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
Claims 1-7 were previously pending and subject to a final office action mailed 03/31/2025. Claim 1 was amended; no claim was cancelled, or added in a reply filed 06/30/2025. Therefore claims 1-7 are currently pending and subject to the non-final office action below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/30/2025 and 07/23/2025 were considered by the examiner.
Response to Arguments
Applicant's arguments filed 06/30/2025 in regards to section 101 rejection have been fully considered but they are not persuasive.
Applicant contends that amended claim 1 recites a specific technical architecture that integrates any alleged abstract idea into a practical application. Applicant further argues that the real time data mesh (RTDM) module, change data capture (CDC) update store purposive data stores (PDSes), and analytics/machine learning (AAML) modules operate in a non-conventional arrangement similar to those upheld in DDR holdings, Amdocs, and Bascom. Examiner has carefully considered these arguments and respectfully maintains the 101 rejection for the following reasons.
The claims remain directed to an abstract idea. The claims are drawn to the collection and standardization of heterogenous enterprise data, analysis of that data with machine learning models, and generation of predictive insights to adjust vendor product roadmaps. Such concepts (i.e. data gathering, manipulation, prediction, and business planning) fall within the judicially recognized categories of mathematical concepts and certain methods of organizing human activity.
Merely specifying that the analysis is performed using convolutional and recurrent neural networks does not remove the claim from the abstract realm. Courts have held that reciting particular algorithms or statistical models, without more, remains abstract (please see SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018) at 1163-66).
Furthermore, the additional elements do not integrate the abstract idea into a practical application. Applicant asserts that the combination of an RTDM with CDC driven updates to PDSes and integration with vendor systems constitutes a technical solution to a computer centric problem. However, the claim language recites these components only at a high level of generality (e.g. collecting heteroneeous data”, “transforming it into a standardized format”, “applying change data capture”, “updating one or more persistent purposive data store”, “real time access by an analytics and machine learning module”). Such recitations merely describe generic data ingestion and storage operations long prevalent in enterprise computing. The claim does not specify a new type of data structure, network protocol, or computer function improvement (please see MPEP 2106.05(f)).
Integration of predictions into a vendor facing project management or catalog publishing system amounts to post solution activity (displaying or using results) and is not sufficient to transform an abstract idea into a practical application (please see MPEP 2106.05(g)).
The claims also do not demonstrate any improvement to computer functionality. Applicant characterizes the invention as solving a “machine implemented problem”. However, the problem addressed, which is aligning product development roadmaps with real time market signals, is a business problem, not a problem in computer technology itself. The claim does not recite a novel improvement to database operation, network traffic, latency, or resource allocation. It merely applies known big data and ML techniques to a business use case.
The current claims are also distinguishable from DDR, Amdocs and Bascom. DDR Holdings is limited to a specific modification of internet hyperlinking to solve a technical problem unique to the web. The present claims concern predictive business analytics and roadmap planning, not a network centric or webpage technical issue. In Amdocs, the court upheld the claims were a particular distributed architecture demonstrably reduced network load. Here, the claims provide no comparable network or computer performance improvement. The RTDM, PDSes, and CDC arrangement is a conventional data pipeline. Bascom involved a unique placement of an internet filter at the ISP server enabling individualized filtering. The instant claims lack any comparable technical deployment detail or non conventional placement. As argued above, the RTDM, PDSes and CDC arrangement is a conventional data pipeline that does not provide an inventive concept.
Considering the elements individually and as an ordered combination, the claim simply implements an abstract idea (i.e. data aggregation, ML prediction, and roadmap adjustment) on a generic computing environment. The CDC and real time data meshes are well-understood, routine and conventional enterprise data synchronization methods. CNNs and RNNs are standard machine learning models and monitoring adoption metrics to update weights is a typical feedback loop. Nothing in the claim reflects an “inventive concept” that amounts to significantly more than applying the abstract idea with conventional technology.
Therefore, the claims are not patent eligible because they do not integrate the abstract idea into a practical application or provide significantly more limitations.
