uses to perform data extraction to the model health check module; paragraph 0175, discussing that the API can be or include the data extractor 150. The API receives the portion (e.g., the labeled of the document) and extracts data from the portion; paragraph 0178, discussing that the API includes endpoints (specific URLs or URIs that represent the locations where API requests can be made, where each endpoint can correspond to a specific function or resource), methods, request and response formats, or authenticators, among others; paragraph 0178, discussing that the API includes endpoints (specific URLs or URIs that represent the locations where API requests can be made, where each endpoint can correspond to a specific function or resource), methods, request and response formats, or authenticators, among others).
As per claim 8, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. Balasubramaniyan further teaches the executable code comprising executable code for a first API endpoint to transmit a confirmation response to the requesting application upon receipt of the document (paragraph 0060, discussing that the application identifies a document of a first type received from a client device. The application establishes a boundary of a portion of the document based on a digital overlay. The application selects a portion of the document based on the boundary. The application creates the digital overlay using: image editing software, or drawing APIs and libraries, among others. It should be understood that this listing of image editing software and drawing APIs is exemplary and is not to be construed as exhaustive or limiting...The application can convert a format of the document. The application displays, an output of the data extractor 150 (e.g., the extracted data). The extracted data can be data extracted from the portion of the document; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document, or extracted results of the portion of the document (e.g., a digital recreation of the portion of the document; paragraph 0159, discussing that the system includes a dashboard, a document labeling user interface (UI), a training/retraining pipeline, a model artifact store, an application program interface (API), a user administration database, a model health check module, a model usage/performance database, or a developer client device. The document labeling UI communicates with the API, the user administration database, and the training/retraining pipeline. The training/retraining pipeline communicates with the document labeling UI and the model artifact store. The model artifact store communicates with the training/retraining pipeline and the API. The API communicates with the model artifact store, the document labeling UI, and the model health check module; paragraph 0178, discussing that the API includes endpoints (specific URLs or URIs that represent the locations where API requests can be made, where each endpoint can correspond to a specific function or resource), methods, request and response formats, or authenticators, among others) and for a second API endpoint to publish the extracted data to the downstream application (paragraph 0082, discussing that the output can be an extraction of data from the document; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document (e.g., a name of an employee or employer on a W-2), or extracted results of the portion of the document; paragraph 0166, discussing that the API sends data about the models it uses to perform data extraction to the model health check module; paragraph 0175, discussing that the API can be or include the data extractor 150. The API receives the portion (e.g., the labeled of the document) and extracts data from the portion; paragraph 0178, discussing that the API includes endpoints (specific URLs or URIs that represent the locations where API requests can be made, where each endpoint can correspond to a specific function or resource), methods, request and response formats, or authenticators, among others; paragraph 0185).
Balasubramaniyan does not explicitly teach publish the extracted data via the subscription service. However, Kalicharan in the analogous art of document parsing systems teaches this concept. Kalicharan teaches:
publish the extracted data via the subscription service (paragraph 0030, discussing that the method includes delivering the extracted textual information to the user. Delivery of the extracted textual information would typically involve the transfer of the electronic file containing the information. All manner of delivery is possible using the system. The extracted textual information may be delivered in any format sought by the user. Examples of such formats are extensible Markup Language (XML), Structured Query Language (SQL) statements for populating any database systems, character delimited files, MICROSOFT ACCESS, MICROSOFT EXCEL, and seamless integration with any remote custom application systems or providing accessibility through remote web service invocation, such as a software system designed to support interoperable Machine to Machine interaction over a network using SOAP (Simple Object Access Protocol) standard; paragraph 0036, discussing that user registration provides a means to identify the user, assign a username and password, log the preferences of the user, for example for delivery of extracted textual information, and to arrange for payment information to be entered by the user; paragraph 0038, discussing enabling processing a text document and delivery of extracted textual information to the user; paragraph 0050).
