DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. The information disclosure statements (IDS) submitted on 10/14/2025 and 01/13/2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Status of the Claims
3. Applicant filed response to Election/Restriction requirement on 06/04/2026. Claims 1-16 are elected for examination. Claims 17-20 are canceled. Claims 1-16 are pending.
Claim Interpretation
Intended Use
4. Intended use language is generally not given patentable weight. See MPEP 2114(II) ("A claim containing a 'recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus’ if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987).”); see also MPEP 2103(C). Examples of claim limitations that are often found to precede intended use include “adapted to,” “capable of,” “sufficient to,” “whereby,” and “for.”
5. Claim 1 recites “applying the business ontology to recognize and tag each transaction element …”, “analyzing the semantic graph to recognize a plurality of relationships…”, “updating the semantic graph to capture the plurality of relationships…”, and “at least one respective interactive control configured to enable a user … to adjust the respective value…”.
Claim Rejections - 35 USC § 112
6. 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.
7. Claims 1-16 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
8. Claim 1 recites “accessing, from a non-transitory computer-readable data store, a plurality of documents…”.
It is not clear whether “a non-transitory computer-readable data store” is the same “a non-transitory computer-readable data store” that configured to store a semantic graph in previously cited limitation or different one.
9. “An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.” Zletz, 893 F.2d at 322, 13 USPQ2d at 1322. “For example, if the language of a claim, given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate.” MPEP 2173.02 I.
10. Claims 2-16 are rejected under the same rationale as claim 1 because claims 2-16 inherit the deficiencies of claim 1 due to their dependency.
Claim Rejections - 35 USC §101
11. 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.
12. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
13. In the instant case, claim 1 is directed to “a system for extracting, and organizing, and visualizing details of risk fulfillment options provided by multiple third parties in response to a risk fulfillment transaction request”.
14. Claim 1 recites “managing insurance related data from multiple parties for risk fulfillment transaction request”. Specifically, claim recites [abstract ideas emphasized in bold] “at least one non-transitory computer-readable storage medium storing a business ontology comprising a plurality of terms and a plurality of business rules, each business rule defining relationships between a respective set of terms of the plurality of terms, and
a semantic ontology defining risk fulfillment information, the semantic ontology comprising
a hierarchy of risk fulfillment terms,
a plurality of risk fulfillment rules, each risk fulfillment rule applied to at least one term of the hierarchy of risk fulfillment terms, and
a plurality of relationships between sets of terms of the hierarchy of risk fulfillment terms;
a non-transitory computer-readable data store configured to store a semantic graph; and
processing circuitry configured to perform a plurality of operations, the operations comprising
accessing, from a non-transitory computer-readable data store, a plurality of documents, each document of the plurality of documents corresponding to a respective risk fulfillment transaction of a plurality of risk fulfillment transactions, wherein
each respective document of the plurality of documents originated from a respective risk coverage entity of a plurality of risk coverage entities,
for each respective document of the plurality of documents, converting unstructured contents of the respective document into standard formatting,
applying the business ontology to recognize and tag each transaction element of a respective plurality of transaction elements within the respective document with a respective tag of a plurality of tags, each tag corresponding to a respective term of the plurality of terms of the business ontology, and
storing the respective plurality of transaction elements into a plurality of stored transaction elements of the semantic graph according to the plurality of relationships of the semantic ontology,
enhancing a portion of the plurality of stored transaction elements with attributes, wherein the enhancing comprises
analyzing the semantic graph to recognize a plurality of relationships, each relationship being between a respective set of transaction elements from a respective two or more different documents of the plurality of documents, and
updating the semantic graph to capture the plurality of relationships as a plurality of relational links,
presenting, for review at a first display of a first computing device, an interactive graphical user interface comprising, for each respective transaction element of a set of transaction elements of the plurality of stored transaction elements,
a respective value,
a respective text label, the respective text label explanative of the respective tag applied to the respective transaction element, and
at least one respective interactive control configured to enable a user of the first computing device to adjust the respective value,
wherein the set of transaction elements correspond to a subject risk fulfillment transaction of the plurality of risk fulfillment transactions,
receiving, via the interactive graphical user interface, adjustment of the respective value of at least one transaction element of the set of transaction elements,
updating the semantic graph according to the adjustment, and
generating, for review at a second display of a second computing device, a visualization of a plurality of options for fulfilling the subject risk fulfillment transaction on behalf of a client entity, each option of the plurality of options corresponding to a respective subset of transaction elements of the plurality of stored transaction elements, each transaction element of the respective subset of transaction elements corresponding to the subject risk fulfillment transaction”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., fundamental economic principles or practices), “Mental processes – concepts performed in the human mind (e.g., evaluation), and an abstract idea in prong one of step 2A (MPEP 2106.04(a)).
