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 .
Status of the Claims
The office action is being examined in response to the amendments submitted by the applicant on January 6, 2026.
Claims 1, 3, 5, 7, 15-18, and 20 have been amended and are hereby entered.
Claims 21-25 have been new added and are hereby entered.
Claims 1-25 are pending and have been examined.
This action is made FINAL.
The examiner would like to note that this application is now being handled by examiner Michael Anderson.
Response to Arguments
Applicant's arguments filed January 6, 2026 have been fully considered but they are not persuasive.
Applicant argues that the recitation of “automatically accessing, via one or more of an Application Programming Interface (API) or Robotic Process Automation (RPA), the insurance company computer system using the user verification data to retrieve the certain insurance data in real-time or near real-time” specifies “a particular technical implementation using APIs or RPA technology to automatically interface with third-party computer systems” and constitutes “a specific technological solution to the technical problem of accessing disparate insurance company systems.”
The Examiner respectfully disagrees. This argument is not persuasive for the following reasons:
First, the claim does not recite a “particular” API architecture, a specific RPA implementation, a novel protocol, or any unconventional technical mechanism for interfacing with third-party systems. Rather, the claim recites the use of APIs and/or RPAs — which are, by their very definition, general-purpose tools designed for the purpose of programmatically accessing remote computer systems and retrieving data. An API is a standard interface for machine-to-machine communication; RPA is a known category of software that automates interactions with computer interfaces. Reciting these tools by their generic category names (without specifying a particular architecture, protocol, data structure, or implementation detail) is analogous to reciting “using a web browser to access a website” — it invokes a known tool for its known purpose at the highest level of generality.
Second, Applicant characterizes the problem as “accessing disparate insurance company systems,” but this is a business/logistical problem (how to obtain insurance data from various carriers), not a technical problem rooted in computer science or network technology. The technical means of accessing remote systems via APIs has long been established. The claimed “solution” is merely applying that known technical capability to the business domain of insurance verification. See Affinity Labs of Texas, LLC v. DIRECTV, LLC, 838 F.3d 1253, 1258–59 (Fed. Cir. 2016) (applying known technology to a particular field of use does not constitute a technical improvement); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327 (Fed. Cir. 2017) (“Adding one abstract idea…to another abstract idea…does not render the claim non-abstract.”).
Third, “real-time or near real-time” describes the speed at which the generic computer system performs the abstract idea of retrieving insurance data. All networked API calls inherently occur in real-time or near real-time — there is no technical innovation in performing an API call quickly, as that is the fundamental nature of programmatic network communication. Performing an abstract idea faster using a computer does not integrate the abstract idea into a practical application. See Bancorp Services, L.L.C. v. Sun Life Assurance Co. of Canada, 687 F.3d 1266, 1278 (Fed. Cir. 2012).
Accordingly, the API/RPA limitation does not impose a meaningful limit on the abstract idea beyond generally linking it to a technological environment and constitutes mere instructions to “apply” the abstract idea using conventional automation tools.
Applicant argues that “extracting text from the certain insurance data received from the insurance company computer system using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques; and normalizing the extracted text into a structured data format using one or more AI or ML techniques” specifies “particular AI/ML processing steps that transform unstructured insurance data into structured formats, representing a specific technological improvement in data processing.”
The Examiner respectfully disagrees. This argument is not persuasive for the following reasons:
the claim does not recite any “particular” AI/ML processing steps. It recites “one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques” — a genus-level invocation that encompasses the entirety of the AI/ML field without identifying any specific algorithm, model architecture, training methodology, neural network configuration, feature engineering approach, or technical parameter. This is the functional equivalent of claiming “using software” or “using a mathematical technique.” The claimed limitation tells the reader what is accomplished (text extraction and normalization) but not how the AI/ML technically achieves it in a manner that differs from conventional approaches.
the specification does not describe any specific, novel AI/ML implementation for text extraction or normalization. The specification merely states the system “extracts text from carrier websites or such documents, preferably via one or more AI/ML techniques…and then preferably normalizes such information into a structured data format” (Spec., p. 29). The specification’s disclosure of AI/ML (FIGS. 3–4) is entirely generic — describing standard neural network concepts (input layers, hidden layers, output layers, synaptic weights, supervised/unsupervised/reinforcement learning) at a textbook level without any application-specific detail. There is no disclosure of how AI/ML is specifically configured, trained, or architected for the particular task of insurance document processing. Without such specificity in either the claim or the specification, the recitation of “AI/ML techniques” amounts to a black-box invocation.
Applicant argues that Claim 18’s recitation that “the monitoring system automatically performs, without human intervention, the steps of sending data requesting certain insurance data, receiving the requested certain insurance data, and determining whether the client has adequate insurance coverage in real-time or near real-time” imposes “meaningful limits on the alleged abstract idea by requiring specific technological implementations rather than generic computer use.”
The Examiner respectfully disagrees. Reciting that a computer performs steps “automatically” and “without human intervention” is the very definition of implementing an abstract idea on a generic computer — it states that the computer, rather than a human, performs the process. This is precisely the type of “apply it” language that the Supreme Court found insufficient in Alice Corp. v. CLS Bank Int’l. The fact that a computer performs a task without human intervention is not a “specific technological implementation” — it is the fundamental characteristic of any automated computer process. Every computer program operates “without human intervention” once initiated. This limitation does not specify how the computer achieves the result in a technically novel way; it merely states that the computer does it.