Balasubramaniyan is directed towards document data extraction. Kalicharan describes a document parsing method and system. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balasubramaniyan with Kalicharan because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying Balasubramaniyan to include Kalicharan’s feature for including publishing the extracted data via the subscription service, in the manner claimed, would serve the motivation of permitting greater efficiency gained by storing text extraction rules (Kalicharan at paragraph 0006); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. Balasubramaniyan further teaches wherein the requesting application comprises a document upload tool to upload the image of the document and metadata associated with the document (paragraph 0160, discussing that the document labeling UI can be accessed via a client device...The user of the client device uploads a document through the document labeling UI. The document labeling UI allows the user of the client device to label, annotate, or create borders on the document. The document labeling UI creates a border around a portion of the document determined by the user of the client device by creating a digital overlay around the portion. The document labeling UI preserves the underlying coordinates of the document when creating the digital overlay...The document labeling UI determines a first type of the document or the portion of the document. The document labeling UI determines the type according to a predetermined list or according to an input from a user of a client device; paragraph 0172, discussing that the document labeling UI can be part of the application. The document labeling UI enables the client device to select and upload documents for data extraction. A user of the client device selects a type of document they wish to label/extract data from a predetermined list, or inputs the type of document; paragraph 0064, discussing that the one or more labels can be one or more types of information contained in the document or the portion; paragraph 0182).
As per claim 11, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 9. While Balasubramaniyan describes receiving a document in real-time (paragraph 0142), Balasubramaniyan does not explicitly teach wherein the extracted data is published to the downstream application in real time. However, Kalicharan in the analogous art of document parsing systems teaches this concept. Kalicharan teaches wherein the extracted data is published to the downstream application in real time (paragraph 0049, discussing that the fifth GUI capability is to deliver the extracted textual information to the user. Typically, the software would permit immediate [i.e. real-time] electronic delivery of the extracted textual information to the user).
Balasubramaniyan is directed towards document data extraction. Kalicharan describes a document parsing method and system. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balasubramaniyan with Kalicharan because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying Balasubramaniyan to include Kalicharan’s feature for including wherein the extracted data is published to the downstream application in real time, in the manner claimed, would serve the motivation of permitting greater efficiency gained by storing text extraction rules (Kalicharan at paragraph 0006); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 12, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. Balasubramaniyan further teaches wherein the web server is to execute the executable code to extract data from the document based on the configuration settings (paragraph 0060, discussing that the application can convert a format of the document. The application displays, an output of the data extractor 150 (e.g., the extracted data). The extracted data can be data extracted from the portion of the document; paragraph 0074, discussing that the query facilitates an extraction of data from the document based on the document being of the first type. The query facilitates the extraction of data by specifying a class of data for the second trained machine learning model to extract from the portion of the document…The class of data includes a classification of a type of the document (e.g., a specific document type, such as a W-2, or a Form 1040, among others), a classification of a domain of a plurality of domains corresponding to the document of the first type,…, among others; paragraph 0080, discussing that the data extractor is any combination of hardware and software for extracting data based on the document being of the first type; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document (e.g., a name of an employee or employer on a W-2), or extracted results of the portion of the document; paragraph 0053, discussing that the application is any platform for performing various tasks associated with the organization, such as a software-as-a-service platform (SaaS), web application, web browser, desktop application, among others; paragraphs 0067, 0179, 0180).
Claim 13 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 13, the Balasubramaniyan-Kalicharan combination teaches a method, comprising: receiving, from a web-based user interface of a web server, configuration settings comprising a plurality of document classification types and a predetermined confidence threshold for each document classification type (Balasubramaniyan, paragraph 0086, discussing storing predetermined terms for specific entities within a domain. By templating the output of the second trained machine learning model according to an ontology library, the technical solutions described address the technical problems of error propagation and lack of consistency...By implementing ontology libraries, the data extractor ensures consistency and accuracy of the second trained machine learning model outputs from a plurality of client devices…By utilizing ontological libraries, the data extractor increases the efficiency and accuracy of templated document data extraction; paragraph 0090, discussing that the validator module determines that the validation score of the extracted data is above a threshold. The threshold can be predetermined or input by a user of a client device. In response to the validator module determining the validation score is above the threshold, the validator module cause the application to displays the extracted data; paragraph 0153, discussing that the data processing system compares the validation score to a threshold. The threshold can be set by a user of a client device or can be predetermined. The threshold can be determined according to the document of the first type, the portion of the document, a domain associated with the document; paragraph 0179, discussing that the document classifier classifies the document or the portion of the document received from the client device. The document classifier interfaces with the model artifact store 610 to determine a model (e.g., a machine learning model or an attention embedded transformer network model) to use to determine a classification of the document; paragraph 0209, discussing that the subject matter described can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or a combination of one or more such back end, middleware, or front end components...; paragraph 0053, discussing that the application is any platform for performing various tasks associated with the organization, such as a software-as-a-service platform (SaaS), web application, web browser, desktop application, among others).