15. This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of claim 1 such as “a system”, “at least one non-transitory computer-readable storage medium”, “a business ontology”, “a semantic ontology”, “a non-transitory computer-readable data store”, “a semantic graph”, “converting unstructured contents of the respective document into standard formatting”, “a first display of a first computing device”, “an interactive graphical user interface”, “at least one respective interactive control”, “a second display of a second computing device”, and “a visualization of a plurality of options for fulfilling the subject risk fulfillment transaction” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, as they do no more than represent the use of a computer as a tool to perform an abstract idea and/or generally linking the use of a judicial exception to a particular technological environment or field of use, they do not improve computer functionality nor improve another technology or technical field. With respect to “at least one respective interactive control configured to enable a user of the first computing device to adjust the respective value”, the claim lacks details regarding what “at least one respective interactive control” and “adjust the respective value” comprises. Therefore, as Applicant has neither placed a restriction on how “at least one respective interactive control” enabling “a user of the first computing device” adjusting “the respective value” is performed nor described how the function is accomplished, the limitations do not integrate the abstract idea into a practical application and does not improve the functioning of a computer, or to another technology or technical field, as it is no more than “apply it” (MPEP 2106.05(f)(1)). And additionally, with respect to “storing the respective plurality of transaction elements into a plurality of stored transaction elements of the semantic graph according to the plurality of relationships of the semantic ontology”, is simply transmitting data; “[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more”, (MPEP 2106.05(f)(2)).
16. When analyzed under step 2B (MPEP 2106.04 II), claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of managing insurance related data from multiple parties for risk fulfillment transaction request using computer technology (e.g., the processor). Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
17. Hence, claim 1 is not patent eligible.
18. The following dependent claims recent additional elements not addressed above:
claim 3 recites “a vector format”;
claim 6 recites “one or more machine learning models trained in a business knowledge” and “one or more artificial intelligence networks fine-tuned in the business knowledge”;
claim 12 recites “natural language processing”; and
claim 13 recites “one or more machine learning models trained with a corpus of risk fulfillment experiential knowledge” and “one or more artificial intelligence networks fine-tuned with the corpus of risk fulfillment experiential knowledge”.
When considered individually, and as a whole, each of these additional elements amount to merely "apply it", as they are merely applying the abstract idea to the technical environment of the vector format, the one or more machine learning models trained in the business knowledge, the one or more artificial intelligence networks fine-tuned in the business knowledge, the natural language processing, the one or more machine learning models trained with the corpus of risk fulfillment experiential knowledge, and the one or more artificial intelligence networks fine-tuned with the corpus of risk fulfillment experiential knowledge.
Dependent claims 2-16 merely expand upon the abstract ideas of the independent claim and are therefore rejected under the same rationale as claim 1.
Conclusion of 35 USC §101
19. The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
20. Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
21. 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.
22. 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.
23. 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.
24. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
25. Claims 1-8 and 10-16 are rejected under 35 U.S.C. 103 as being unpatentable over US11282144B2 to Propati et al. in view of US11797778B2 to Venkateshwaran et al., and US11783427B1 to Yoder et al.
