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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 27, 2025 has been entered.
Status of the Claims
Claims 1-20 are currently pending. Claims 1 and 11 were amended in the reply filed December 27, 2025.
Response to Arguments
101:
Applicant's arguments filed with respect to the rejection made under 35 U.S.C. § 101 have been fully considered but they are not persuasive. Applicant first argues that any purported abstract idea is integrated in to a practical application by virtue of the neural network (Remarks p. 7), microservices (Remarks p. 7), micro front-ends (Remarks p. 7-8), and self-training mechanism (Remarks p. 8).
With respect to the neural network with multiple layers and algorithm parameters, Applicant argues that the claims recite “a specific architectural implementation that enables the system to process complex patterns in financial and transactional data” (Remarks p. 7). Examiner respectfully disagrees. The neural network is still described at a very high level of detail (see paragraph [0056] of Applicant’s specification) and is part of a generic computer system (see paragraph [0018] of Applicant’s specification). Furthermore, it is being used in its ordinary capacity to perform the abstract idea. As such, the neural network (and underlying computer system) is merely being used as a tool or the equivalent of “apply it” (see MPEP 2106.05(f)).
With respect to the microservices, Applicant argues that the use of microservices “represents a recognized improvement in computer system architecture that provides technological advantages over monolithic reporting systems” (Remarks p. 7). Examiner respectfully disagrees. Microservices are described at a high-level of detail (see paragraph [0067] of Applicant’s specification) indicating that they are tools and techniques well known to one having ordinary skill in the art. While the microservices themselves may provide an improvement to a computer system or technology, the claimed use of them does not. Rather, they are merely being used as a tool to improve the generation of a tailored business report, which is part of the abstract idea. An improvement in the abstract idea itself is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (See MPEP 2106.05(a)(II)).
With respect to the micro front-ends, Applicant argues that “independent components enable modular updates and enhancements without requiring system-wide changes, representing a technical improvement in user interface architecture” (Remarks p. 8). Micro front-ends are described at a high-level of detail (see paragraph [0039] of Applicant’s specification) indicating that they are also tools and techniques well known to one having ordinary skill in the art. While the micro front-ends themselves may provide an improvement to a computer system or technology, the claimed use of them does not. Rather, they are merely being used as a tool to improve the generation of a tailored business report, which is part of the abstract idea. As stated above, an improvement in the abstract idea itself is not an improvement in technology.
With respect to the recited self-training, Applicant argues that the “limitation specifies a particular process for improving the neural network’s performance over time through continuous learning and adjustment processes that selectively fine-tune outputs of the data analysis” (Remarks p. 8) and that the self-training is an improvement to technology similar to Ex Parte Desjardins (Remarks p. 11-12). Examiner respectfully disagrees. The self-training recited by Applicant’s claims is not an improvement to technology. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212).
Applicant further argues that the amended claims are analogous to Example 47, claim 3 from the Subject Matter Eligibility Examples in that “the present claim similarly reflects specific improvements to the technical fields of artificial intelligence-driven analysis, microservices architecture, and adaptive user interfaces” (Remarks p. 9). Examiner respectfully disagrees. As noted above, Applicant’s claims merely recite the usage of existing tools and technologies to improve the abstract idea of generating tailored business reports, they do not provide any improvements to the underlying tools and technologies themselves.
Lastly, Applicant argues that the claims recite significantly more than the judicial exception. Specifically, that the “combination of the neural network architecture, microservices, micro front-ends, and self-training mechanism also provides a non-conventional and non-generic arrangement of elements under Step 2B” (Remarks p. 9) and that the elements “work together as an ordered combination to provide a reporting system that adapts and improves over time through artificial intelligence-driven analysis and continuous learning” (Remarks p. 12). Examiner respectfully disagrees. As discussed above, the elements recited by the claims are the equivalent of using a computer as a tool to perform an abstract idea, or “apply it”. As such, they do not provide an inventive concept in Step 2B. Examiner notes that whether or not a claim recites additional elements that are well-understood, routine, conventional activities previously known to the industry is one of multiple considerations under step 2B and that “lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements” (see MPEP 2106.05).
Accordingly, the rejection is maintained.
103:
Applicant's amendments overcome the rejections made under 35 U.S.C. 103 and they are withdrawn. The closest prior art of record, cited both before and below, does not teach or fairly suggest the combination of limitations in any reasonable grouping.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent Claims
MPEP 2106 Step 2A- Prong 1:
Independent claims 1 and 11 recite, receiving data related to customer transactions, historical financial information, and business-specific parameters for a business;
analyzing the data using an algorithm to process and extract complex patterns from the data, wherein the [algorithm] comprises algorithm parameters;
tailoring reporting content based on the data, wherein the reporting content is specifically adapted to meet individual business characteristics and requirements;
utilizing a predictive algorithm including time-series analysis techniques to anticipate future reporting requirements of the business, based on analysis of historical data trends, current financial activities, and predictive market analysis;
generating one or more reports that are tailored to the future reporting requirements of the business, in accordance with the reporting content;
displaying the one or more reports;
updating the algorithm based on newly-received data and customer feedback, to enhance a precision of the reporting content and an accuracy of predictions regarding the future reporting requirements, with the updating including self-training by:
using the historical financial information as training data to create predictive outputs for the future reporting requirements evaluating the accuracy of predictions by comparing predicted outputs with actual reporting requirements; and
tuning and adjusting algorithm parameters based on the accuracy of predictions to improve accuracy of future outputs through continuous learning and adjustment processes that selectively fine-tune outputs of the data analysis to align with the actual reporting requirements, wherein the continuous learning and adjustment processes enable the accuracy of predictions to improve over time.