As per claim 14, the Balasubramaniyan-Kalicharan combination teaches the method of Claim 13. Balasubramanayinan further teaches wherein automatically generating an executable code based on the configuration settings further comprises automatically generating a plurality of API endpoints for the workflow (paragraph 0159, discussing that the system includes a dashboard, a document labeling user interface (UI), a training/retraining pipeline, a model artifact store, an application program interface (API), a user administration database, a model health check module, a model usage/performance database, or a developer client device. The document labeling UI communicates with the API, the user administration database, and the training/retraining pipeline. The training/retraining pipeline communicates with the document labeling UI and the model artifact store. The model artifact store communicates with the training/retraining pipeline and the API. The API communicates with the model artifact store, the document labeling UI, and the model health check module; paragraph 0166, discussing that the API sends data about the models it uses to perform data extraction to the model health check module; paragraph 0175, discussing that the API can be or include the data extractor 150. The API receives the portion (e.g., the labeled of the document) and extracts data from the portion; paragraph 0178, discussing that the API includes endpoints (specific URLs or URIs that represent the locations where API requests can be made, where each endpoint can correspond to a specific function or resource), methods, request and response formats, or authenticators, among others; paragraph 0178, discussing that the API includes endpoints (specific URLs or URIs that represent the locations where API requests can be made, where each endpoint can correspond to a specific function or resource), methods, request and response formats, or authenticators, among others).
Claim 16 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 6, as discussed above.
As per claim 17, the Balasubramaniyan-Kalicharan combination teaches the method of Claim 16. Balasubramaniyan further teaches further comprising receiving, via the web-based user interface, a user validation of extracted data (paragraph 0173, discussing that the feedback model allows a user of a client device to validate results of the data extraction).
As per claim 19, the Balasubramaniyan-Kalicharan combination teaches the method of Claim 13. Balasubramaniyan further teaches further comprising processing, by the web server, the extracted data to conform to the downstream application (paragraph 0041, discussing a system, method, and computer readable medium to automatically template document data extraction for a plurality of document types in a plurality of domains using standardized ontologies; paragraph 0140, discussing that the technical solutions described are able to provide robust, standardized data extraction by specifying a standardized schema for outputs of the trained attention embedded transformer network model. By incorporating the use of ontological libraries, the technical solutions described provide technical advantages of not only being able to accurately extract data across a variety of domains, but also standardizing the output of the data extraction; paragraph 0175, discussing that the API templates the data extraction into a standardized format; paragraphs 0007, 0149, 0190).
As per claim 20, the Balasubramaniyan-Kalicharan combination teaches the method of Claim 13. Balasubramaniyan further teaches wherein the workflow generator of the web server is to automatically generate the executable code to visually identify data from the image of the document based on positioning of the data relative to an anchor location (paragraph 0004, discussing that the technical solutions described determine a portion of the document based on a boundary. The boundary can be established by a digital overlay; paragraph 0008, discussing that the one or more processors establish a boundary of a portion of the document based on a digital overlay. The one or more processors select a portion of the document based on the boundary; paragraph 0043, discussing that using a portion of the document based on a boundary established by a digital overlay, the technical solutions generate a query. The query is designed to facilitate an extraction of data relating to the first type when input by the technical solutions described…The technical solutions described generate an output including at least the extraction of data relating to the first type. The technical solutions described template the extracted data (e.g., the output) using an ontological library corresponding to the domain. The technical solutions described display the extracted data. By identifying a type of the document, determining a domain of the document corresponding to the first type, generating a query using a portion of the document based on a boundary established via a digital overlay, generating an output from the query, the technical solutions described can extract data from documents of a variety of domains and provide standardized outputs; paragraph 0055, discussing that the application establishes a boundary of a portion of the document based on a digital overlay. The pre-processor augments the portion of the document; paragraph 0069, discussing that in an illustrative example, the document of the first type is an image of a W-2 form. The application 145 determines a portion of the image based on a boundary established by a digital overlay. The pre-processor augments the image by changing a qualitative aspect of the image. For an illustrative example, the augmentation performed by the pre-processor is a file type conversion, wherein the data extractor determines that a first file type of the image is a JPEG file and converts the first file type to a second file type, wherein the second file type is an XML file…; paragraph 0088, discussing that the application determines, responsive to the display of the extracted data, a second portion of the document based on a second boundary established by a second digital overlay; paragraph 0143, discs using that the data processing system determines a portion of the document based on a boundary established by a digital overlay. The client device can create and place the digital overlay through a variety of methods, such as, for example, image editing software, graphical user interface-based image processing tools, or built-in editing features in mobile application graphical user interfaces, among others. The client device can define the portion encompassed by the digital overlay by creating a mask. The client device can create the digital overlay using overlay generation, wherein an overlay image is generated to represent the digital overlay…; paragraphs 0060, 0070, 0154).
23. Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramaniyan in view of Kalicharan, in further view of Panda et al., Pub. No.: US 2025/0014374 A1, [hereinafter Panda].
As per claim 2, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. While Balasubramaniyan teaches wherein the executable code comprises code (paragraph 0204, discussing that the computer-readable programs can be implemented in any programming language, such as LISP, PERL, C, C++, C#, PROLOG, or in any byte code language such as JAVA. The software programs or executable instructions can be stored on or in one or more articles of manufacture as object code; paragraph 0207, discussing that a computer program (also known as a program, software, software application, app, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment), the Balasubramaniyan-Kalicharan combination does not explicitly teach wherein the executable code comprises containerized code. However, Panda in the analogous art of information extraction systems teaches this concept. Panda teaches wherein the executable code comprises containerized code (paragraph 0008, discussing that the disclosure relates to extracting information; paragraph 0235, discussing that respective containers (N) contained in the VMs may be configured to run the code; paragraph 0244, discussing that the customer can use the containers to call cloud services. In this example, the customer may run code in the containers that requests a service from cloud services. The containers can transmit this request to the secondary VNICs that can transmit the request to the NAT gateway that can transmit the request to public Internet…).
The Balasubramaniyan-Kalicharan combination describes features related to document processing and data extraction. Panda describes a system for information extraction. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. 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 Balasubramaniyan-Kalicharan combination with Panda because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying the Balasubramaniyan-Kalicharan combination to include Panda’s feature for including wherein the executable code comprises containerized code, in the manner claimed, would serve the motivation of accurately extracting information (Panda at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. While Balasubramaniyan teaches JavaScript Object Notation (JSON) (paragraph 0052),
the Balasubramaniyan-Kalicharan combination does not explicitly teach wherein the executable code is written in a language selected from a group consisting of Python and JavaScript. However, Panda in the analogous art of information extraction systems teaches this concept. Panda teaches wherein the executable code is written in a language selected from a group consisting of Python and JavaScript (paragraph 0179, discussing that a second example of soft text augmentation uses identifies a similar word by identifying a synonym of the word…The text augmentation content generator identifies an element from the second document (which is a copy of the first document). The text augmentation content generator determines a synonym of the element identified. In certain embodiments, the text augmentation content generator may include or have remote access to a software module including a thesaurus or synonym generator. For example, a software library may provide this functionality, or it may be attained using an application programming interface (“API”) provided by a third-party. For instance, a synonym may be obtained of the word “invoice” using a method call provided by a thesaurus implemented in an object-oriented programming language like Java, C++, C#, JavaScript, or Python).
The Balasubramaniyan-Kalicharan combination describes features related to document processing and data extraction. Panda describes a system for information extraction. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. 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 Balasubramaniyan-Kalicharan combination with Panda because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying the Balasubramaniyan-Kalicharan combination to include Panda’s feature for including wherein the executable code is written in a language selected from a group consisting of Python and JavaScript, in the manner claimed, would serve the motivation of accurately extracting information (Panda at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
24. Claims 6, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramaniyan in view of Kalicharan, in further view of Gangadhar et al., Pub. No.: US 2024/0241898 A1, [hereinafter Gangadhar].