26. As per claim 1:
Propati et al. discloses the following limitations:
A system for extracting, and organizing, and visualizing details of risk fulfillment options provided by multiple third parties in response to a risk fulfillment transaction request, the system comprising: (Col.1, lines 23-26 “The present technology relates to automated systems and methods for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client”, col.1, lines 45-47 “Once an adequate number of quotes are received by the broker, the broker presents them to the client who then accepts one of the quotes”, col.14, lines 25-26 “A method for building and sharing digital submissions for negotiating transactions with a third party”)
at least one non-transitory computer-readable storage medium storing a business ontology comprising a plurality of terms and a plurality of business rules, each business rule defining relationships between a respective set of terms of the plurality of terms (Col/line 3/66-4/2 “All the risk attributes in the system with common core properties are marked as a master attribute. Master attributes can be shared across products”, col.14, lines 61-65 “identifying the set of risk attributes comprises generating attribute mappings of groups of attributes, each attribute mapping linking two or more attributes in the set of risk attributes within a respective group of the groups of attributes”, col.5, lines 9-11 “All the risk attributes identified in the templates/forms are mapped in the tool to define the display, logical, conditional and business rules of the field”)
a hierarchy of risk fulfillment terms (Col.4, lines 5-6 “All the risk attributes required to define a submission are grouped and organized for a product”, col.7, lines 62-64 “Section 503 provides the user with an interface to build a section or a sub-section for logical grouping and organization of risk attributes”)
a plurality of risk fulfillment rules, each risk fulfillment rule applied to at least one term of the hierarchy of risk fulfillment terms (Col.15, lines 3-6 “wherein generating the attribute mappings comprises applying fuzzy logic rules to the set of risk attributes to identify the groups of the two or more attributes”, col.7, lines 46-48 “Section 408 can be used to define the business rules, validation, and conditional behavior of the attribute in context of the application”)
a plurality of relationships between sets of terms of the hierarchy of risk fulfillment terms (Col.3, lines 58-60 “Any new risk attribute being configured, if similar to an existing risk attribute, can be mapped to it and inherit its properties”, col.14, lines 57-59 “wherein one or more attributes of the set of risk attributes are repeated in two or more of the plurality of fields across the plurality of transaction forms”)
processing circuitry configured to perform a plurality of operations, the operations comprising (Col.14, lines 28-29 “identifying, by one or more processors, a plurality of transaction forms associated with a product”, col.12, lines 26-27 “Computing device 1000 can include a central processor 1010 that controls the overall operation of the computing device”)
accessing, from a non-transitory computer-readable data store, a plurality of documents, each document of the plurality of documents corresponding to a respective risk fulfillment transaction of a plurality of risk fulfillment transactions, wherein (Col.1, lines 35-37 “The broker then creates a submission, which is a document identifying the client and the risks it wishes to insure”, col.16, lines 3-4 “identifying a plurality of transaction forms associated with a product of a plurality of insurance products”, col.5, lines 33-35 “The files are uploaded into a temporary document management system with additional metadata properties like product-type, version, and applicable forms/templates”)
for each respective document of the plurality of documents (Col.8, lines 15-18 “The submission forms/templates documents are identified and uploaded into the mapping application as shown at 601. All the risk attributes captured in the template are added in the mapping tool”)
applying the business ontology to recognize and tag each transaction element of a respective plurality of transaction elements within the respective document with a respective tag of a plurality of tags, each tag corresponding to a respective term of the plurality of terms of the business ontology (Col.4, lines 25-29 “ACORD eForms contain a consistent XML (Extensible Markup Language) format, and unique XML tags for each form field, which are referred to as ‘eLabels.’ The eLabels are applied consistently across all ACORD eForms”, col.7, lines 28-32 “As soon as the user selects the attribute in section 402, a fuzzy logic runs to determine if a similar attribute has been mapped earlier. The results of the logic are shown in section 404 with the confidence of the match”)
enhancing a portion of the plurality of stored transaction elements with attributes, wherein the enhancing comprises (Col.7, lines 37-39 “Sections 405 to 409 capture different property details of the risk attribute that will be used to render the attribute on the application”, col.9, lines 3-6 “The application also provides an interface to setup the carrier appetite parameters based on which the submission is analyzed, and appropriate carrier(s) are identified as shown at 802.”)