In response to Applicant architectural discussion in paragraph 141-170, Examiner respectfully reminds Applicant that eligibility is based on the claim language itself and not what the specification recites. While the claims are read in light of the specification, limitations from the specification are not read into the claims.
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 1-7 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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “(a) collecting heterogeneous data from at least two predefined sources, wherein the heterogeneous data comprises structured and unstructured data representing one or more aspects of product planning, user behavior, or environmental signals, and transforming the collected data into a standardized format for downstream processing; (b) analyzing the standardized data to identify one or more predictive patterns, (c) generating predictive insights associated with vendor product roadmaps, wherein the predictive insights identify one or more proposed modifications to product development attributes (d) determining at least one roadmap adjustment by comparing the predictive insights against a stored product roadmap data structure, the roadmap adjustment comprising at least one of a timeline modification, feature refinement, or pricing adjustment; (e) integrating the determined roadmap adjustment into a vendor-facing project management or catalog publishing system; (f) monitoring vendor adoption metrics and market response signals in real time; and (g) refining a set of weighting parameters used in pattern analysis based on the monitored vendor adoption and market feedback, wherein the refined parameters are applied in a subsequent execution of the method to iteratively improve future roadmap predictions.”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers collecting, standardizing, and analyzing enterprise data to generate predictive insights and adjust product development roadmaps which is a method of organizing a human activity (i.e. planning and adjusting vendor product strategies, including marketing, pricing, feature selection and timeline management) and mathematical concepts (analyzing data with CNN/RNN models, generating and refining predictive weights, detecting and propagating changes). That is, the method allows for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and mathematical concepts and relationships.
This judicial exception is not integrated into a practical application. In particular, the claim recites “a Real-Time Data Mesh (RTDM) module”, “an Analytics and Machine-Learning (AAML) module”, “roadmap optimization module”, “wherein the model includes at least one of a convolutional neural network for image data patterns and a recurrent neural network for temporal data patterns”, “feedback analysis module” and “wherein the RTDM applies change data capture (CDC) to detect and propagate data modifications in a data layer, and updates one or more persistent Purposive Data Stores (PDSes) based on the CDC-detected changes, wherein each PDS comprises a structured, dynamically updateable data repository that persists records corresponding to a defined schema for a specific supply chain data domain, and configured for real-time access by an Analytics and Machine Learning (AAML) module for predictive insight generation and roadmap optimization”. Each of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (i.e. ingesting data streams, synchronizing updates, storing in database, applying ML architectures, and outputting results to business systems). The specification describes these using conventional big data/ML paradigms, and the claim does not require any specific improvement to computer functionality (e.g. no new network protocol, data structure, or measurable performance gain). Simply executing the analysis “in real time” on a computer does not impose a meaningful limit on the abstract idea. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, alone or in combination, are nothing more than mere instructions to apply the exception on a general computer.
Dependent claim 2 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (historical data repository of the RTDM is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 3 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (new data sources and types is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 4 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (a user interface is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 5 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (automated communication channels are recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 6 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 7 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Novelty and Non-Obviousness
No prior art was applied to the claims because Examiner is unaware of any prior art, alone or in combination, which discloses the limitations of the independent claim. The closest prior art is the prior art of record. The following references teach the amended limitations, however, a person of ordinary skill in the art would not be motivated to combine the reference without hindsight or piecemeal rejection.
The closest prior art is Muthukrishnan (US 2021/0182746) hereinafter “Muth”. Muth discloses collecting and standardizing data from two or more predefined sources and analyzing the data with a machine learning model to identify one or more patterns, generating predictive insights based on the patterns and updating the vendor product roadmap to incorporate the insights. However, Muth does not disclose the RTDM structure not does it disclose “d) determining, by a roadmap optimization module, at least one roadmap adjustment by comparing the predictive insights against a stored product roadmap data structure, the roadmap adjustment comprising at least one of a timeline modification, feature refinement, or pricing adjustment; (f) monitoring, by a feedback analysis module, vendor adoption metrics and market response signals in real time; and (g) refining, by the AAML module, a set of weighting parameters used in pattern analysis based on the monitored vendor adoption and market feedback, wherein the refined parameters are applied in a subsequent execution of the method to iteratively improve future roadmap predictions.”