As per claim 6, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 1. Balasubramaniyan further teaches wherein the configuration settings comprise a predetermined confidence threshold for each document classification type (paragraph 0090, discussing that the validator module determines that the validation score of the extracted data is above a threshold. The threshold can be predetermined or input by a user of a client device. In response to the validator module determining the validation score is above the threshold, the validator module cause the application to displays the extracted data; paragraph 0153, discussing that the data processing system compares the validation score to a threshold. The threshold can be set by a user of a client device or can be predetermined. The threshold can be determined according to the document of the first type, the portion of the document, a domain associated with the document…), and
wherein the executable code is to: compare a threshold of confidence for the classification of the document to the predetermined confidence threshold for the document classification type transmitted to the data extraction server (paragraph 0091, discussing that the validator module determines a validation score for the output. The validator module determines that the validation score is below a threshold. In response to this determination, the validator module causes the data extractor to generate a new output. The validator module determines a new validation score for the new output. The validator module determines that the new validation score is above the threshold. In response to this determination, the validator module replaces the output with the new output; paragraph 0094, discussing that the validator module determines a first validation score via the trained machine learning model by providing the output to the trained machine learning model as a first input to create a second output. The validator module determines a second validation score via the second trained machine learning model by providing the output as an input to the second trained machine learning model as a second input to create a third output. The validator module compares the first validation score with the second validation score to determine that both the first validation score and the second validation score are above the threshold; paragraph 0132, discussing that this comparison between the known outcome and the model-generated outcome can be repeated for various inputs of a model to generate an overall error score or rate. The error score or rate relates to the validity of the model. If the error score or rate for the model exceeds a threshold error, the model is considered to be invalid or erroneous. If the error score or rate for the model is at or below the threshold error, the model is considered valid. In this manner, each model is validated; paragraph 0133);
transmit a human-in-the-loop request to the web-based user interface based on the threshold of confidence subceeding the predetermined threshold of confidence (paragraph 0154, discussing a determination by the data processing system that the validation score is not above the threshold; paragraph 0167, discussing that the model health check module notifies the developer client device when the performance of a model falls below a threshold. Upon receiving this notification, a user of the developer client device intervenes to further analyze the health of the model); and
receive, via the web-based user interface, a user validation (paragraph 0173, discussing that the feedback model allows a user of a client device to validate results of the data extraction).
The Balasubramaniyan-Kalicharan combination does not explicitly teach that the user validation is a validation of the classification. However, Gangadhar in the analogous art of document classification systems teaches this concept. Gangadhar teaches:
receive, via the web-based user interface, a user validation of the classification (paragraph 0010, discussing a computer-implemented method for classifying documents; paragraph 0049, discussing that the user validates that the document classification by the machine learning document classification model is correct or makes a correction when the document classification is incorrect).
The Balasubramaniyan-Kalicharan combination describes features related to document processing and data extraction. Gangadhar describes a method and system for document classification. Therefore, they are deemed to be analogous as they both are directed towards document processing systems. 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 Balasubramaniyan-Kalicharan combination with Gangadhar because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying the Balasubramaniyan-Kalicharan combination to include Gangadhar’s feature for including receive, via the web-based user interface, a user validation of the classification, in the manner claimed, would serve the motivation of increasing document classification accuracy (Gangadhar at paragraph 0043); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 15, the Balasubramaniyan-Kalicharan combination teaches the method of Claim 13. Balasubramaniyan further teaches further comprising: executing, by the web server, the executable code for the workflow to extract data from the document received from the requesting application (paragraph 0060, discussing that the application can convert a format of the document. The application displays, an output of the data extractor 150 (e.g., the extracted data). The extracted data can be data extracted from the portion of the document; paragraph 0067, discussing that the data processing system includes a query generator designed, constructed, and operational to generate a query using the portion of the document. The query generator designs the query to facilitate an extraction of data from the portion of the document. Specifically, the query generator designs the query to extract data based on the document being of the first type. The query generator is any combination of hardware or software for generating queries to facilitate data extraction based on a type of a document…; paragraph 0074, discussing that the query facilitates an extraction of data from the document based on the document being of the first type. The query facilitates the extraction of data by specifying a class of data for the second trained machine learning model to extract from the portion of the document…The class of data includes a classification of a type of the document (e.g., a specific document type, such as a W-2, or a Form 1040, among others), a classification of a domain of a plurality of domains corresponding to the document of the first type,…, among others; paragraph 0145, discussing that the data processing system can structure the text component using natural language text, key-value pairs, markdown or rich text, code snippets, or user input tags, among others; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document (e.g., a name of an employee or employer on a W-2), or extracted results of the portion of the document; paragraph 0180, discussing that the document classifier sends the classification to the document extractor. In some embodiments of the technical solutions described, the document classifier sends a query to the document extractor. The query can include a natural language text component, key value pairs, or schema specifying a format of the data extraction, among others); and
receiving, via the web-based user interface, feedback (paragraph 0173, discussing that the feedback model allows a user of a client device to validate results of the data extraction).
