DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Final Office Action in response to communications received 12/12/2025. Claim(s) 4 and 12 have been canceled. Claims 1-2, 5-6, 9-10, 13-14, 16-17 and 20 have been amended. No new claims have been added. Therefore, claims 1-3, 5-11 and 13-20 are pending and addressed below.
Priority
Application No. 18,609,685 filing date: 03/19/2024
Applicant Name/Assignee: Wolters Kluwer Financial Services, Inc.
Inventor(s): Mittal, Abhishek; Arora, Rajiv; Chourasia, Saheb; Agarwal, Mukesh; Gupta, Amit; Pimprikar, Rohan Girishchandra; Wadhwa, Piyush; Mahapatra, Ashtik; Singh, Ashuvendra Pratap
Response to Arguments/Amendments
Claim Rejections - 35 USC § 101
Applicant's arguments filed 12/12/2025 have been fully considered but they are not persuasive.
In the remarks applicant points to Ex Parte Desjardins and the USPTO requirements of consideration. Applicant argues that the amended claim limitations cannot reasonably be implemented using mental processes. The examiner agrees that the current limitations which include “train…learning model using …dataset”, “generate a plurality of tags…”, “generate structured data …” are not limitations in the mental process category. The examiner withdraws the abstract category of mental concepts in the step 2A prong 1 rejection.
In the remarks applicant points to the USPTO Aug. 04, 2025 memo of important considerations which include determining whether a claim improves technology or technical field or covers a particular solution in a particular way to a problem to achieve a desired outcome. Applicant argue the amended limitations recite an improvement to a technical field because it covers a way to generate insight data based on machine learning, a plurality of tags for domain-specific documents based on taxonomies where the tags indicate insight data unextractable from domain-specific documents and generated based on a score representing probability of whether insight criterion associated with specific domain is met. Applicant recites the specification “many insight data like whether a lien is a blanket lien or not, cannot be directly extracted from a lien document AI models are trained to generate a score evaluating a probability whether a lien is blanket, based on and textual and contextual information of the lien is tagged as blanket when the score is larger than a predetermined threshold document the AI models are trained with training data including qualifying phrases indicating a lien is blanket and negative phrases indicating a lien is not blanket." (¶ 0084) concluding the limitations integrate any alleged abstract idea into a practical application. Applicant’s argument is not persuasive. Improvements to an abstract idea is not an improvement to technology. Insight data refers to information derived from analyzing raw data to uncover hidden patterns, trends and as in the instant case of the current application, understanding customer behavior with respect to liens. Domain specific documents according to its ordinary meaning in the art are documents tailored to a specific area of interest, or focused information that serve a specific purpose withing a particular domain (e.g. legal, finance, property) providing detailed information on specific topics or issues. The term “taxonomies” refers to class or classification in light of the specification. Accordingly, the claimed “generate …tags for …domain-specific document …based on taxonomies is merely assigning information related to the document type, classification of category. The “train…learning model using training dataset” does not focus on the technical process for training the model instead is directed toward the data acted upon at a high level lacking any technical disclosure (see Recentive Analytics, Inc v Fox Corp). Accordingly the model claimed is merely being applied as instructions to analyze, organize and tag data according to domain-specific categories. The limitations are not directed toward improvement to any of the underlying technology (e.g. system, non-transitory medium, processor or machine learning) or to provide a solution specific to any of the claimed technologies. The specification makes clear that the focus of the invention is to generate and present structured insight data in financial due diligence (spec ¶ 0001, 0003). The claimed technology merely is applied to automate the analysis, organization and classification of data from documents for use in lending scenarios. Applying high level generic technology to receive, organize and analyze data for financial applications does not qualify under step 2A prong 2. The rejection is maintained.
Claim Rejections - 35 USC § 102
Applicant’s arguments, filed 12/12/2025, with respect to 102 rejection of Claim(s) 1-2 and 6-8; Claim(s) 9-10 and 14-16 and Claim(s) 17 and 20 have been fully considered and are persuasive. The 102 rejection of Claim(s) 1-2 and 6-8; Claim(s) 9-10 and 14-16 and Claim(s) 17 and 20 has been withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to Claim(s) 1-2 and 6-8; Claim(s) 9-10 and 14-16 and Claim(s) 17 and 20 have been considered but are moot because the new ground of rejection applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-11 and 13-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claim(s) 1-3 and 5-8:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 1 and the dependent claims. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. The processor of system claim 1 recites the operations (1) receive …request (2) search database to identify document (3) extract data from document (4) train machine learning model using training dataset (5) provide textual data as inputs (6) generate plurality of tags based on taxonomies/categories and score representing probability of whether insight criterion associated with domain-specific document is met (7) generate insight data based on data categorization using tags (8) transmit instruction to display results. The claimed limitations which under its broadest reasonable interpretation, covers performance analyzing and organizing data for commercial activities and human behavior. When considered as a whole the claimed subject matter is directed toward receiving, searching databases to identify specific documents, extract data identifying from the document input for analysis and analyze the data to generate data structured/organized based on taxonomies/categories for output. The specification discloses that the focus of the invention is to parse, organize and categorize data for accurate asset evaluation for use by financial institutions (¶ 0003). Specifically a process to perform a search for a legal document in a database, extract data from the document and apply probability scores in order to categorized and tagged to generate “insight data” based on taxonomies/categories and output the insight data (¶ 0004). Such concepts can be found in the abstract category of commercial interactions. These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims recite additional elements which include a system comprising instructions stored in a memory coupled and read by a processor and the use of a NLP and learning model. The claimed system and its components are recited at a high level of generality and merely automates functions that have been identified as abstract above (mental processes and methods of organizing human activity), therefore acting as a generic computer to perform the abstract idea. Taking the claim elements separately, the operation performed by the system at each step of the process is purely in terms of results desired and devoid of implementation of details.
The additional limitation system “processor” is applied to perform the operation “receive a search request”, “provide textual data and metadata inputs” to the model, “transmit…instructions …to display …data”. The additional element “natural language processing model” applied to extract data. According to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) the courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity.
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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
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
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)
The applying of the NLP model as a tool to extract data from the document does not remove the limitation from insignificant extra activity of data identification and extraction (see Content Extraction). The claim limitations (receive, extract, provide and transmit) are recited at a high level of generality without details of technical implementation and thus are insignificant extra solution activity.