Furthermore, Applicant’s own specification confirms that the “improvement” is to the speed and efficiency of the business process (replacing 15–30 minute phone calls), not to any underlying computer technology. An improvement to the efficiency of a business process accomplished by automating it on a generic computer is not a technological improvement for § 101 purposes.
Applicant argues that “automatically accessing, via one or more of an Application Programming Interface (API) or Robotic Process Automation (RPA), the insurance company computer system using the user verification data to retrieve the certain insurance data in real-time or near real-time…represents a specific technological solution that enables automated, real-time verification that was not previously feasible.”
The Examiner respectfully disagrees. This argument is not persuasive for the following reasons:
First, the assertion that automated real-time verification “was not previously feasible” is not supported by evidence. The specification states the prior process was “manual” ; meaning humans performed it by phone or by manually accessing databases. That a process was previously done manually does not establish that automating it via API/RPA is unconventional or constitutes an inventive concept. APIs and RPAs have been in widespread use for decades specifically for the purpose of automating formerly manual data retrieval tasks.
Second, the claim does not recite a “specific technological solution” — it recites a generic category of tools (API or RPA) used for their standard purpose. No particular API protocol, endpoint architecture, authentication mechanism, data exchange format, or RPA workflow that would be unconventional is claimed. The “inventive concept” inquiry at Step 2B asks whether the additional elements, individually or in ordered combination, are something other than what is well-understood, routine, and conventional in the field. Using an API to access a remote system and retrieve data is the foundational, conventional function of APIs.
Third, the specification itself treats API/RPA as off-the-shelf tools, stating the system “accesses the carrier’s website or database through an RPA, or API” (Spec., p. 2) without any suggestion that its use of APIs or RPAs differs from conventional implementations. There is no disclosure of a custom API architecture, a novel RPA technique, or any technical advance in how APIs or RPAs function.
Applicant argues that AI/ML text extraction and normalization “represent specific technological processing steps that transform unstructured insurance data into structured formats” and that this “addresses the technical challenge of processing disparate data formats from multiple insurance providers.”
The Examiner respectfully disagrees. This argument is not persuasive for the following reasons:
First, as discussed above, the claim does not recite “specific technological processing steps.” It recites the generic use of “one or more AI or ML techniques” — a black-box invocation that does not identify any specific algorithm, model, or technical approach. Because the claim does not identify any particular implementation — only a result achieved by some unspecified AI/ML technique — the claim sweeps in all conventional AI/ML text extraction and normalization methods, which are well-understood, routine, and conventional in the data processing field.
Second, the “challenge of processing disparate data formats from multiple insurance providers” is a data heterogeneity problem — a common data engineering challenge that AI/ML tools routinely address across all industries (healthcare, finance, legal, etc.). The claim does not recite any insurance-domain-specific AI/ML innovation — it merely applies generic AI/ML tools to insurance data. This is analogous to claiming “using AI/ML to process medical records” or “using AI/ML to extract financial data” — the application domain does not transform generic AI/ML into an inventive concept.
Third, the specification passage cited by Applicant (“the system extracts text from carrier websites or such documents, preferably via one or more AI/ML techniques…and then preferably normalizes such information into a structured data format” (Spec., p. 29)) does not describe a specific or unconventional AI/ML implementation. It merely confirms the system uses AI/ML generically for text extraction and normalization — without disclosing what type of AI/ML, how it is trained, what architecture is used, or how it differs from conventional text extraction/normalization tools. This passage does not provide evidentiary support for an inventive concept under Berkheimer.
Applicant argues Claim 18’s automated, real-time monitoring “represents a specific technological improvement over prior manual processes” and cites the specification stating “this is a significant advantage over prior art techniques for monitoring a client’s continuing insurance requirements compliance, which was labor intensive, time consuming and costly.”
The Examiner respectfully disagrees. The fact that a computer performs a task faster and more cheaply than a human does not establish patent eligibility. Virtually every computer automation of a manual process is faster, less labor intensive, and less costly — that is the fundamental purpose of computing. An improvement to the speed and cost of performing a business process (insurance monitoring) is not a technological improvement — it is a business improvement achieved through generic automation.
Furthermore, Applicant’s additional assertion that “the system removes the requirement for the human that would be interpreting the insurance data to have extensive insurance knowledge” describes the elimination of a need for human expertise — i.e., replacing human judgment with computer execution. This is precisely the “mental process performed by a computer” that the MPEP identifies as an abstract idea. The fact that AI/ML can perform evaluations that previously required a trained human does not make the claim patent-eligible; rather, it confirms the claim is directed to automating a mental process.
Response to Applicant’s Arguments Regarding Examiner’s Characterizations
(1) Regarding “Pen and Paper” / Mental Process
Applicant argues that “independent claims 1, 16 and 18, as currently recited, do not recite steps that could practically be performed by a human with pen and paper” because “API/RPA integration, real-time processing, and AI/ML text extraction and normalization…require specific computer implementation and cannot be performed manually.” Applicant cites SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) for the proposition that “[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind.”