analyzing the semantic graph to recognize a plurality of relationships, each relationship being between a respective set of transaction elements from a respective two or more different documents of the plurality of documents (Col.14, lines 57-59 “wherein one or more attributes of the set of risk attributes are repeated in two or more of the plurality of fields across the plurality of transaction forms.”, col.15, lines 34-36 “detecting, by the one or more processors within the updated version of the portion of the plurality of transaction forms, a plurality of updated fields;”)
updating the semantic graph to capture the plurality of relationships as a plurality of relational links, (Col.15, lines 1-2 “the attribute mappings are stored within the non-transient database.”, col.8, lines 20-23 “The user can logically link templates based on the data capture logic as shown at 603. This will enable the data to overflow from one form to another and conditionally enable forms based on the data captured.”)
presenting, for review at a first display of a first computing device, an interactive graphical user interface comprising, for each respective transaction element of a set of transaction elements of the plurality of stored transaction elements (Col.14, lines 33-37 “providing, by the one or more processors for presentation at a user interface of a computing device of a user of a plurality of users associated with the product, a graphical user interface for providing inputs for the set of risk attributes”, col.9, lines 25-29 “an eForms viewer is provided (an example page output by the viewer is shown in FIG. 14), which is used to surface ACORD eForms and eLabels to the broker to facilitate the submission creation process”)
a respective value (Col.14, lines 38-41 “receiving, by the one or more processors via the user interface, a plurality of answers, each answer corresponding to a respective risk attribute of the set of risk attributes;”, col.6, lines 63-64 “The final step involves retrieving and binding answers”)
a respective text label, the respective text label explanative of the respective tag applied to the respective transaction element (Col.10, lines 16-20 “eLabel descriptions 900 are provided to the user. The descriptions 900 associate e.g., a question ID, question name, field/display name and ‘tooltip’ (i.e., tip as to what the eLabel information is all about)”, col.15, lines 18-21 “wherein the one or more properties include an attribute label associated with each of the plurality of fields within the plurality of transaction forms.”)
at least one respective interactive control configured to enable a user of the first computing device to adjust the respective value (Col.9, lines 32-34 “the input of data into the eForms is controlled by using custom searchers, custom dropdowns and date controls on the viewer.”)
wherein the set of transaction elements correspond to a subject risk fulfillment transaction of the plurality of risk fulfillment transactions (Col.11, lines 5-7 “To initiate a submission for a client, a broker selects the applicable product or products from a list 940.”)
receiving, via the interactive graphical user interface, adjustment of the respective value of at least one transaction element of the set of transaction elements (Col.14, lines 38-41 “receiving, by the one or more processors via the user interface, a plurality of answers, each answer corresponding to a respective risk attribute of the set of risk attributes;”, col.8, lines 40-43 “The user completes the submission by answering the risk attributes, through the application as shown in the step identified at 702, and the data gets stored in the database 204.”)
Propati et al. does not disclose, however, Venkateshwaran et al., as shown, teaches the following limitations:
a semantic ontology defining risk fulfillment information, the semantic ontology comprising (Col.2, lines 64-67 “defining a semantic ontology using a graph database based on the sentence intents, a multitude of mini-dictionaries and domain attributes”)
a non-transitory computer-readable data store configured to store a semantic graph (Col.5, lines 50-52 “Graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.”)
converting unstructured contents of the respective document into standard formatting (Col.8, lines 52-54 “a corpus of claim notes and other claim documents at the FNOL stage can be first converted to text using optical character recognition (OCR) techniques.”, col.20, lines 60-63 “the classified and tagged sentences are fed to a powerful text search engine (e.g. Apache Solr, Elastic Search, etc.) which performs transforms such as stemming, lemmatization, etc.”)