The additional element system “processor” applied to perform the operation “search …database…to identify …domain-specific document”. The limitation search database to identify document” is little different from the basic concept of an individual searching a database/library for a document and picking out the data within the document to use in an analysis.
The additional limitation system “processor” applied to “train …machine learning model using a training dataset” lacks technical disclosure, instead focusing on the content of the training dataset and therefore, is not directed toward the technology of machine learning models.
The additional limitation system “processor” applied to “generate a plurality of tags for …domain specific document based on …taxonomies, using the machine learn model. The wherein clause limits the data of the documents to be unextractable and the tags generated based on probability score whether insight criterion associated with domain-specific document met”; applied to perform the operation “generate …structured insight data based on categorization of data meta data using …tags” using the machine learning model
The claimed processor using the machine learning model is merely applying technology lacking technical details to organize and tag data for analysis according to taxonomies and to generate insight data using the tags and probability score of insight criterion associated with domain specific document. The “generate” steps are not directed improvement to any underlying technology or provide solutions to a problem in the technology itself. Applying technology to organize and classify data according to domains in order to output “insight data” without going beyond merely linking the process to a technical environment or field of use is not sufficient under step 2A prong 2.
The functions are is recited at a high-level of generality such that it amounts to no more than applying the exception using generic computer components. The claim limitations and specification lacks technical disclosure on the specific operations claimed to perform the limitations for receiving, identifying and extracting data for use in analysis for generating data insights based on set of taxonomies and outputting the results. Technology is not integral to the process as the claimed subject matter is so high level that any generic programming could be applied and the functions could be performed by any known means. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
When the claims are taken as a whole, as an ordered combination, the combination of limitations 1-3 are directed toward searching to identify a document in response to a request, and then applying at a high level NLP model to extract textual data from the document for input into a learning model for analysis – which applying technology in order to identify and extract data for data organization- a business practice. The combination of limitations 1-3 and 4-5 is directed toward training the model using the training datasets and inputting data of limitations 1-3. The combination is not directed toward improving machine learning model through a training process but rather limiting the data applied to train the model and the data inputted for analysis by the model for analysis. The combination of limitations 1-5 and 6-7 is directed toward organizing and classifying data that is generated into structured insight data using a learning model to organize and manipulate inputted data from limitations 1-5 and outputting the results.
Accordingly as a whole, the claimed subject matter is directed toward an organizing and classifying data received for transmission for a business practice. The combinations of parts is not directed toward any of the indications of patent eligible subject matter under step 2A prong 2.
MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h).
(i) an improvement to the functioning of a computer;
(ii) an improvement to another technology or technical field;
(iii) an application of the abstract idea with, or by use of, a
particular machine;
(iv) a transformation or reduction of a particular article to a
different state or thing; or
(v) other meaningful limitations beyond generally linking the
use of the abstract idea to a particular technological environment.
When the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements that go beyond applying technology (NLP and learning models) as a tool to perform the identified abstract idea. The functions recited in the claims recite the concept of identifying and extracting document textual information that is inputted into a learning model used to classify and organize data that is then generate data insights and outputting the results. The limitations of identify, extract and manipulate data for output is not directed toward the underlying technology of the system claimed, the NLP models or learning models recited in the limitations. The limitations claimed do not improve upon technology or improve upon computer functionality or capability in how system, processors, NLP models or learning models carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The limitations do not recite a specific use machine or the transformation of an article to a different state or thing. The limitations do not provide other meaningful limits beyond generally linking the use of the abstract idea to a particular technological environment. The resource claimed performing the steps is merely a “field of use” application of technology. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments apply what generic computer functionality in the related arts. The steps are still a combination made to perform a financial activity and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, a particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim provides no technical details regarding how the operations performed by the “system”, “NLP model”, or “learning model”. Instead, similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)). The claim is directed to an abstract idea
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond abstract idea include a system comprising a non-transitory memory having instructions stored thereon; and at least one processor communicatively coupled to the non-transitory memory for use in performing the operations “receive …request”, “search database”, “apply NLP model”, “provide data as inputs” and “transmit” data- some of the most basic functions of a computer. The “generate…classification tags for generating structured insight data” implemented “via the…learning model” is recited at a high level for use in organizing and classifying data. Taking the claim elements separately, the function performed by the system and corresponding models at each operations of the process is purely conventional.
Limitations referenced in Alice that are not enough to qualify as “significantly more” include “apply it” (or an equivalent) with an abstract idea, which have been determined to be no more than mere instructions to implement the abstract idea on a system and/or models or requiring no more than a generic model/system to perform generic computer functions that are well understood activities known to the industry. As a result, none of the hardware or models recited by the system claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.... The claim limitations do not recite that any of the “devices” perform more than a high level generic function .... None of the limitations recite technological implementation details for any of these steps, but instead recite only results desired to be achieved by any and all possible means.... Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these computer functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “receive”, “search”, “extract”, “provide…inputs”, “generate…structured data” and “transmit…display” ... are functions can be achieved by any general purpose computer without special programming. None of these activities are used in some unconventional manner nor do any produce some unexpected result. In short, each step does no more than require a generic system and high level NLP and learning models to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). The limitations of “extracting”, “providing inputs”, “generate …structured data” and “transmit …display” similarly does not make the data organization less abstract. Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) The claimed sequence of “receive request”, “searching database”, “extract data”, “provide data as inputs”, “generate structured data” and “transmit display” does not provide significantly more than the identified abstract idea. The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
The specification discloses:
[0025]… The network environment 100 includes a plurality of devices or systems configured to communicate over one or more network channels, illustrated as a network cloud 118. For example, in various embodiments, the network environment 100 can include, but not limited to, an insight generation computing device 102, a server 104 (e.g., a web server or an application server), a cloud-based engine 121 including one or more processing devices 120, data center(s) 109, a database 116, and one or more user computing devices 110, 112, 114 operatively coupled over the network 118. The insight generation computing device 102, the server 104, the data center(s) 109, the processing device(s) 120, and the multiple user computing devices 110, 112, 114 can each be any suitable computing device that includes any hardware or hardware and software combination for processing and handling information. For example, each can include one or more processors, one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more state machines, digital circuitry, or any other suitable circuitry. In addition, each can transmit and receive data over the communication network 118.