The Examiner respectfully disagrees. This argument conflates the abstract idea with the additional elements. Under the MPEP/PEG framework, the analysis separates the judicial exception (the abstract idea) from the additional elements (the technological implementation). The abstract idea recited in the claims — verifying whether insurance coverage is adequate for a transaction by (1) obtaining authorization, (2) requesting insurance data, (3) receiving that data, (4) reading/extracting the relevant information, (5) organizing that information, and (6) evaluating it against requirements — is a process that can be (and historically was) performed by a human. The specification itself confirms this: “verification of in-force automobile insurance during the purchase of a new car, is a manual operation requiring either manually accessing a database or, worse, obtaining verification through a telephone call” (Spec., Background).
The fact that the claim recites performing these steps via API/RPA and AI/ML does not eliminate the underlying abstract idea — it merely automates it. The 2019 PEG October 2019 Update, Section III.A.2, now integrated into the MPEP states: “If a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes grouping unless the claim cannot practically be performed in the mind.” Here, but for the recitation of APIs, RPAs, and AI/ML (generic computing tools), the core steps of the claim (obtain data, read it, organize it, evaluate adequacy) can practically be performed in the human mind (as the specification confirms was done before the invention).
Furthermore, SRI International is distinguishable. In SRI, the claims recited a specific network monitoring technique involving the creation of network monitors that analyzed network packet data using particular hierarchical algorithms to detect intrusions — a technological solution to a technological problem inherent in computer networks. The claims here do not recite a specific algorithm, a particular data processing architecture, or a technological solution to a problem rooted in computer technology. The “problem” solved is a business problem (slow, manual insurance verification), and the “solution” is generic automation using known tools.
Applicant argues that “a human cannot mentally execute machine learning algorithms in real-time to perform text extraction and normalization.” This conflates the speed at which AI/ML operates with whether the underlying cognitive task is a mental process. A human can extract text from an insurance document and organize it into a structured format — this is exactly what insurance agents and F&I representatives have done for decades. The fact that AI/ML performs this task computationally faster does not change the nature of the underlying process. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was confirmed by the Supreme Court” in Gottschalk v. Benson).
(2) Regarding “Not Insignificant Extra-Solution Activity”
Applicant argues that “the API/RPA integration and AI/ML processing limitations are not insignificant extra-solution activity, but rather core technological features that enable the claimed functionality.”
The Examiner acknowledges that these limitations are not characterized solely as extra-solution activity. However, even when characterized as part of the “core” of the claim, these limitations still do not integrate the abstract idea into a practical application because they are recited at such a high level of generality that they amount to mere instructions to “apply” the abstract idea using generic tools. The determination of whether a limitation integrates an abstract idea into a practical application does not turn solely on whether it is “core” to the functionality, but on whether it imposes a meaningful limit beyond mere automation. As discussed above, reciting “via API or RPA” and “using AI/ML” at the genus level without technical specificity is the functional equivalent of reciting “using a computer” — it invokes known categories of technology without specifying a particular implementation that would demonstrate a technical improvement.
(3) Regarding Specification Support for Technical Implementation
Applicant cites the specification’s description of “Artificial Intelligence and/or Machine Learning (ML) techniques” and “an AI system (e.g., an Expert System) that implements machine learning and artificial intelligence algorithms” (Spec., p. 18) as demonstrating “specific technological implementations, not generic computer components.”
The Examiner has reviewed this portion of the specification and finds it does not support Applicant’s characterization. The specification passage in question (and the associated FIGS. 3–4) describes AI/ML at a textbook level of generality — defining standard concepts such as neural networks, supervised learning, unsupervised learning, reinforcement learning, model parameters, hyperparameters, activation functions, etc. — without connecting these concepts to any specific implementation for insurance data extraction or normalization. The specification does not disclose:
What type of AI/ML model is used for text extraction (e.g., a specific transformer architecture, a specific CNN for document layout analysis, a particular NER model);
How the model is trained (e.g., on what training data, with what loss function, with what hyperparameters);
What the normalization process specifically entails (e.g., what structured schema is used, what mapping rules are applied, what entity types are extracted);
How the AI/ML implementation differs from or improves upon conventional OCR/NLP tools; or
Any benchmarks, comparisons, or technical metrics demonstrating improvement over prior AI/ML approaches.
Applicant argues that dependent claims 3, 5, and 15, as amended, recite “specific AI/ML applications rather than generic AI usage.”
The Examiner respectfully disagrees. These dependent claims merely specify the purpose for which generic AI/ML is applied:
Claim 3: “the one or more AI or ML techniques are further utilized to identify one or more instances of insufficient insurance coverage” — specifies a purpose (identifying insufficient coverage) but not a specific AI/ML technique.
Claim 5: “the one or more AI or ML techniques are further utilized by the verification system for analyzing historical data to determine the one or more insurance products” — specifies a purpose (analyzing historical data for product recommendation) but not a specific AI/ML technique.
Claim 15: “the one or more AI or ML techniques access external databases including public records to determine the change of life status for the client” — specifies a data source (external databases/public records) and a purpose (determining life status changes) but not a specific AI/ML technique.
In each case, the claims define what AI/ML does (its business function), not how it does it (its technical implementation). Specifying the business purpose of an AI/ML tool does not transform generic AI/ML recitation into a “specific AI/ML application” for § 101 purposes. A “specific AI/ML application” would require, for example, recitation of a particular model type, architecture, algorithm, training approach, or technical configuration that provides a specific technical advantage. None of these claims provide such specificity.