Balasubramaniyan does not explicitly teach that the feedback is real-time feedback selected from a group consisting of acceptance feedback and rejection feedback. Kalicharan in the analogous art of document parsing systems teaches:
receiving, via the web-based user interface, real-time feedback (paragraph 0044, discussing a rule-testing capability to test, alter and validate the rules to extract the textual information. A user can run the rules on an electronic document to see the results of the rules created, that is, to see the extracted textual information or any information obtained from the extracted textual information. If the rules work for the test document as intended, the user can then apply the rules to that document and others that maybe uploaded. If the rules do not work as intended, then the user can immediately alter the rules and validate the revised rules for use on the electronic document, or create a brand new set of rules; paragraph 0049).
Balasubramaniyan is directed towards document data extraction. Kalicharan describes a document parsing method and system. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balasubramaniyan with Kalicharan because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying Balasubramaniyan to include Kalicharan’s feature for including receiving, via the web-based user interface, real-time feedback, in the manner claimed, would serve the motivation of permitting greater efficiency gained by storing text extraction rules (Kalicharan at paragraph 0006); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Balasubramaniyan-Kalicharan does not explicitly teach that the feedback is feedback selected from a group consisting of acceptance feedback and rejection feedback. However, Gangadhar in the analogous art of document classification systems teaches this concept. Gangadhar teaches:
receiving, via the web-based user interface, feedback selected from a group consisting of acceptance feedback and rejection feedback (paragraph 0010, discussing a computer-implemented method for classifying documents; paragraph 0012, discussing that illustrative embodiments retrain the machine learning document classification model utilizing user feedback regarding the classification of the document; paragraph 0047, discussing retraining the machine learning document classification model using up-to-date or current document classification statistics and user feedback regarding correct or incorrect document classifications; paragraph 0049, discussing that the user validates that the document classification by the machine learning document classification model is correct or makes a correction when the document classification is incorrect. The user provides the document classification validation or the document classification correction to illustrative embodiments as user feedback; paragraph 0062).
The Balasubramaniyan-Kalicharan combination describes features related to document processing and data extraction. Gangadhar describes a method and system for document classification. Therefore, they are deemed to be analogous as they both are directed towards document processing systems. 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 Balasubramaniyan-Kalicharan combination with Gangadhar because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying the Balasubramaniyan-Kalicharan combination to include Gangadhar’s feature for including receiving, via the web-based user interface, feedback selected from a group consisting of acceptance feedback and rejection feedback, in the manner claimed, would serve the motivation of increasing document classification accuracy (Gangadhar at paragraph 0043); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 18, the Balasubramaniyan-Kalicharan combination teaches the method of Claim 17. Balasubramaniyan further teaches further comprising, receiving, via the web-based user interface, a user validation (paragraph 0173, discussing that the feedback model allows a user of a client device to validate results of the data extraction).
The Balasubramaniyan-Kalicharan combination does not explicitly teach that the user validation is a validation of the document classification. However, Gangadhar in the analogous art of document classification systems teaches this concept. Gangadhar teaches:
receiving, via the web-based user interface, a user validation of the document classification (paragraph 0010, discussing a computer-implemented method for classifying documents; paragraph 0049, discussing that the user validates that the document classification by the machine learning document classification model is correct or makes a correction when the document classification is incorrect).
The Balasubramaniyan-Kalicharan combination describes features related to document processing and data extraction. Gangadhar describes a method and system for document classification. Therefore, they are deemed to be analogous as they both are directed towards document processing systems. 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 Balasubramaniyan-Kalicharan combination with Gangadhar because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying the Balasubramaniyan-Kalicharan combination to include Gangadhar’s feature for including receiving, via the web-based user interface, a user validation of the document classification, in the manner claimed, would serve the motivation of increasing document classification accuracy (Gangadhar at paragraph 0043); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
25. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Balasubramaniyan in view of Kalicharan, in further view of Bleiweiss et al., Pub. No.: US 2021/0065320 A1, [hereinafter Bleiweiss].