[0026] In some examples, each of the insight generation computing device 102 and the processing device(s) 120 can be a computer, a workstation, a laptop, a server such as a cloud-based server, or any other suitable device. In some examples, each of the processing devices 120 is a server that includes one or more processing units, such as one or more graphical processing units (GPUs), one or more central processing units (CPUs), and/or one or more processing cores. Each processing device 120 may, in some examples, execute one or more virtual machines. In some examples, processing resources (e.g., capabilities) of the one or more processing devices 120 are offered as a cloud-based service (e.g., cloud computing). For example, the cloud-based engine 121 may offer computing and storage resources of the one or more processing devices 120 to the insight generation computing device 102. [0036] In some embodiments, the insight generation computing device 102 is further operable to communicate with the database 116 over the communication network 118. For example, the insight generation computing device 102 can store data to, and read data from, the database 116. The database 116 can be a remote storage device, such as a cloud-based server, a disk (e.g., a hard disk), a memory device on another application server, a networked computer, or any other suitable remote storage. Although shown remote to the insight generation computing device 102, in some examples, the database 116 can be a local storage device, such as a hard drive, a non-volatile memory, or a USB stick. The insight generation computing device 102 may store data received from the server 104 in the database 116. The insight generation computing device 102 may receive data from the data center(s) 109 and store them in the database 116. The insight generation computing device 102 may also store the structured insight data in the database 116.
With respect to the NLP and learning models the specification discloses:
[0021] The disclosed system saves time and expense by reducing third-party legal or inhouse experts review times. The disclosed system can organize, tag, and analyze search results to provide intelligent inputs for users' risk decisions, rather than providing a data dump. The disclosed system provides a scalable platform to support multi-factor growth. The disclosed system utilizes optical character recognition (OCR), natural language processing (NLP), artificial intelligence (Al), feedback loops, rather than or in addition to human expert reviews, to produce accurate decision making and reduce risk of decision errors. In some embodiments, the disclosed method can be realized with a single API call that is integrated into workflow and supporting systems.
[0035] In some examples, the insight generation computing device 102 may execute one or
more models (e.g., programs or algorithms), such as a machine learning model, deep learning model, statistical model, etc., to generate the structured insight data. The insight generation computing device 102 may transmit the structured insight data (e.g. insights about a lien, a contract, a legal document, etc.) to the server 104 over the communication network 118, and the server 104 may display the structured insight data on the website or via API to users (e.g. supplier finance programs, business lenders) who are interested in these data.
[0037] In some examples, the insight generation computing device 102 generates training
data for a plurality of models (e.g., machine learning models, deep learning models, statistical
models, algorithms, etc.) based on: e.g. historical search data, historical insight data, historical user
feedback data, etc. The insight generation computing device 102 trains the models based on their
corresponding training data, and stores the models in a database, such as in the database 116 (e.g., a
cloud storage).
[0038] The models, when executed by the insight generation computing device 102, allow
the insight generation computing device 102 to generate insight data based on corresponding
datasets. For example, the insight generation computing device 102 may obtain the models from the
database 116. The insight generation computing device 102 may receive, in real-time from the
server 104, an insight generation request identifying a request from a user for insights of some legal
documents. In response to receiving the request, the insight generation computing device 102 may
execute the models to generate insights for the legal documents to be displayed to the user.
[0061] The data extraction model 392 may be used to extract textual data and/or metadata from a document in the search results. In some embodiments, the data extraction model 392 is based on optical character recognition (OCR) and natural language processing (NLP). For example, after OCR is applied to recognize textual information of the document, NLP can be applied to determine boundaries of the textual data meeting predetermined conditions or queries.
[0062] The taxonomy generation model 394 may be used to generate a set of taxonomies based on an industry type and/or user configuration related to a user request. For example, the taxonomy generation model 394 is used to generate different taxonomies according to different industry types of user requests. A different industry type corresponds to different types of documents and/or different concerned data in the documents of the search results. The taxonomy generation model 394 may map different sets of taxonomies to different industry types. In some examples, the taxonomy generation model 394 is used to generate taxonomies based on a user configuration specified in the user request, where the user configuration may identify concerned data lists or types in the search results. In some examples, the taxonomy generation model 394 is used to generate taxonomies based on both the industry type and the user configuration.
[0072] In some embodiments, the data extraction engine 420 can obtain or collect various
data with respect to the insight generation request 310, either from the document retrieval engine
410 or directly from the database 116. In some embodiments, the data extraction engine 420
extracts textual data and metadata from each document in the search results 322, e.g. based on the
data extraction model 392. For example, the data extraction engine 420 can automatically extract
the textual data and metadata using optical character recognition (OCR) and natural language
processing (NLP), based on predetermined keywords or conditions. The keywords or conditions
may be determined based on the industry type associated with the insight generation request 310 or a
user configuration. The data extraction engine 420 then sends the extracted data to the taxonomy
based tagging engine 430 for tagging.
[0073] In the above example of the lender, the data extraction engine 420 can extract, from
the UCC documents, text and metadata including key information related to liens or the collateral.
For example, the metadata may comprise information related to at least one of: a filing identity of
each UCC document, a filing date of each UCC document, a UCC type of each UCC document, a
debtor in each UCC document, a secured party in each UCC document, or other filing data of each
UCC document. The extracted data can help to quickly identify insights on lending risk locked in
lengthy collateral text descriptions in the UCC fillings of the search results 322.
[0083] The automated workflow at the operation 520 uses AI models or machine learning models to reduce risk in critical lending decisions and deliver intelligence at scale such that lenders have confidence in their decisions. The AI models used in the disclosed system have overcome many challenges caused by non-uniformity of the UCC forms across states and time, historical changes of lien documents, etc.
[0084] First, many UCC filings have a poor image quality, with no clear boundary defined for different fields. In addition, metadata, collateral descriptions, and filing details may be distributed across different locations (e.g. UCC forms, Schedule A, Addendums, Exhibits, etc.) of lien documents. Even a same content may be in different locations or sections of different documents. The AI models are trained to extract concerned text from all possible locations of lien documents, and using OCR and NLP to extract accurate texts from poor imaged documents.