For the foregoing reasons, Applicant’s amendments and arguments do not overcome the rejection under 35 U.S.C. § 101. The claims remain directed to the abstract idea of verifying and monitoring adequacy of insurance coverage for transactions — a method of organizing human activity (fundamental economic practice/commercial interaction) and mental process — implemented using generically-recited computer components (processors, memory, networks), generic automation tools (APIs, RPAs), and generically-invoked AI/ML, none of which integrate the abstract idea into a practical application or provide significantly more.
The rejection of Claims 1–25 under 35 U.S.C. § 101 is MAINTAINED.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 21 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 21 depends from Claim 1 and recites:
“wherein the verification: extracts information from one or more sources via one or more AI/ML techniques; and normalizes such information into a structured data format.”
Claim 1 already recites:
“extracting text from the certain insurance data received from the insurance company computer system using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques; and normalizing the extracted text into a structured data format using one or more AI or ML techniques.”
Claim 21’s limitations appear to be coextensive with (or broader than) the extraction and normalization already required by Claim 1:
Claim 1
Claim 21
“extracting text”
“extracts information” (broader — information / text)
“from the certain insurance data received from the insurance company computer system”
“from one or more sources” (broader — any source / insurance company system)
“using one or more AI or ML techniques”
“via one or more AI/ML techniques” (same)
“normalizing the extracted text into a structured data format”
“normalizes such information into a structured data format” (same/broader)
Since Claim 21 recites the same operations but with broader parameters (“information” vs. “text”; “one or more sources” vs. “the insurance company computer system”), it does not further restrict the scope of Claim 1. A dependent claim that is broader in scope than its parent fails to further limit and violates § 112(d).
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-25 are directed to a systems and methods, which are/is one of the statutory categories of invention. (Step 1: YES).
Claim 1 recites the abstract idea of verifying adequacy of insurance coverage for a contemplated transaction, which is a method of organizing human activity — specifically, a fundamental economic practice and/or commercial/legal interaction (i.e., insurance underwriting, verification of contractual compliance).
The following limitations, under their broadest reasonable interpretation, recite the abstract idea:
“sending…data indicative of a contemplated transaction between the client and the business entity”
This is data gathering related to a commercial transaction — transmitting transaction information for purposes of insurance verification is a step in the fundamental economic practice of verifying insurance.
“sending…a data packet requesting electronic authorization for the verification system to access the insurance company computer system for verifying adequacy of insurance coverage for the contemplated transaction”
This is a request for permission to access insurance records — an administrative/organizational step in the insurance verification process.
“sending, from the client computing device to the verification system data enabling the verification system to access the insurance company computer system (user verification data) for verifying adequacy of insurance coverage for the contemplated transaction”
This is providing credentials/authorization — an organizational step in the commercial process of verifying insurance.
“sending, from the verification system to insurance company computer system, data requesting certain insurance data relating to the client utilizing the user verification data”
This is requesting insurance records — a data gathering step in the insurance verification process.
“sending, from the insurance company computer system to the verification system, the requested certain insurance data”
This is receiving insurance data — data gathering/extra-solution activity in support of the insurance verification process.
“determining, by the verification system, upon analysis of the certain insurance data, whether the client has adequate insurance coverage for the contemplated transaction”
This is the core commercial interaction — evaluating insurance data against transaction requirements to determine adequacy..
“extracting text from the certain insurance data received from the insurance company computer system using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques”
This recites extracting/reading information from insurance documents —merely automated by generically-recited AI/ML.
“normalizing the extracted text into a structured data format using one or more AI or ML techniques”
The claim, taken as a whole, recites the concept of: receiving transaction data, requesting and obtaining authorization to access insurance records, retrieving insurance data, reading/extracting information from that data, organizing it, and evaluating whether insurance coverage is adequate for the transaction. This is squarely within “certain methods of organizing human activity” (commercial/legal interactions, fundamental economic practices) (Step 2A-Prong 1: YES. The claims recite an abstract idea)
The claim recites the following additional elements beyond the abstract idea:
“A computer-implemented method”
“via a third party verification computer system (verification system)”
“a computer system associated with the business entity”
“via a communications network”
“a computing device associated with the client”
“the insurance company computer system”
“automatically accessing, via one or more of an Application Programming Interface (API) or Robotic Process Automation (RPA), the insurance company computer system using the user verification data to retrieve the certain insurance data in real-time or near real-time”
“using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques” (recited twice for extracting and normalizing)
These additional elements do not integrate the abstract idea into a practical application for the following reasons:
Generic computer components used as tools: The “verification computer system,” “computer system associated with the business entity,” “computing device associated with the client,” “insurance company computer system,” and “communications network” are recited at a high level of generality and amount to merely implementing the abstract idea on generic computer systems communicating over a network. The specification confirms these are general-purpose computers (Spec., FIG. 2, ¶¶ describing computing device 200 as any type of computer system — “personal computers, work stations, smart phone devices, tablets,” etc.).
API/RPA — mere automation of data retrieval: The limitation reciting “automatically accessing, via one or more of an Application Programming Interface (API) or Robotic Process Automation (RPA)…to retrieve the certain insurance data in real-time or near real-time” does not recite a particular or unconventional API architecture, a specific RPA implementation, or any technical improvement to how APIs or RPAs function. Rather, it merely automates the pre-existing manual process of accessing an insurance carrier’s website to retrieve policy data. The specification itself acknowledges this replaces a “manual operation requiring either manually accessing a database or, worse, obtaining verification through a telephone call” (Spec., Background). Using an API or RPA to access a remote computer system and retrieve data is a generic, well-known function of these tools. This amounts to mere instructions to “apply” the abstract idea using conventional automation tools. See Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) (finding that automating a formerly manual process using generic technology does not transform an abstract idea into patent-eligible subject matter).