As per claim 10, the Balasubramaniyan-Kalicharan combination teaches the system of Claim 9. Balasubramaniyan further teaches wherein the document upload tool comprises a file upload tool (paragraph 0160, discussing that the document labeling UI can be accessed via a client device...The user of the client device uploads a document through the document labeling UI. The document labeling UI allows the user of the client device to label, annotate, or create borders on the document. The document labeling UI creates a border around a portion of the document determined by the user of the client device by creating a digital overlay around the portion. The document labeling UI preserves the underlying coordinates of the document when creating the digital overlay...The document labeling UI determines a first type of the document or the portion of the document. The document labeling UI determines the type according to a predetermined list or according to an input from a user of a client device; paragraph 0172, discussing that the document labeling UI can be part of the application. The document labeling UI enables the client device to select and upload documents for data extraction. A user of the client device selects a type of document they wish to label/extract data from a predetermined list, or inputs the type of document), and
wherein, for each document uploaded via the file upload tool, the executable code is to: classify the document as one of N different document classification types, where N is an integer greater than 1 (paragraph 0059, discussing identifying types of documents…In some aspects of the technical solutions described, the first type of the document can be selected from a predetermined list by the client device. In some aspects of the technical solutions described, the document is an image. In some aspects of the technical solutions described, the type of document identified by the application corresponds to a domain of a plurality of domains. For example, the application receives an image from a client device. The application identifies a first type corresponding to the image…The first type can correspond to a domain of a plurality of domains and can be selected from a predetermined list or can be input via the client device. For example, the predetermined list can include: W-2, Form 1040, Schedule A, Schedule B, Schedule C, Schedule D,…, Form 1099, Wage and Tax Statement,…; paragraph 0060, discussing that the application identifies a document of a first type received from a client device; paragraph 0073, discussing that the query generated by query generator can include a classification of the document. The classification can be: of the first type of the document, of a domain of a plurality of domains corresponding to the first type of the document, or a format of the document, among others…; paragraphs 0074, 0146);
transmit the image of the document and document classification type to the data extraction server (paragraph 0059, discussing identifying types of documents….In some aspects of the technical solutions described, the document is an image…The application receives an image from a client device; paragraph 0069, discussing that in an illustrative example, the document of the first type is an image of a W-2 form; paragraph 0142, discussing that the data processing system can receive the document in real-time, via a data stream, periodically);
receive, from the data extraction server, extracted data based on the document (paragraph 0060, discussing that the application can convert a format of the document. The application displays, an output of the data extractor 150 (e.g., the extracted data). The extracted data can be data extracted from the portion of the document; paragraph 0067, discussing that the data processing system includes a query generator designed, constructed, and operational to generate a query using the portion of the document. The query generator designs the query to facilitate an extraction of data from the portion of the document. Specifically, the query generator designs the query to extract data based on the document being of the first type. The query generator is any combination of hardware or software for generating queries to facilitate data extraction based on a type of a document…; paragraph 0074, discussing that the query facilitates an extraction of data from the document based on the document being of the first type. The query facilitates the extraction of data by specifying a class of data for the second trained machine learning model to extract from the portion of the document…The class of data includes a classification of a type of the document (e.g., a specific document type, such as a W-2, or a Form 1040, among others), a classification of a domain of a plurality of domains corresponding to the document of the first type,…, among others; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document (e.g., a name of an employee or employer on a W-2), or extracted results of the portion of the document; paragraph 0180, discussing that the document classifier sends the classification to the document extractor. In some embodiments of the technical solutions described, the document classifier sends a query to the document extractor. The query can include a natural language text component, key value pairs, or schema specifying a format of the data extraction, among others); and
publish the extracted data to the downstream application paragraph 0082, discussing that the output can be an extraction of data from the document; paragraph 0147, discussing that the output includes at least an extraction of data relating to the first type (e.g., the first type of the document). For example, where the document of the first type is a W-2, the extraction of data relating to the first type generated by the second trained machine learning model can be a classification of the type of document (e.g., a classification confirming the first type of document), an extraction of an entity relating to the document (e.g., a name of an employee or employer on a W-2), or extracted results of the portion of the document).
Balasubramaniyan does not explicitly teach wherein the document upload tool comprises a bulk file upload tool, wherein the workflow generator comprises a task queue, and each document uploaded via the bulk file upload tool; and publish the extracted data via the subscription service. Kalicharan in the analogous art of document parsing systems teaches:
publish the extracted data via the subscription service (paragraph 0030, discussing that the method includes delivering the extracted textual information to the user. Delivery of the extracted textual information would typically involve the transfer of the electronic file containing the information. All manner of delivery is possible using the system. The extracted textual information may be delivered in any format sought by the user. Examples of such formats are extensible Markup Language (XML), Structured Query Language (SQL) statements for populating any database systems, character delimited files, MICROSOFT ACCESS, MICROSOFT EXCEL, and seamless integration with any remote custom application systems or providing accessibility through remote web service invocation, such as a software system designed to support interoperable Machine to Machine interaction over a network using SOAP (Simple Object Access Protocol) standard; paragraph 0036, discussing that user registration provides a means to identify the user, assign a username and password, log the preferences of the user, for example for delivery of extracted textual information, and to arrange for payment information to be entered by the user; paragraph 0038, discussing enabling processing a text document and delivery of extracted textual information to the user; paragraph 0050).