Similar to Content Extraction and Electric Power Group the combination of limitation apply known and generic high level technology to extract data from documents (collect data), input data for analysis and output the result
With respect to the limitation “generate …a plurality of tags…” wherein at least one tag…indicates insight data unextractable from …document …based on a score representing probability …”, the specification fails to provide any technical details as to implementation, therefore the generating of the one tag using AI technology amounts to no more than mere instructions to classify the data in the generation of tags. .
[0087] Further, many insight data like whether a lien is a blanket lien or not, cannot be
directly extracted from a lien document. The AI models are trained to generate a score evaluating a
probability whether a lien is blanket, based on and textual and contextual information of the lien
document. The lien is tagged as blanket when the score is larger than a predetermined threshold. In some embodiments, the AI models are trained with training data including qualifying phrases
indicating a lien is blanket and negative phrases indicating a lien is not blanket.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-3 and 5-8 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 claims the learning model comprising one of at least a data extraction model, taxonomy generation model, tag generation model, an insight generation model and insight presentation model- well understood technology. Dependent claim 3 limits the document of claim 1 to a lien over collateral document- legal obligation agreement. Dependent claim 5 is directed toward limiting tags of claim 4 to property of collateral- indexing data- common business practice. Dependent claim 6 is directed toward limiting the NLP model to the operations of scanning document and applying OCR to extract data- well understood technology. Dependent claim 7 is directed toward limiting taxonomies to an industry, user search request or combination thereof- directed toward categorizing data based on business practice data- common business practice. Dependent claim 8 is directed toward training model based on labelled data/feedback data- lacks technical disclosure merely limits the data acted upon- generic implementation of technology.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 5. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-3 and 5-8 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to claims 9-13 and 15-16:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 9 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The steps of Method claim 9 corresponds to system claim 1. Therefore, claim 9 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The steps of Method claim 9 corresponds to system claim 1. Therefore, claim 9 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include applying an NLP model to extract data and using a ML model to generate structured data. Nearly every NLP model as recited in the claim is capable of performing the basic computer extracting data from a document. The language “via …machine learning model” applied to generate structured data lacks technical disclosure. The limitation as claim merely applies ML model for organizing data based on sets of categories for use in data analysis. As a result, none of the models recited by the method claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.
Method claim 9 steps corresponds to system functions claim 1. Therefore, claim 9 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
Evidence of such conventional technology includes:
With respect to the NLP and learning models the specification discloses:
[0021] The disclosed system saves time and expense by reducing third-party legal or inhouse experts review times. The disclosed system can organize, tag, and analyze search results to provide intelligent inputs for users' risk decisions, rather than providing a data dump. The disclosed system provides a scalable platform to support multi-factor growth. The disclosed system utilizes optical character recognition (OCR), natural language processing (NLP), artificial intelligence (Al), feedback loops, rather than or in addition to human expert reviews, to produce accurate decision making and reduce risk of decision errors. In some embodiments, the disclosed method can be realized with a single API call that is integrated into workflow and supporting systems.
[0035] In some examples, the insight generation computing device 102 may execute one or
more models (e.g., programs or algorithms), such as a machine learning model, deep learning model, statistical model, etc., to generate the structured insight data. The insight generation computing device 102 may transmit the structured insight data (e.g. insights about a lien, a contract, a legal document, etc.) to the server 104 over the communication network 118, and the server 104 may display the structured insight data on the website or via API to users (e.g. supplier finance programs, business lenders) who are interested in these data.
[0037] In some examples, the insight generation computing device 102 generates training
data for a plurality of models (e.g., machine learning models, deep learning models, statistical
models, algorithms, etc.) based on: e.g. historical search data, historical insight data, historical user
feedback data, etc. The insight generation computing device 102 trains the models based on their
corresponding training data, and stores the models in a database, such as in the database 116 (e.g., a
cloud storage).
[0038] The models, when executed by the insight generation computing device 102, allow
the insight generation computing device 102 to generate insight data based on corresponding
datasets. For example, the insight generation computing device 102 may obtain the models from the
database 116. The insight generation computing device 102 may receive, in real-time from the
server 104, an insight generation request identifying a request from a user for insights of some legal
documents. In response to receiving the request, the insight generation computing device 102 may
execute the models to generate insights for the legal documents to be displayed to the user.
[0061] The data extraction model 392 may be used to extract textual data and/or metadata from a document in the search results. In some embodiments, the data extraction model 392 is based on optical character recognition (OCR) and natural language processing (NLP). For example, after OCR is applied to recognize textual information of the document, NLP can be applied to determine boundaries of the textual data meeting predetermined conditions or queries.
[0062] The taxonomy generation model 394 may be used to generate a set of taxonomies based on an industry type and/or user configuration related to a user request. For example, the taxonomy generation model 394 is used to generate different taxonomies according to different industry types of user requests. A different industry type corresponds to different types of documents and/or different concerned data in the documents of the search results. The taxonomy generation model 394 may map different sets of taxonomies to different industry types. In some examples, the taxonomy generation model 394 is used to generate taxonomies based on a user configuration specified in the user request, where the user configuration may identify concerned data lists or types in the search results. In some examples, the taxonomy generation model 394 is used to generate taxonomies based on both the industry type and the user configuration.
[0072] In some embodiments, the data extraction engine 420 can obtain or collect various
data with respect to the insight generation request 310, either from the document retrieval engine
410 or directly from the database 116. In some embodiments, the data extraction engine 420
extracts textual data and metadata from each document in the search results 322, e.g. based on the
data extraction model 392. For example, the data extraction engine 420 can automatically extract
the textual data and metadata using optical character recognition (OCR) and natural language
processing (NLP), based on predetermined keywords or conditions. The keywords or conditions
may be determined based on the industry type associated with the insight generation request 310 or a
user configuration. The data extraction engine 420 then sends the extracted data to the taxonomy
based tagging engine 430 for tagging.
[0073] In the above example of the lender, the data extraction engine 420 can extract, from
the UCC documents, text and metadata including key information related to liens or the collateral.