AI/ML — generic invocation without technical specificity: The claim recites “using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques” for text extraction and normalization. However, no specific AI/ML model, architecture, training methodology, algorithm, or technical improvement to AI/ML processing is claimed. The claim does not recite what type of ML technique is used (e.g., a specific neural network architecture, a particular NLP model, a defined training process), nor does it claim how AI/ML improves the functioning of the computer itself. Rather, AI/ML is invoked as a black-box tool to perform the cognitive tasks of reading and organizing data — tasks a human could perform — merely at computer speed. This is analogous to reciting “using a calculator” to perform mathematical operations. See TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere generic recitation of technology without specific technical improvement is insufficient).
“Real-time or near real-time” — speed of execution: The recitation of performing the retrieval in “real-time or near real-time” merely describes the speed at which the generic computer performs the abstract idea. Performing an abstract idea faster on a computer does not integrate it into a practical application. See Bancorp Services, L.L.C. v. Sun Life Assurance Co. of Canada, 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”).
No improvement to computer technology: The claim does not recite an improvement to the functioning of a computer, network, or any particular technology. Rather, it uses computers, networks, APIs, RPAs, and AI/ML as tools to perform the business process of insurance verification more efficiently. The alleged improvement is to the business process itself (faster, automated insurance verification), not to any underlying technology.
Data gathering and output: The “sending” steps constitute insignificant extra-solution activity (data gathering and data transmission) that do not impose meaningful limits on the abstract idea. See Mayo Collaborative Services v. Prometheus Labs., Inc., 566 U.S. 66, 79 (2012); MPEP § 2106.05(g).
Accordingly, the additional elements, individually and in ordered combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The additional elements, considered individually and in ordered combination, do not provide “significantly more” than the abstract idea:
Generic computer systems communicating over a network: Sending and receiving data between computers over a network is well-understood, routine, and conventional. See Symantec, 838 F.3d at 1321 (receiving or transmitting data over a network); OIP Technologies, 788 F.3d at 1363 (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); MPEP § 2106.05(d)(II) (receiving or transmitting data over a network; electronic recordkeeping; storing and retrieving information in memory).
APIs for accessing remote systems and retrieving data: Using APIs to programmatically access remote computer systems and retrieve data is a well-understood, routine, and conventional function of networked computing. The specification itself does not describe the API/RPA implementation as unconventional but rather uses these as off-the-shelf tools for their intended purpose (accessing carrier websites/databases). See Specification describing “an API ecosystem comprising API & data catalogues” without any assertion of technical novelty in the API itself.
RPA for automating web-based interactions: Robotic Process Automation is, by definition, a conventional technology for automating routine tasks previously performed manually on computer interfaces. The specification confirms RPA is used in its conventional sense — to automate access to carrier websites using stored credentials.
AI/ML for text extraction and normalization: The generic use of AI/ML for extracting text from documents (OCR, NLP-based extraction) and normalizing/structuring data are well-understood, routine, and conventional applications of AI/ML in the data processing field. The specification provides no evidence that a novel or unconventional AI/ML technique is employed; it merely references “one or more AI or ML techniques” generically. See Versata Dev. Group, Inc. v. SAP America, Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015). The specification’s description of AI/ML (Spec., FIGS. 3–4, associated paragraphs) is entirely generic, describing standard neural network architectures, supervised/unsupervised/reinforcement learning, and standard hardware — none of which is claimed with any specificity.
“Real-time or near real-time” retrieval: Computer systems inherently operate in real-time or near real-time when performing automated data retrieval. This does not add significantly more.
The claim amounts to applying the abstract idea of insurance verification using generic computing tools (computers, networks, APIs, RPAs, AI/ML) operating in their conventional manner. The claim does not recite any specific technical implementation that would constitute an “inventive concept.”
Claim 1 is NOT patent eligible under 35 U.S.C. § 101.
(Step 2B: NO. The claims do not provide significantly more)
DEPENDENT CLAIMS 2–15 (depending from Claim 1)
Claim 2
Claim 2 further recites that the data packet is “one of a Short Message Service (SMS), Multimedia Messaging Service (MMS) or email message having an HTML link (Hyperlink) to a web page associated with the verification system such that user selection of the Hyperlink causes a GUI to be generated on the client computing device enabling the user to input information.”
The additional elements are: SMS/MMS/email messaging, an HTML Hyperlink, and a GUI generated on the client computing device.
This limitation further defines the particular form of data transmission and user interface for collecting authorization data — it is insignificant extra-solution activity (data gathering) and recites well-understood, routine, conventional technology (sending text messages/emails with hyperlinks, generating web-based GUIs). This does not integrate the abstract idea into a practical application nor provide significantly more. See MPEP § 2106.05(g); Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1328–29 (Fed. Cir. 2017) (web-based forms for collecting data are conventional).
Claim 2 is NOT patent eligible.
Claim 3
Claim 3 recites that “the one or more AI or ML techniques are further utilized to identify one or more instances of insufficient insurance coverage required by the contemplated transaction.”