Balasubramaniyan is directed towards document data extraction. Kalicharan describes a document parsing method and system. Therefore, they are deemed to be analogous as they both are directed towards data extraction systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Balasubramaniyan with Kalicharan because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying Balasubramaniyan to include Kalicharan’s feature for including publish the extracted data via the subscription service, in the manner claimed, would serve the motivation of permitting greater efficiency gained by storing text extraction rules (Kalicharan at paragraph 0006); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Balasubramaniyan-Kalicharan combination does not explicitly teach wherein the document upload tool comprises a bulk file upload tool, wherein the workflow generator comprises a task queue, and each document uploaded via the bulk file upload tool. However, Bleiweiss in the analogous art of document processing systems teaches these concepts. Bleiweiss teaches:
wherein the document upload tool comprises a bulk file upload tool (paragraph 0049, discussing that the add document modal lets the user upload a document to the Master File using one of several file upload methods (e.g., by dragging the document into the modal 302, using the browser to select the file by clicking “upload”, or by bulk uploading multiple documents, etc.); paragraph 0059, discussing that the user can also bulk upload documents together as a Family), wherein the workflow generator comprises a task queue (paragraph 0185, discussing that the system also features a workspace between the review tool and the Evidence tab called the Queue. The Queue adds an intermediary layer to the review process. Users can elevate documents to the Queue for a second-level review to determine if the document should be elevated into Evidence or returned to the review tool. Users can set it up so several users review the same document in the Queue and vote on its destination; paragraph 0198, discussing that as an example, suppose a team loads a million documents produced by the other side into the review tool…The system builds up its intelligence and adds more documents to the Queue and adjusts the confidence level of documents already in the Queue), and
each document uploaded via the bulk file upload tool (paragraph 0049, discussing that the add document modal lets the user upload a document to the Master File using one of several file upload methods (e.g., by dragging the document into the modal 302, using the browser to select the file by clicking “upload”, or by bulk uploading multiple documents, etc.); paragraph 0059, discussing that the user can also bulk upload documents together as a Family).
The Balasubramaniyan-Kalicharan combination describes features related to document processing and data extraction. Bleiweiss describes a system for document processing. Therefore, they are deemed to be analogous as they both are directed towards document processing systems. 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 Balasubramaniyan-Kalicharan combination with Bleiweiss because the references are analogous art because they are both directed to solutions for data extraction, which falls within applicant’s field of endeavor (document processing and data extraction systems), and because modifying the Balasubramaniyan-Kalicharan combination to include Bleiweiss’ features for including wherein the document upload tool comprises a bulk file upload tool, wherein the workflow generator comprises a task queue, and each document uploaded via the bulk file upload tool, in the manner claimed, would serve the motivation of effectively managing, organizing, and having rapid on-demand access to every individual document (Bleiweiss at paragraph 0003); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Singh et al., Pub. No.: US 2011/0249905 A1 - describes systems and methods for automatically extracting data from electronic documents including tables.
Mi et al., Pub. No.: US 2024/0078239 A1 – describes a system and method of generating data for populating or updating accounting databases based on digitized accounting source documents.
Bergeron et al., Pub. No.: US 2012/0265759 A1 – describes a computer-implemented method for storing configuration data for electronic documents having different native file formats.
Welling et al., Pub. No.: US 2011/0258195 A1 - describes that inspection workers must recognize documents, find relevant information on the documents and insure that the data has been accurately extracted and appropriately entered in particular software programs. Typically, any changes made by inspection workers must be reviewed and approved by other, more senior, inspection workers before replacing the data extracted by optical character recognition.
Peng et al., Pub. No.: US 2025/0349141 A1 – describes a neural network analysis trained on historical data to verify/validate a correctness of the document classification.
Magatti, Davide, Fabio Stella, and Marco Faini. "A software system for topic extraction and document classification." 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. Vol. 1. IEEE, 2009 – describes a software system for topic extraction and automatic document classification.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
Prosecution Insights