For example, the metadata may comprise information related to at least one of: a filing identity of
each UCC document, a filing date of each UCC document, a UCC type of each UCC document, a
debtor in each UCC document, a secured party in each UCC document, or other filing data of each
UCC document. The extracted data can help to quickly identify insights on lending risk locked in
lengthy collateral text descriptions in the UCC fillings of the search results 322.
[0083] The automated workflow at the operation 520 uses AI models or machine learning models to reduce risk in critical lending decisions and deliver intelligence at scale such that lenders have confidence in their decisions. The AI models used in the disclosed system have overcome many challenges caused by non-uniformity of the UCC forms across states and time, historical changes of lien documents, etc.
[0084] First, many UCC filings have a poor image quality, with no clear boundary defined for different fields. In addition, metadata, collateral descriptions, and filing details may be distributed across different locations (e.g. UCC forms, Schedule A, Addendums, Exhibits, etc.) of lien documents. Even a same content may be in different locations or sections of different documents. The AI models are trained to extract concerned text from all possible locations of lien documents, and using OCR and NLP to extract accurate texts from poor imaged documents.
With respect to the limitation “generate …a plurality of tags…” wherein at least one tag…indicates insight data unextractable from …document …based on a score representing probability …”, the specification fails to provide any technical details as to implementation, therefore the generating of the one tag using AI technology amounts to no more than mere instructions to classify the data in the generation of tags. .
[0087] Further, many insight data like whether a lien is a blanket lien or not, cannot be
directly extracted from a lien document. The AI models are trained to generate a score evaluating a
probability whether a lien is blanket, based on and textual and contextual information of the lien
document. The lien is tagged as blanket when the score is larger than a predetermined threshold. In some embodiments, the AI models are trained with training data including qualifying phrases
indicating a lien is blanket and negative phrases indicating a lien is not blanket.
Electric Power Group and Content Extraction
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 10-13 and 15-16 these dependent claim have also been reviewed with the same analysis as independent claim 9. Dependent claim 10 claims the learning model comprising one of at least a data extraction model, taxonomy generation model, tag generation model, an insight generation model and insight presentation model- well understood technology. Dependent claim 11 limits the document of claim 9 to a lien over collateral document- legal obligation agreement. Dependent claim 12 limits the model to generate a plurality of tags for document based on taxonomies and structured data generated based on categorization of data using tags- applying technology to index and organize data- see Cogent Med., Inc. v. Elsevier Inc. Dependent claim 13 is directed toward limiting tags of claim 12 to property of collateral- indexing data- common business practice. Dependent claim 15 is directed toward limiting taxonomies to an industry, user search request or combination thereof- directed toward categorizing data based on business practice data- common business practice. Dependent claim 16 is directed toward training model based on labelled data/feedback data- lacks technical disclosure merely limits the data acted upon- generic implementation of technology.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 9. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 10-13 and 15-16 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 17-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include non-transitory computer readable medium, as in independent Claim 17 and the dependent claims. Such mediums fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of medium claim 17 corresponds to operations of system claim 1. Therefore, claim 17 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The instructions of medium claim 17 corresponds to operations of system claim 1. Therefore, claim 17 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a non-transitory computer readable medium having instructions stored for execution by a processor, applying an NLP model to extract data and using a ML model to generate structured data to perform the operations corresponding to claim 1 operations. Nearly every NLP model as recited in the claim is capable of performing the basic computer extracting data from a document. The language “via …machine learning model” applied to generate structured data lacks technical disclosure. The limitation as claim merely applies ML model for organizing data based on sets of categories for use in data analysis. As a result, none of the models recited by the method claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.
Medium claim 17 instructions corresponds to system functions claim 1. Therefore, claim 9 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
Evidence of such conventional technology includes:
With respect to the NLP and learning models the specification discloses:
[0021] The disclosed system saves time and expense by reducing third-party legal or inhouse experts review times. The disclosed system can organize, tag, and analyze search results to provide intelligent inputs for users' risk decisions, rather than providing a data dump. The disclosed system provides a scalable platform to support multi-factor growth. The disclosed system utilizes optical character recognition (OCR), natural language processing (NLP), artificial intelligence (Al), feedback loops, rather than or in addition to human expert reviews, to produce accurate decision making and reduce risk of decision errors. In some embodiments, the disclosed method can be realized with a single API call that is integrated into workflow and supporting systems.
[0035] In some examples, the insight generation computing device 102 may execute one or
more models (e.g., programs or algorithms), such as a machine learning model, deep learning model, statistical model, etc., to generate the structured insight data. The insight generation computing device 102 may transmit the structured insight data (e.g. insights about a lien, a contract, a legal document, etc.) to the server 104 over the communication network 118, and the server 104 may display the structured insight data on the website or via API to users (e.g. supplier finance programs, business lenders) who are interested in these data.
[0037] In some examples, the insight generation computing device 102 generates training
data for a plurality of models (e.g., machine learning models, deep learning models, statistical
models, algorithms, etc.) based on: e.g. historical search data, historical insight data, historical user
feedback data, etc. The insight generation computing device 102 trains the models based on their
corresponding training data, and stores the models in a database, such as in the database 116 (e.g., a
cloud storage).
[0038] The models, when executed by the insight generation computing device 102, allow
the insight generation computing device 102 to generate insight data based on corresponding
datasets. For example, the insight generation computing device 102 may obtain the models from the
database 116. The insight generation computing device 102 may receive, in real-time from the
server 104, an insight generation request identifying a request from a user for insights of some legal
documents. In response to receiving the request, the insight generation computing device 102 may
execute the models to generate insights for the legal documents to be displayed to the user.
[0061] The data extraction model 392 may be used to extract textual data and/or metadata from a document in the search results. In some embodiments, the data extraction model 392 is based on optical character recognition (OCR) and natural language processing (NLP). For example, after OCR is applied to recognize textual information of the document, NLP can be applied to determine boundaries of the textual data meeting predetermined conditions or queries.
[0062] The taxonomy generation model 394 may be used to generate a set of taxonomies based on an industry type and/or user configuration related to a user request. For example, the taxonomy generation model 394 is used to generate different taxonomies according to different industry types of user requests. A different industry type corresponds to different types of documents and/or different concerned data in the documents of the search results. The taxonomy generation model 394 may map different sets of taxonomies to different industry types. In some examples, the taxonomy generation model 394 is used to generate taxonomies based on a user configuration specified in the user request, where the user configuration may identify concerned data lists or types in the search results. In some examples, the taxonomy generation model 394 is used to generate taxonomies based on both the industry type and the user configuration.