This further defines the abstract idea — determining instances of insufficient coverage is an evaluation/judgment (mental process) and a step in the fundamental economic practice of insurance verification. The generic invocation of “AI or ML techniques” to perform this evaluation does not change the § 101 analysis. No specific algorithm, model architecture, or technical improvement is recited.
Claim 3 is NOT patent eligible.
Claim 4
Claim 4 recites “determining by the verification system one or more insurance products for providing sufficiency of insurance coverage for the contemplated transaction without client intervention whereby the determined one or more insurance products are provided for client selection on the GUI of the client computing device.”
This limitation recites a commercial activity — recommending insurance products to cure coverage deficiencies — which is a fundamental economic practice (insurance sales/marketing). The additional element of displaying recommendations “on the GUI of the client computing device” is insignificant extra-solution activity (outputting results) performed on a generic display. “Without client intervention” merely describes automation of the business process on a generic computer. This does not integrate the abstract idea nor provide significantly more.
Claim 4 is NOT patent eligible.
Claim 5
Claim 5 recites that “the one or more AI or ML techniques are further utilized by the verification system for analyzing historical data to determine the one or more insurance products.”
This further narrows the abstract idea — using historical data to recommend products is a mental process (evaluation/judgment based on experience) and a fundamental economic practice (actuarial analysis, product recommendation). Generic AI/ML is invoked as a tool; no specific algorithm or improvement to AI/ML is claimed.
Claim 5 is NOT patent eligible.
Claim 6
Claim 6 recites “enabling the business entity computer system to have user access to electronic data relating to insurance data for one or more clients relating to either a respective contemplated transaction or completed transaction…wherein such electronic data indicates whether a respective client has adequate insurance coverage.”
This is insignificant extra-solution activity — providing access to results/data — and further defines the commercial interaction (sharing verification results with the business entity). The additional element of “the business entity computer system” having “user access to electronic data” is generic data access/display on a computer. This does not integrate nor provide significantly more.
Claim 6 is NOT patent eligible.
Claim 7
Claim 7 recites that “the verification system utilizes the one or more AI or ML techniques to analyze the structured data format to determine whether the client has adequate insurance coverage for the contemplated transaction.”
This further defines the abstract idea (analyzing structured data to make an adequacy determination — a mental process/evaluation). The generic invocation of AI/ML does not change the analysis. No specific algorithm, technical improvement, or unconventional implementation is recited.
Claim 7 is NOT patent eligible.
Claim 8
Claim 8 recites that “the verification system further causes the GUI provided on the display of the business entity computer system to indicate one or more instances of insufficient insurance coverage.”
The additional element is displaying results on a GUI of the business entity computer system. This is insignificant post-solution activity (displaying results of the abstract analysis). See MPEP § 2106.05(g). Generic display of information on a computer screen does not integrate the abstract idea nor provide significantly more.
Claim 8 is NOT patent eligible.
Claim 9
Claim 9 recites “the verification system causes a display to be generated on the GUI of the client computing device to display whether a respective client has adequate insurance coverage.”
The additional element is generating a display on the GUI of the client computing device. This is insignificant post-solution activity (outputting results). See analysis of Claim 8 above.
Claim 9 is NOT patent eligible.
Claim 10
Claim 10 recites “the contemplated transaction relates to either a vehicle or real estate property.”
This is a field-of-use limitation that merely restricts the type of transaction to which the abstract idea is applied. Field-of-use limitations do not integrate an abstract idea into a practical application. See MPEP § 2106.05(h); Bilski v. Kappos, 561 U.S. 593, 612 (2010).
Claim 10 is NOT patent eligible.
Claim 11
Claim 11 recites that “the verification system further determines…for the completed client transaction…whether there are one or more changes to the client’s insurance policy that provides inadequate insurance coverage for the completed transaction such that electronic notification is provided from the verification system to the business entity computer system indicating such inadequate insurance coverage.”
This recites ongoing monitoring and notification of insurance compliance — a fundamental economic practice (insurance monitoring/compliance management) and mental process (evaluating whether policy changes cause non-compliance). The additional elements of electronic communication with the insurance company computer system and electronic notification to the business entity computer system are generic data transmission activities.
Claim 11 is NOT patent eligible.
Claim 12
Claim 12 recites “the verification system further provides to the client’s computing device electronic notification indicating inadequate insurance coverage.”
The additional element is sending electronic notification to the client’s computing device — insignificant post-solution activity (transmitting results) and well-understood, routine, conventional activity (sending electronic notifications/alerts). See MPEP § 2106.05(d)(II).
Claim 12 is NOT patent eligible.
Claim 13
Claim 13 recites “the verification system further determines…if there has been a change of life status for the client that adversely effects the client’s insurance policy for the completed transaction such that electronic notification is provided…to the business entity computer system indicating such inadequate insurance coverage.”
Determining a “change of life status” and its effect on insurance adequacy is a mental process (evaluation/judgment) and a commercial interaction (insurance compliance monitoring). The additional element of electronic notification to the business entity computer system is generic data output.
Claim 13 is NOT patent eligible.
Claim 14
Claim 14 recites “the verification system further provides electronic notification to the client’s computing device of the inadequate insurance coverage when it is determined a change of life status for the client adversely effects the client’s insurance policy.”