storing the respective plurality of transaction elements into a plurality of stored transaction elements of the semantic graph according to the plurality of relationships of the semantic ontology (Col.20, lines 60-62 “the classified and tagged sentences are fed to a powerful text search engine (e.g. Apache Solr, Elastic Search, etc.)” col.21, lines 35-36 “all of the above configurations and corresponding links are stored in an ontology graph database”)
updating the semantic graph according to the adjustment (Col.9, lines 47-50 “Reinforcement learning learns patterns of when the users provided positive versus negative feedback, and accordingly tunes the system to provide more meaningful and targeted suggestions.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for defining that the insurance-claims information (lines of business, coverages, claim events) is risk fulfillment information, storing by a non-transitory data store (graph database, e.g., Neo4j) a semantic graph of nodes, edges, and properties, converting unstructured document contents into standardized formats (OCR text, standardized sentence units), storing tagged document elements in a text database while the semantic graph stores the ontology relationships, and updating the system's models and rules (‘778, col.2, lines 64-67; col.5, lines 50-52; col.8, lines 52-54; col.9, lines 47-50; col.20, lines 60-63; col.21, lines 35-36 ).
Neither Propati et al. nor Venkateshwaran et al. disclose, however, Yoder et al., as shown, teaches the following limitations:
each respective document of the plurality of documents originated from a respective risk coverage entity of a plurality of risk coverage entities (Col.10, lines 8-10 “request and automatically transmit a request for quote to any of a variety of user selected markets electronically linked to the systems of the present disclosure”, col.20, lines 15-16 “Two quotes received are shown.”)
generating, for review at a second display of a second computing device, a visualization of a plurality of options for fulfilling the subject risk fulfillment transaction on behalf of a client entity, each option of the plurality of options corresponding to a respective subset of transaction elements of the plurality of stored transaction elements, each transaction element of the respective subset of transaction elements corresponding to the subject risk fulfillment transaction (Col.8, lines 30-34 “FIG. 28 is a graphical user interface of the present disclosure that allows two or any plurality of related proposed tower structures constructed by the user to be compared to one another simultaneously side by side with one another.”, col.26, lines 54-58 “each structure or group is depicted in a column format comprised of a custom tower structure thumbnail for visual reference purposes followed below by a scrollable vertical table that houses each corresponding value according to the left-hand metric panel”, col.16, lines 19-23 “This allows all three parties, the carrier, the broker, which is typically the operator of a system, according to the present disclosure, and actuaries to review and adjust the data of a particular company within the system.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) and methods for graphically creating and displaying complex data of insurance/reinsurance structures in a graphical user interface of Yoder et al. (‘427, col.2, lines 34-36) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for receiving quotes originated from a plurality of markets (e.g., risk coverage entities) and generating a visualization of a plurality of options (alternative coverage structures, quotes) for fulfilling the subject placement on behalf of the client, each option comprising its own subset of coverage elements, reviewable by parties on their own devices and displays (‘427, col.10, lines 8-10, 30-34; col.16, lines 19-23; col.26, lines 54-58).
27. As per claim 2:
Propati et al. discloses the following limitations:
The system of claim 1, wherein the second computing device is the first computing device (Col.3, lines 14-16 “the application 100 is part of the broker's desktop platform and integrates with a policy management system and the Microsoft Office suite.”, col.3, lines 45-49 “In addition, the broker can track quotes and declinations, prepare proposals and bind client selected quotes using the application 100. Real-time views of all placement activity are also provided using the application's 100 user interface (e.g., web pages).”
28. As per claim 3:
Propati et al. does not disclose, however, Venkateshwaran et al., as shown, teaches the following limitations:
The system of claim 1, wherein the operations further comprise, prior to storing the respective plurality of transaction elements into the semantic graph, converting the respective plurality of transaction elements to a vector format (Col.18, lines 25-26 “process 1600 can use a sentence embedding to convert the sentences to a vector.”, col/line 8/66-9/1 “Further transformations, such as word-vectorization, can be performed on these tokens to convert the document to a time series of vectors or tensors.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for converting of extracted elements to vector format prior to downstream storage is disclosed (‘778, col.18, lines 25-26; col/line 8/66-9/1).