[0072] In some embodiments, the data extraction engine 420 can obtain or collect various
data with respect to the insight generation request 310, either from the document retrieval engine
410 or directly from the database 116. In some embodiments, the data extraction engine 420
extracts textual data and metadata from each document in the search results 322, e.g. based on the
data extraction model 392. For example, the data extraction engine 420 can automatically extract
the textual data and metadata using optical character recognition (OCR) and natural language
processing (NLP), based on predetermined keywords or conditions. The keywords or conditions
may be determined based on the industry type associated with the insight generation request 310 or a
user configuration. The data extraction engine 420 then sends the extracted data to the taxonomy
based tagging engine 430 for tagging.
[0073] In the above example of the lender, the data extraction engine 420 can extract, from
the UCC documents, text and metadata including key information related to liens or the collateral.
For example, the metadata may comprise information related to at least one of: a filing identity of
each UCC document, a filing date of each UCC document, a UCC type of each UCC document, a
debtor in each UCC document, a secured party in each UCC document, or other filing data of each
UCC document. The extracted data can help to quickly identify insights on lending risk locked in
lengthy collateral text descriptions in the UCC fillings of the search results 322.
[0083] The automated workflow at the operation 520 uses AI models or machine learning models to reduce risk in critical lending decisions and deliver intelligence at scale such that lenders have confidence in their decisions. The AI models used in the disclosed system have overcome many challenges caused by non-uniformity of the UCC forms across states and time, historical changes of lien documents, etc.
[0084] First, many UCC filings have a poor image quality, with no clear boundary defined for different fields. In addition, metadata, collateral descriptions, and filing details may be distributed across different locations (e.g. UCC forms, Schedule A, Addendums, Exhibits, etc.) of lien documents. Even a same content may be in different locations or sections of different documents. The AI models are trained to extract concerned text from all possible locations of lien documents, and using OCR and NLP to extract accurate texts from poor imaged documents.
With respect to the limitation “generate …a plurality of tags…” wherein at least one tag…indicates insight data unextractable from …document …based on a score representing probability …”, the specification fails to provide any technical details as to implementation, therefore the generating of the one tag using AI technology amounts to no more than mere instructions to classify the data in the generation of tags. .
[0087] Further, many insight data like whether a lien is a blanket lien or not, cannot be
directly extracted from a lien document. The AI models are trained to generate a score evaluating a
probability whether a lien is blanket, based on and textual and contextual information of the lien
document. The lien is tagged as blanket when the score is larger than a predetermined threshold. In some embodiments, the AI models are trained with training data including qualifying phrases
indicating a lien is blanket and negative phrases indicating a lien is not blanket.
Electric Power Group and Content Extraction
The claim is not "truly drawn to a specific" computer readable medium, but rather is directed toward the using technology to store instructions of organizing data extracted from documents. Simply reciting the use of a computer to execute an algorithm that can be performed the abstract idea will not change the analysis. The claim is determined not to meet the Alice/May 2A and 2B test. Although the claim altered data, "[t]he mere manipulation or reorganization of data, however, does not satisfy the transformation prong." Furthermore, the "incidental use" of a computer and recited models does not allow the claim to meet the Alice 2A or 2B requirements.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 18-20 these dependent claim have also been reviewed with the same analysis as independent claim 17. Dependent claim 18 limits the document of claim 17 to a lien over collateral document- legal obligation agreement. Dependent claim 19 limits the model to one of data extraction model, taxonomy generation model, tag generation model, an insight generation model and insight presentation model- well understood technology. Dependent claim 20 is directed toward the operations of scanning document and applying OCR to extract data- well understood technology.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 17. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 18-20 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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.
Claim(s) 1-2 and 6-8; Claim(s) 9-10 and 14-16; Claim(s) 17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0153641 A1 by Manda et al (Manda), and further in view of US Pub No. 2022/0345543 A1 by Oleinikov et al. (Oleinikof)
In reference to Claim 1:
Manda teaches:
(Currently Amended) A system for automated data extraction and analysis in financial due diligence ((Manda) in at least FIG. 1; para 0067), comprising:
a non-transitory memory having instructions stored thereon ((Manda) in at least FIG. 1; para 0067); and
at least one processor communicatively coupled to the non-transitory memory, and configured to read the instructions ((Manda) in at least FIG. 1; para 0067) to:
receive a search request from a user device, wherein the search request is directed to a domain-specific database ((Manda) in at least para 0027, para 0061-0062, para 0074);
search the domain-specific database based on the search request to identify at least one domain-specific document ((Manda) in at least FIG. 3A; Abstract; para 0025, para 0029, para 0040, para 0043, para 0049-0050);
apply a natural language processing (NLP) model to extract textual data and metadata from the at least one domain-specific document ((Manda) in at least FIG. 2; para 0018, para 0025-0026, para 0033, para 0035, para 0036, para 0053);
train an insight related machine learning model ((Manda) in at least para 0002) using a training dataset including both positive phrases indicating an insight criterion associated with the at least one domain-specific document is met and negative phrases indicating the insight criterion associated with the at least one domain-specific document is not met ((Manda) in at least para 0016, para 0025, para 0029, para 0030, para 0035-0036 wherein the prior art teaches machine learning data provided to the model includes positive/negative indicators, para 0050 wherein the prior art teaches trained to search for particular labels within a document (e.g. invoice, purchase order, customer number, sales representative) ;
provide the textual data and the metadata as inputs to the insight related machine learning model ((Manda) in at least Abstract; para 0015, para 0018-0019, para 0027-0029, para 0032);
generate, via the insight related machine learning model, a plurality of tags for the at least one domain-specific document based on a set of taxonomies ((Manda) in at least Abstract; FIG. 3B; para 0015, para 0025, para 0033, para 0050, para 0052, para 0054, para 0063, para 0066)…;
generate, via the insight related machine learning model, structured insight data based on a categorization of the textual data and the metadata using the plurality of tags ((Manda) in at least para 0002, para 0056; and
transmit, to the user device, instructions configured to cause the user device to display the structured insight data ((Manda) in at least FIG. 3B; para 0054).