Same analysis as Claims 12 and 13 — electronic notification to the client’s computing device is generic, routine data transmission.
Claim 14 is NOT patent eligible.
Claim 15
Claim 15 recites “the one or more AI or ML techniques access external databases including public records to determine the change of life status for the client.”
This recites data gathering from external sources (public records) to make a determination — this is insignificant extra-solution activity (data gathering from external databases including public records) in support of the mental process/commercial interaction. The generic use of AI/ML to access databases does not transform the claim. Accessing publicly available databases to gather information is well-understood, routine, and conventional. See OIP Technologies, 788 F.3d at 1363.
Claim 15 is NOT patent eligible.
INDEPENDENT CLAIM 16
Step 1 — Statutory Category
Claim 16 recites a “verification computer system…comprising: a memory configured to store instructions; a processor disposed in communication with said memory,” which falls within the statutory category of a machine/apparatus.
Step 2A, Prong 1 — Judicial Exception
Claim 16 recites substantially the same abstract idea as Claim 1 — verifying adequacy of insurance coverage for a contemplated transaction — which is a method of organizing human activity (fundamental economic practice/commercial interaction) and mental process. The functional limitations recited by the processor include:
Receiving transaction data
Sending authorization requests to a client
Receiving authorization data from the client
Sending data requests to the insurance company
Receiving insurance data
Determining adequacy of insurance coverage
Extracting text from insurance data using AI/ML
Normalizing extracted text into structured data format using AI/ML
These are the same abstract idea steps identified in Claim 1 (insurance verification process comprising data gathering, authorization, retrieval, extraction, normalization, and evaluation).
Step 2A, Prong 2 — Integration into a Practical Application
The additional elements are:
“A verification computer system…comprising: a memory configured to store instructions; a processor disposed in communication with said memory”
“automatically access, via one or more of an Application Programming Interface (API) or Robotic Process Automation (RPA), the insurance company computer system…to retrieve the certain insurance data in real-time or near real-time”
“using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques” (for extraction and normalization)
“a computing device associated with the client”
“the insurance company computer system”
These additional elements are the same generic computing components identified in Claim 1. A generic processor and memory executing instructions is the quintessential “apply it on a computer” implementation. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 224–25 (2014). The API/RPA and AI/ML elements are analyzed identically to Claim 1 — they are generic tools recited without technical specificity. The claim does not recite an improvement to computer functioning, a particular machine (beyond a generic processor/memory), or a transformation of an article.
Step 2B — Significantly More
The additional elements are well-understood, routine, and conventional for the same reasons discussed in Claim 1. A generic processor executing stored instructions, communicating over networks, using APIs to retrieve data, and applying AI/ML generically does not provide an inventive concept. See Alice, 573 U.S. at 225–26; MPEP § 2106.05(d)(II) (performing repetitive calculations; receiving, processing, and storing data; electronically scanning or extracting data from a physical document).
Claim 16 is NOT patent eligible under 35 U.S.C. § 101.
DEPENDENT CLAIM 17 (depending from Claim 16)
Claim 17
Claim 17 recites “the one or more AI or ML techniques further analyze historical data to determine whether the client has adequate insurance coverage for the contemplated transaction.”
This further defines the abstract idea — analyzing historical data to assess insurance adequacy is a mental process (evaluation based on past information) and a fundamental economic practice (actuarial/underwriting analysis). The generic invocation of AI/ML does not integrate nor provide significantly more.
Claim 17 is NOT patent eligible.
V. INDEPENDENT CLAIM 18
Step 1 — Statutory Category
Claim 18 recites a “computer-implemented method for monitoring adequacy of insurance for a completed transaction,” which falls within the statutory category of a process.
Step 2A, Prong 1 — Judicial Exception
Claim 18 recites the abstract idea of monitoring adequacy of insurance coverage for a completed transaction. This is a method of organizing human activity (fundamental economic practice — insurance compliance monitoring; commercial/legal interaction — ensuring contractual insurance requirements remain satisfied) and mental process (evaluating insurance data, determining adequacy).
The abstract idea limitations include:
“sending…a data packet requesting electronic authorization for the monitoring system to access the insurance company computer system for verifying adequacy of insurance coverage for the completed transaction”
Administrative/organizational step — requesting permission.
“sending, from the client computing device to the monitoring system data enabling the monitoring system to access the insurance company computer system (user verification data)”
Providing credentials — organizational step.
“sending, from the monitoring system to insurance company computer system, data requesting certain insurance data relating to the client utilizing the user verification data”
Requesting insurance records — data gathering.
“sending, from the insurance company computer system to the monitoring system, the requested certain insurance data”
Receiving data — data gathering.
“determining, by the monitoring system, upon analysis of the certain insurance data, whether the client has adequate insurance coverage for the completed transaction”
Core mental process/commercial interaction — evaluating adequacy.
“extracting text from the certain insurance data received from the insurance company computer system using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques”
Reading/extracting information from documents — mental process automated by generic AI/ML.
“normalizing the extracted text into a structured data format using one or more AI or ML techniques”
Organizing information — mental process automated by generic AI/ML.
“the monitoring system automatically performs, without human intervention, the steps of sending data requesting certain insurance data, receiving the requested certain insurance data, and determining whether the client has adequate insurance coverage in real-time or near real-time”
This describes performing the abstract idea automatically — mere automation of the mental process/commercial interaction on a generic computer. The specification confirms this replaces what was previously done manually.