29. As per claim 4:
Propati et al. discloses the following limitations:
The system of claim 1, wherein tagging the respective plurality of transaction elements comprises logically applying, to each transaction element of the respective plurality of transaction elements, a respective label corresponding to a respective term of a plurality of terms in the business ontology. (Col.4, lines 25-29 “ACORD eForms contain a consistent XML (Extensible Markup Language) format, and unique XML tags for each form field, which are referred to as ‘eLabels.’ The eLabels are applied consistently across all ACORD eForms.”, col.10, lines 16-20 “eLabel descriptions 900 are provided to the user. The descriptions 900 associate e.g., a question ID, question name, field/display name and ‘tooltip’ (i.e., tip as to what the eLabel information is all about).”)
30. As per claim 5:
Propati et al. discloses the following limitations:
The system of claim 1, wherein enhancing the portion of the plurality of stored transaction elements comprises importing, to each respective transaction element of the portion of the plurality of stored transaction elements, one or more respective characteristics from one or more similar transaction elements determined, according to the plurality of relationships, to be related to the respective transaction element (Col.15, lines 7-10 “wherein mapping the answers to the plurality of fields within the plurality of submission forms comprises applying a given answer of the plurality of answers to two or more fields of the plurality of fields”, col.3, lines 58-60 “Any new risk attribute being configured, if similar to an existing risk attribute, can be mapped to it and inherit its properties.”)
31. As per claim 6:
Propati et al. does not disclose, however, Venkateshwaran et al., as shown, teaches the following limitations:
The system of claim 1, wherein enhancing the portion of the plurality of stored transaction elements comprises applying at least one of i) one or more machine learning models trained in a business knowledge or ii) one or more artificial intelligence networks fine-tuned in the business knowledge to add one or more respective characteristics to each transaction element of the portion of the plurality of stored transaction elements according to the business knowledge (Col.9, lines 4-7 “Machine-learning models (such as, inter alia: deep learning RNN, SVM, GBM, etc.) can be trained with the annotated data to be able to predict suggestions, along with their context, based on claim notes at FNOL stage.”, col.21, lines 55-60 “these classifiers are added as the final stage to the above processing pipeline to automatically tag a chunk of text (for e.g. claim notes) with a list of semantic hashtags, topics, and events; along with temporal information on when the hashtag/topic/event was detected in the document.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for training machine learning models (deep learning RNN, SVM, GBM) in domain knowledge that add characteristics (semantic hashtags, topics, events, temporal information) to the stored elements (‘778, col.9, lines 4-7; col.21, lines 55-60).
32. As per claim 7:
Propati et al. discloses the following limitations:
The system of claim 1, wherein the plurality of documents comprises a plurality of unstructured email documents. (Col.3, lines 33-36 “The application 100 communicates via e-mail, facsimile and/or directly with the insurance carriers and/or intermediaries 114 via available digital means (e.g., through a network or Internet connection).”, col.11, lines 25-28 “In addition to delivering a submission via email, a submission may also be delivered and received via a network connection as a file in submission extensible markup language.”)
33. As per claim 8:
Propati et al. discloses the following limitations:
The system of claim 6, wherein the plurality of operations further comprise, prior to applying the business ontology, identifying, within each document of the plurality of documents, a respective transaction identifier corresponding to the subject risk fulfillment transaction (Col.4, lines 15-16 “The first step is to identify the forms and templates that are used as submission documents for a product.”, col/line 8/67-9/1 “identifying the policies that are expiring and need to be submitted for renewal.”)
34. As per claim 10:
Propati et al. discloses the following limitations:
The system of claim 1, wherein the semantic ontology defines insurance quote information related to at least one type of insurance quote (Col/line 2/65-3/3 “Account executives, brokers and/or account specialists (collectively referred to herein as a “broker” or “brokers”) collaborate to gather the client's risk related data and put together a submission quote proposal, which is sent out to various markets to secure insurance coverage for these risks.”, col.9, lines 53-55 “Table I below lists exemplary insurance products and product categories that are associated with ACORD eForms in the disclosed embodiments.”)