Manda does not explicitly teach:
wherein at least one tag in the plurality of tags indicates insight data unextractable from the at least one domain-specific document and is generated based on a score representing a probability of whether the insight criterion associated with the at least one domain-specific document is met;
Oleinikov teaches:
wherein at least one tag in the plurality of tags indicates insight data unextractable from the at least one domain-specific document and is generated based on a score representing a probability of whether the insight criterion associated with the at least one domain-specific document is met ((Oleinikov) in at least para 0062-0063 wherein the prior art teaches aggregating and synchronizing activities from a plurality of sources and information in system records of the data source providers to maintain a node graph with a connections between nodes to provide data driven insights to improve business processes where information of the activities of the node are applied to determine insights that can be shared with other nodes in synchronize activities to objects of record; para 0089-0091 wherein the prior art teaches nodes can be associated with contents of activity where information can be inferred based on information maintained by the node graph and based on connections of the node; para 0111 wherein the prior art teaches parser identifying values corresponding to attributes of node profile; para 0113-0114 wherein the prior art teaches use data from node profiles in an attempt to match activities to one or more node profiles based on values of node profiles by computing a match score between activities and candidate node profile; para 0117-0118-0121, para 0124, para 0133-0134 wherein the prior art teaches assigning tags to electronic activities, node profiles wherein the tagging to assign a confidence score to the one or more tags with details about types of tags, para 0135-0137 wherein the prior art teaches for example tags assigned to determine two domains belong to the same company updating the confidence score of values of fields of other node profiles; para 0143 wherein the prior art teaches tagging assigned may provide occurrence tags with respective confidence scores indicating likelihood of occurrence; para 0150 wherein the prior art teaches tagging process determining activity is business related and assigning a confidence score to the tag based on confidence electronic activity is business/personal related; para 0151 wherein the prior art teaches tag two node profiles and determining a confidence score for tag classifying two node profiles based on confidence in prediction of two node profiles have a relationship based on commonalities in values of node profiles; para 0300 wherein the prior art teaches setting restrictions on certain information (sensitive/competitive); para 0315, para 0326-0328; para 0358, para 0382, para 0402, para 0491, para 0506-0507, para 0572, para 0687, para 0698, para 0730, para 0782);
generate, via the insight related machine learning model, structured insight data based on a categorization of the textual data and the metadata using the plurality of tags ((Oleinikov) in at least para 0402-0403, para 0424, para 0767, para 0782)
Both Manda and Oleinikov teach collecting, analyzing, categorizing and tagging data in order to determine and output insight data for business applications. Oleinikov teaches the motivation of in the process for categorizing data for insights where data is restricted or protected from extraction assigning tags to specific content determined to be sensitive so that such data can be filtered from analysis and searching and determining content which satisfies content filtering rule based on determined confidence score associated with each of record objects. The prior art teaches the data processing system determining the highest confidence score and may only include single values for field value pairs that are not restricted. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the tag assignment process of Manda for outputting insight data to include a process to address protected data as taught by Oleinikov since Oleinikov teaches the motivation of in the process for categorizing data for insights where data is restricted or protected from extraction assigning tags to specific content determined to be sensitive so that such data can be filtered from analysis and searching and determining content which satisfies content filtering rule based on determined confidence score associated with each of record objects. The prior art teaches the data processing system determining the highest confidence score and may only include single values for field value pairs that are not restricted.
In reference to Claim 2:
The combination of Manda and Oleinikov discloses the limitations of independent claim 1. Manda further discloses the limitations of dependent claim 2.
(Currently amended) The system of claim 1 (see rejection of claim 1 above), wherein the insight related machine learning model comprises
at least one of the group consisting of: a data extraction model, a taxonomy generation model, a tag generation model, an insight generation model, and an insight presentation model. ((Manda) in at least para 0026, para 0029, para 0032, para 0038, para 0044, para 0046, para 0050)
Oleinikov provides supporting evidence:
at least one of the group consisting of: a data extraction model, a taxonomy generation model, a tag generation model, an insight generation model, and an insight presentation model ((Oleinikov) in at least FIG. 4; para 0069 wherein the prior art teaches the data processing system including a record data extractor, activity parser (taxonomy model), para 0164 wherein the prior art teaches a tagging engine, para 0403 wherein the prior art teaches node graph generation system including a performance module applied to generate insight data)
Both Manda and Oleinikov teach collecting, analyzing, categorizing and tagging data in order to determine and output insight data for business applications. Oleinikov teaches the motivation of including a node generations with a plurality of modules/engines applied to generate insight data and output the result for use in business practices. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to incorporate the engines and modules as taught by Oleinikov since similar to Manda, . Oleinikov teaches the motivation of including a node generations with a plurality of modules/engines applied to generate insight data and output the result for use in business practices
In reference to Claim 6:
The combination of Manda and Oleinikov discloses the limitations of independent claim 1. Manda further discloses the limitations of dependent claim 6.
(Currently Amended) The system of claim 1 (see rejection of claim 1 above), wherein the NLP model is configured to:
scan the at least one domain-specific document ((Manda) in at least para 0034, para 0062, ; and
apply optical character recognition (OCR) to extract the textual data and metadata relevant to the search request ((Manda) in at least para 0018, para 0026, para 0036, para 0053).
In reference to Claim 7:
The combination of Manda and Oleinikov discloses the limitations of independent claim 1. Manda further discloses the limitations of dependent claim 7.
(Original) The system of claim 1 (see rejection of claim 1 above),
wherein the set of taxonomies are determined based on an industry associated with the search request, a user configuration associated with the search request, or a combination thereof. ((Manda) in at least para 0043, para 0050)
In reference to Claim 8:
The combination of Manda and Oleinikov discloses the limitations of independent claim 1. Manda further discloses the limitations of dependent claim 8.
(Original) The system of claim 1 (see rejection of claim 1 above),
wherein the insight related machine learning model is trained based on labelled data and feedback data.((Manda) in at least par 0029-0030, para 0036, para 0048),
In reference to Claim 9:
The combination of Manda and Oleinikov discloses the limitations of independent claim 9.