Step 2A, Prong 2 — Integration into a Practical Application
The additional elements are:
“A computer-implemented method”
“via a third party verification/monitoring computer system (monitoring system)”
“a computing device associated with the client”
“the insurance company computer system”
“automatically…without human intervention…in real-time or near real-time”
“automatically accessing, via one or more of an Application Programming Interface (API) or Robotic Process Automation (RPA), the insurance company computer system using the user verification data to retrieve the certain insurance data”
“using one or more Artificial Intelligence (AI) or Machine Learning (ML) techniques” (for extraction and normalization)
These are the same generic elements analyzed in Claims 1 and 16. The addition of “without human intervention” and “automatically” does not transform the claim — it merely describes that the abstract idea is performed by a computer rather than a human, which is the very definition of “apply it” per Alice. “Real-time or near real-time” is a speed-of-processing descriptor that does not indicate a technological improvement.
No improvement to computer functioning, no particular machine, no transformation of an article, and no other indicia of integration are present.
Step 2B — Significantly More
For the same reasons stated in Claims 1 and 16, the additional elements are well-understood, routine, and conventional. Automated, periodic querying of remote systems via APIs is conventional network computing. Generic AI/ML for text extraction and normalization is conventional data processing.
Claim 18 is NOT patent eligible under 35 U.S.C. § 101.
VI. DEPENDENT CLAIMS 19–25
Claim 19 (depending from Claim 18)
Claim 19 recites “the monitoring system further determines if there has been a change of life status for the client that adversely effects the client’s insurance policy…such that electronic notification is provided…to the business entity computer system.”
Determining change of life status and its impact on insurance is a mental process (evaluation/judgment) and commercial interaction. Electronic notification to the business entity computer system is generic data transmission .
Claim 19 is NOT patent eligible.
Claim 20 (depending from Claim 19)
Claim 20 recites “the one or more AI or ML techniques extract and normalize insurance data into the structured data format for determining whether the client has adequate insurance coverage…and wherein the one or more AI or ML techniques access external databases to determine if there has been a change of life status.”
This further defines the abstract idea (extracting, normalizing, and evaluating data; accessing external records to assess life changes). AI/ML and external databases are generic tools performing conventional functions (data extraction, database queries). This does not integrate nor provide significantly more.
Claim 20 is NOT patent eligible.
Claim 21 (depending from Claim 1)
Claim 21 recites “the verification: extracts information from one or more sources via one or more AI/ML techniques; and normalizes such information into a structured data format.”
This is duplicative of limitations already in amended Claim 1 and further defines the abstract idea (extracting and organizing information). AI/ML is recited generically.
Claim 21 is NOT patent eligible.
Claim 22 (depending from Claim 21)
Claim 22 recites “the information is text extracted from one or more carrier websites.”
This is a field-of-use limitation specifying the source of data (carrier websites). It does not change the § 101 analysis. Extracting text from websites is well-understood and conventional (web scraping).
Claim 22 is NOT patent eligible.
Claim 23 (depending from Claim 21)
Claim 23 recites “the verification system utilizes an API and RPA, for accessing the one or more carrier websites utilizing client credentials.”
The additional elements — an API, RPA, and accessing carrier websites using client credentials — are conventional tools performing their intended functions. Using APIs and RPAs to log into websites with credentials and retrieve data is well-understood, routine, and conventional automated data retrieval.
Claim 23 is NOT patent eligible.
Claim 24 (depending from Claim 23)
Claim 24 recites “the verification system utilizes one or more AI/ML techniques for extracting, classifying and analyzing the extracted data for determining the client’s insurance for determining active and adequate insurance coverage.”
This further defines the abstract idea — classifying and analyzing insurance data to determine adequacy is a mental process and fundamental economic practice. AI/ML is recited generically without specifying any particular model, architecture, or unconventional implementation.
Claim 24 is NOT patent eligible.
Claim 25 (depending from Claim 1)
Claim 25 recites “the verification system periodically monitors compliance of the client’s insurance coverage on a prescribed basis.”
This further defines the abstract idea — periodic monitoring of insurance compliance is a commercial/legal interaction (contractual compliance monitoring). “Periodically” and “on a prescribed basis” describe the temporal frequency of performing the abstract idea and do not add a technological improvement. The additional element of the verification system performing this task is generic computer implementation.
Claim 25 is NOT patent eligible.
CONCLUSION
Claims 1–25 are directed to the abstract idea of verifying and monitoring adequacy of insurance coverage for contemplated and completed transactions — a fundamental economic practice, commercial/legal interaction, and mental process — implemented using generic computer components (processors, memory, computing devices, networks), generic automation tools (APIs, RPAs), and generically-recited AI/ML techniques. The claims do not recite a specific technological improvement to computer functionality, network architecture, AI/ML model design, or any other technology.
Claims 1–25 are therefore rejected under 35 U.S.C. § 101 as being directed to a judicial exception without significantly more.
CONCLUSION
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W ANDERSON whose telephone number is (571)270-0508. The examiner can normally be reached Monday - Thursday 9am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tariq Hafiz can be reached at (571) 272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mike Anderson
Supervisor Patent Examiner
Art Unit 3693
/Mike Anderson/ Supervisory Patent Examiner, Art Unit 3693
Prosecution Insights