The limitations above are processes that under broadest reasonable interpretation cover “certain methods of organizing human activity” (including sales activities or behaviors, and business relations). Specifically, generating tailored business reports based on market, customer transaction, and financial information is establishing business relationships and performing sales activities. Examiner also notes that the generated reports are used for risk mitigation (see Applicant’s specification [0033]), which is included in the “fundamental economic practices or principles” grouping (see MPEP 2106.04(a)(2)(II)(A)).
Additionally, the limitations include mental processes (including an observation, evaluation, judgment, or opinion) because they can be performed in the human mind, or by a human using pen and paper. Specifically, claims to receive and analyze data, tailor report content, predict future reporting requirements, generate reports, and tune/adjust algorithm parameters to increase output accuracy can all be practically performed in the human mind, or by a human using pen and paper.
MPEP 2106 Step 2A- Prong 2:
The judicial exceptions are not integrated into a practical application. Claims 1 and 11 as a whole amount to: merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, or “apply it”.
Independent claims 1 and 11 recite the following additional elements to perform the above recited steps: artificial intelligence that incorporates a neural network with multiple layers (claims 1 and 11), microservices (claims 1 and 11), a user interface created with micro front-ends (claims 1 and 11), one or more processors (claim 11) and non-transitory computer-readable storage media (claim 11). These additional elements are generic computer components performing generic computer functions at a high level of generality, and are recited at a high level of generality. These additional elements amount to no more than mere instructions to apply the exception using a generic computer component.
Individually and as a whole, these additional elements do not integrate the judicial exceptions into a practical application because the claims do not: improve the functioning of the computer itself or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment to transform the judicial exception into patent-eligible subject matter; amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer.
MPEP 2106 Step 2B:
Independent claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more (also known as an “inventive concept”) than the judicial exception. As discussed above, the additional elements are generic computer components performing generic computer functions at a high level of generality. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Alone or in combination, the additional elements do not contribute significantly more than the judicial exception and as a result, the claims are ineligible.
Dependent Claims
Dependent claims 2-10 and 12-20, recite additional details that merely narrow the previously recited abstract idea limitations, without adding any additional elements for analysis. Thus, claims 2-10 and 12-20 are also ineligible for the reasons stated above with respect to independent claims 1 and 11.
Allowable Over Prior Art
Available prior art, alone or in combination, fails to teach all of the claim limitations in the independent claims. Particularly, the limitation “utilizing a predictive algorithm including time-series analysis techniques to anticipate future reporting requirements of the business, based on analysis of historical data trends, current financial activities, and predictive market analysis” in combination with the additional limitations and features in claims 1 and 15. Examiner notes that there is a 101 rejection of the claims.
The following are the closest prior art:
U.S. Patent Publication No. 2012/0095956 to Xiong et al. (Xiong) teaches, predicting relevant business intelligence reports using artificial intelligence algorithms, such as machine learning, data mining or statistical classification. However, Xiong does not teach, using microservices, micro front-ends, a neural network with multiple layers, or self-training the artificial intelligence algorithms.
U.S. Patent Publication No. 2022/0147895 to Katz et al. (Katz) teaches, a prediction model for predicting future bank account information. The model includes a neural network with multiple layers and self-training using actual results to improve the accuracy of future output. However, Katz does not teach, predicting future reporting requirements or the use of microservices and micro front-ends.
U.S. Patent Publication No. 2024/0403798 to Busam et al. (Busam) teaches, a microservices-based framework that supports a model lifecycle for a business enterprise. Busam teaches, using microservices for executing complex queries and displaying the one or more reports on a user interface created with micro front-ends. However, Busam does not teach, utilizing a predictive algorithm including time-series analysis techniques to anticipate future reporting requirements of the business.
U.S. Patent Publication No. 2018/0240052 to Goyal et al. (Goyal) teaches, a reporting (business intelligence) application with automatic dynamic recommendations of one or more objects that the reporting application can utilize in an ad-hoc analysis of a business intelligence (BI) report. Goyal teaches, using machine learning to enhance reporting tool functionality including self-learning based on user behavior and patterns. However, Goyal does not teach, using microservices, micro front-ends, a neural network with multiple layers, or self-training using actual results to improve the accuracy of future output.
NPL “Customized Reporting: Tailoring Insights to Your Business Needs” to lendfoundary (lendfoundary) teaches, a computer module that creates customized reports and generates tailored insights that meet specific business needs.
Conclusion
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/S.S.W./Examiner, Art Unit 3628
/RUPANGINI SINGH/Primary Examiner, Art Unit 3628