The steps of method claim 9 correspond to the operations of system claim 1. Therefore, claim 9 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 10:
The combination of Manda and Oleinikov discloses the limitations of independent claim 9. Manda further discloses the limitations of dependent claim 10.
The steps of method claim 10 correspond to the operations of system claim 2. Therefore, claim 10 has been analyzed and rejected as previously discussed with respect to claim 2.
In reference to Claim 14:
The combination of Manda and Oleinikov discloses the limitations of independent claim 9. Manda further discloses the limitations of dependent claim 14
The steps of method claim 14 correspond to the operations of system claim 6. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 6.
In reference to Claim 15:
The combination of Manda and Oleinikov discloses the limitations of independent claim 9. Manda further discloses the limitations of dependent claim 15.
The steps of method claim 15 correspond to the operations of system claim 7. Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 7
In reference to Claim 16:
The combination of Manda and Oleinikov discloses the limitations of independent claim 9. Manda further discloses the limitations of dependent claim 16
(Currently Amended) The computer implemented method of claim 9 (see rejection of claim 9 above),
wherein the insight related machine learning model is trained based on labelled data and feedback data.((Manda) in at least par 0029-0030, para 0036, para 0048),
In reference to Claim 17:
The combination of Manda and Oleinikov discloses the limitations of independent claim 17.
The instructions of medium claim 17 correspond to the operations of system claim 1. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 1
In reference to Claim 19:
The combination of Manda and Oleinikov discloses the limitations of dependent claim 17. Manda further discloses the limitations of dependent claim 19.
The instructions of medium claim 19 correspond to the operations of system claim 2. Therefore, claim 19 has been analyzed and rejected as previously discussed with respect to claim 2.
In reference to Claim 20:
The combination of Manda and Oleinikov discloses the limitations of independent claim 17. Manda further discloses the limitations of dependent claim 20
The instructions of medium claim 20 correspond to the operations of system claim 6. Therefore, claim 20 has been analyzed and rejected as previously discussed with respect to claim 6
Claim(s) 3 and 5 as applied to claim 1 above, Claim(s) 11 and 13 as applied to claim 9 above; Claim(s) 18-19 of claim 17 above is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0153641 A1 by Manda et al (Manda), in view of US Pub. No. 2022/0345543 A1 by Oleinikov et al. (Olienikov) and further in view of US Pub No. 2018/0173681 A1 by Dedhia et al. (Dedhia)
In reference to Claim 3:
The combination of Manda and Oleinikov discloses the limitations of independent claim 1. Manda further discloses the limitations of dependent claim 3.
(Original) The system of claim 1 (see rejection of claim 1 above),
wherein the at least one domain-specific document is related to a …[agreement] granted over a collateral ((Manda) in at least para 0025 wherein the prior art teaches documents related to asset management, claims property, property damage inspection, para 0049 wherein the prior art teaches document includes insurance underwriting, para 0050, para 0056).
Manda does not explicitly teach:
document is related to a lien granted over a collateral
Dedhia teaches:
document is related to a lien granted over a collateral ((Dedhia) in at least para 0028, para 0045)
According to KSR, simple substitution of one known element for another to obtain predictable results is common sense obviousness rationale. The prior art Manda contained documents with content which differed from the claimed document content by the substitution of document content with other document content. The prior art Dedhia provide evidence that the substituted document content and their functions where known in the art and the content can also be extracted and applied in analysis.
Accordingly, one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable.
Both Manda and Dehia are directed toward applying technology to scan and extract data from documents where the textual data extracted is classified for use. Dehia teaches the motivation of generating content from webpages, websites pertaining to business transactions and that such business transaction include content related to real property assets that contain importance data related to the business transaction. Dehia teaches that loan documents contain valuable information for generation of content and applying a process for parsing documents related to real property assets in order to extract various features depicted in the document from which data is extracted can be related to loans as in a financial service process such documents are applied in verifying assets/debt/income for use in the business application process. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the documents from which data is extracted for a business process of Manda to include documents related to liens as taught by Dedhia since Dehia teaches the motivation of generating content from webpages, websites pertaining to business transactions and that such business transaction include content related to real property assets that contain importance data related to the business transaction. Dehia teaches that loan documents contain valuable information for generation of content and applying a process for parsing documents related to real property assets in order to extract various features depicted in the document from which data is extracted can be related to loans as in a financial service process such documents are applied in verifying assets/debt/income for use in the business application process.
In reference to Claim 5:
The combination of Manda, Oleinikov and Dehia discloses the limitations of dependent claim 3. Manda further discloses the limitations of dependent claim 5.
(currently Amended) The system of claim 3 (see rejection of claim 3 above),
Manda does not explicitly teach:
wherein the plurality of tags comprises tags related to a property of the collateral.
Dehia teaches:
wherein the plurality of tags comprises tags related to a property of the collateral. ((Dehia) in at least Abstract; para 0009, para 0018)
Both Manda and Dehia are directed toward extracting content from document for various business practices. Dehia teaches the motivation of assigning tags which indicates separate characteristics of data in order to provide a means to search content based on category of data. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the classification of data of Dehia to include assigning tags to specific content extracted from documents as taught by Dehia since Dehia teaches the motivation of assigning tags which indicates separate characteristics of data in order to provide a means to search content based on category of data.
In reference to Claim 11:
The combination of Manda and Oleinikov discloses the limitations of independent claim 9. Manda further discloses the limitations of dependent claim 11.
The steps of method claim 11 correspond to the operations of system claim 3. Therefore, claim 11 has been analyzed and rejected as previously discussed with respect to claim 3.
In reference to Claim 13:
The combination of Manda, Oleinikov and Dehia discloses the limitations of dependent claim 12. Manda further discloses the limitations of dependent claim 13.
The steps of method claim 13 correspond to the operations of system claim 5. Therefore, claim 13 has been analyzed and rejected as previously discussed with respect to claim 5
In reference to Claim 18:
The combination of Manda and Dehia discloses the limitations of dependent claim 17. Manda further discloses the limitations of dependent claim 18
The instructions of medium claim 18 correspond to the operations of system claim 3. Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 3.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Pub No. 2025/0088538 A1 by Saripalli et al; WO 2025058795 A1 by Saripalli et al
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MARY M GREGG/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695