35. As per claim 11:
Propati et al. discloses the following limitations:
The system of claim 1, wherein a portion of the plurality of relational links connect transaction elements related to a same risk fulfillment transaction of the plurality of risk fulfillment transactions. (Col.14, lines 62-65 “each attribute mapping linking two or more attributes in the set of risk attributes within a respective group of the groups of attributes”, col.15, lines 7-10 “wherein mapping the answers to the plurality of fields within the plurality of submission forms comprises applying a given answer of the plurality of answers to two or more fields of the plurality of fields”)
35. As per claim 12:
Propati et al. does not disclose, however, Venkateshwaran et al., as shown, teaches the following limitations:
The system of claim 1, wherein converting the unstructured contents of the respective document into the standard formatting comprises applying natural language processing to the unstructured contents (Col.1, lines 26-29 “The invention is in the field of natural language processing and more specifically a method, system, and apparatus for computerized natural language processing with insights extraction using semantic search.”, col.8, lines 52-54 “a corpus of claim notes and other claim documents at the FNOL stage can be first converted to text using optical character recognition (OCR) techniques.”, col.20, lines 60-63 “the classified and tagged sentences are fed to a powerful text search engine (e.g. Apache Solr, Elastic Search, etc.) which performs transforms such as stemming, lemmatization, etc.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for applying natural language processing (stemming, lemmatization) to the unstructured contents in converting them to standardized form (‘778, col.1, lines 26-29; col.8, lines 52-54; col.20, lines 60-63).
37. As per claim 13:
Propati et al. discloses the following limitations:
The system of claim 1, wherein generating the visualization of the plurality of options for fulfilling the subject risk fulfillment transaction comprises submitting the plurality of options to at least one of i) one or more machine learning models trained with a corpus of risk fulfillment experiential knowledge or ii) one or more artificial intelligence networks fine-tuned with the corpus of risk fulfillment experiential knowledge to obtain an assessment of a respective value of each option of the plurality of options, wherein the corpus of risk fulfillment experiential knowledge comprises fulfillment options and offer acceptances related to a plurality of historic risk fulfillment transaction requests (Col.3, lines 22-25 “STATISTICAL ANALYSIS PLATFORM FOR PREDICTING INSURANCE CARRIERS MOST LIKELY TO ACCEPT A PARTICULAR TYPE OF CLIENT RISK”, col.3, lines 28-31 “AON's GRIP provides web-accessible data to help brokers evaluate which markets will likely have an appetite for a given placement type and which carriers are likely to provide the best value for their clients.”)
38. As per claim 14:
Neither Propati et al. nor Venkateshwaran et al. disclose, however, Yoder et al., as shown, teaches the following limitations:
The system of claim 13, wherein the respective value comprises a relative ranking in view of the plurality of options (Col.26, lines 38-42 “The compare page utilizes the weighted metrics to analyze each of the proposed structures and rank them and provide them by order of preference in color coded fashion with green being the best, amber or yellow being less preferred and red being the least preferred.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) and methods for graphically creating and displaying complex data of insurance/reinsurance structures in a graphical user interface of Yoder et al. (‘427, col.2, lines 34-36) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for disclosing that the assessed value of each option comprises a relative ranking in view of the plurality of options (‘427, col.26, lines 38-42).
39. As per claim 15:
Neither Propati et al. nor Venkateshwaran et al. disclose, however, Yoder et al., as shown, teaches the following limitations:
The system of claim 13, wherein the assessment is based in part on a provisional structure representing risk coverage requirements of the client entity for the subject risk fulfillment transaction (Col.19, lines 45-47 “The systems of the present disclosure produce analytics on the capital efficiency of a proposed structure based on how a cedent organizes its book of business.” col.21, lines 7-11 “FIG. 14 presents a customizable graphical diagram for building new or modifying existing reinsurance constructs, as well as displaying relevant analytics obtained live and dynamically from a backend computation engine using a fixed defined loss(es) set of the present disclosure.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) and methods for graphically creating and displaying complex data of insurance/reinsurance structures in a graphical user interface of Yoder et al. (‘427, col.2, lines 34-36) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for providing assessment based in part on the proposed coverage structure representing the cedent's risk coverage requirements for the subject placement (‘427, col.19, lines 45-47; col.21, lines 7-11).
40. As per claim 16:
Neither Propati et al. nor Venkateshwaran et al. disclose, however, Yoder et al., as shown, teaches the following limitations:
The system of claim 1, wherein generating the visualization of the plurality of options for fulfilling the subject risk fulfillment transaction comprises arranging the plurality of options as a color-coded mud map (Col.26, lines 38-42 “The compare page utilizes the weighted metrics to analyze each of the proposed structures and rank them and provide them by order of preference in color coded fashion with green being the best, amber or yellow being less preferred and red being the least preferred.”, col.26, lines 54-58 “each structure or group is depicted in a column format comprised of a custom tower structure thumbnail for visual reference purposes followed below by a scrollable vertical table that houses each corresponding value according to the left-hand metric panel”, col/line 26/67-27/3 “To the right of each metric value is a weighted average color-coded ranking system, also defined by the user previously according to their desired weights.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23) and methods for graphically creating and displaying complex data of insurance/reinsurance structures in a graphical user interface of Yoder et al. (‘427, col.2, lines 34-36) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for arranging by the compare page the plurality of options as that a color-coded (e.g., green, amber, red) schematic layout of option thumbnails and ranked values enabling the user to quickly visualize the relative merit of every option (‘427, col.26, lines 38-42, 54-58; col/line 26/67-27/3).
41. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over US11282144B2 to Propati et al. in view of US11797778B2 to Venkateshwaran et al., US11783427B1 to Yoder et al., and US10169454B2 to Ait-Mokhtar et al.
42. As per claim 9:
Neither Propati et al. nor Venkateshwaran et al. or Yoder et al. disclose, however, Ait-Mokhtar et al., as shown, teaches the following limitations:
The system of claim 1, wherein the business ontology is stored as a resource description framework (RDF) (Col.10, lines 32-35 “A triple pattern can be seen as a conjunction of an rdfs:domain axiom having a single domain class and an rdfs:range axiom with a single class or a union of classes”, col.8, lines 23-24 “N-Triples is a line-based, plain text format for encoding an RDF graph.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a method for extracting domain specific insights from a corpus of files containing large documents, where the insights may be related to small snippets of the documents of Venkateshwaran et al. (‘778, col.4, lines 20-23), methods for graphically creating and displaying complex data of insurance/reinsurance structures in a graphical user interface of Yoder et al. (‘427, col.2, lines 34-36), and a method for extracting a relations graph that includes providing an ontology of elements in the form of a graph of Ait-Mokhtar et al. (‘454, col.2, lines 27-28) with the teaching of Propati et al. for implementing and managing the submission, quote, proposal and binding stages (i.e., placement process) for securing insurance coverage for a client (‘144, col.2, lines 47-49) for storing the ontology in resource description framework form and its triple patterns are expressed as RDFS domain axioms, and the graphs instantiating it are encoded in the RDF N-Triples format (‘454, col.8, lines 23-24; col.10, lines 32-35).
Conclusion
43. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US11734579B2 – Neelamana – Discloses a method for extracting data from formatted and non-formatted documents using machine learning algorithms to answer relevant questions, wherein a machine learning algorithm is an algorithm that is able to use data to progressively improve performance of one or more tasks without being explicitly programmed to perform the task(s).
US10157347B1 – Kasturi et al. – Discloses a platform for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results wherein the platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine.
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/AMANULLA ABDULLAEV/Examiner, Art Unit 3692
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692
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