The closest prior art is Eades (US 2018/0005296). Eades is directed towards continually scoring and segmenting open opportunities using client data and product predictors. It teaches “an optimization process that includes an updating of a product roadmap for the downloaded software product. The product roadmap can include technical specifications that can be automatically adjusted based on current open opportunities (e.g., prospects) in step 214. For example, the prospect indicates that the software product is lacking in a particular feature. The feature is added to a feedback loop that results in the selective adjustment of a product roadmap to include the requested feature. Stated otherwise, an adjustment of a product roadmap of the product using the open opportunities occurs” which is equivalent to “(c) generating, by the AAML module, predictive insights associated with vendor product roadmaps, wherein the predictive insights identify one or more proposed modifications to product development attributes (d) determining, by a roadmap optimization module, at least one roadmap adjustment by comparing the predictive insights against a stored product roadmap data structure, the roadmap adjustment comprising at least one of a timeline modification, feature refinement, or pricing adjustment; (e) integrating, by the roadmap optimization module, the determined roadmap adjustment into a vendor-facing project management or catalog publishing system”. Eades also discloses “refining future roadmap modifications based on vendor adoption and market feedback” because Eades also discloses that a software trial phase. “During this process [software trial phase] the prospect interacts with sales team members. Inputs, such as explicit data, client behaviors for implicit data, and outcomes are gathered and fed into the system 10. In some embodiments, the system 10 continually receives input that is indicative of client events comprising client behaviors and respective outcomes of software trials of a product maintained in a database in step 206…[0029] As the system 10 gathers input, the system 10 then continually segments (re-segmenting) the open opportunities using the client behaviors and respective outcomes in step 208. That is, the system 10 determines attributes or parameters that are similar amongst current and former prospects, both successfully closed (prospect purchased software) and unsuccessfully closed prospects (prospect did not purchase software). Data from successful prospects are provided in step 210. Management of opportunities, such as prioritization based on opportunity scores and segments occurs in step 212. iteratively refining future roadmap modifications based on vendor adoption and real-time performance feedback...where feedback from the targeted content is evaluated to parse out metrics, client behaviors, and outcomes. In one or more embodiments, the systems and methods determine metrics such as opportunity scores and client segmentation. The opportunity scoring is a mathematical representation of how open opportunities compare with various predictors”. However, Eades does not disclose “(f) monitoring, by a feedback analysis module, vendor adoption metrics and market response signals in real time and (g) refining, by the AAML module, a set of weighting parameters used in pattern analysis based on the monitored vendor adoption and market feedback, wherein the refined parameters are applied in a subsequent execution of the method to iteratively improve future roadmap predictions.”
Cantor-Grable (WO 0233581), directed towards developing and managing a financial services product, discloses a post launch feedback loop that measures and analyzes the market response to the new product which is equivalent to “monitoring market response to the adjustment”.
The closest prior art is Pinheiro (US 2022/0114509). Pinheiro is directed towards a decentralized domain-oriented data architecture. It discloses collecting and saving data in a data lake that is part of a data mesh and capturing data changes in a data mesh via a change data capture mechanism.
The closest prior art is Joonas Palo, "Product roadmapping tool and process unification as a part of global end-to-end repeatability operating model development", published by Haaga-helia in 2023, hereinafter “Palo”. Palo discloses “a framework for continuous roadmap updating that incorporates principles from agile management fields.” It discloses updating a product roadmap, however, it does not disclose “(g) refining, by the AAML module, a set of weighting parameters used in pattern analysis based on the monitored vendor adoption and market feedback, wherein the refined parameters are applied in a subsequent execution of the method to iteratively improve future roadmap predictions”
The closest prior art is Rafael Carlos, "Framework for continuous agile technology roadmap updating", published by Emeraldinsight.com in 2017, hereinafter “Carlos”. Carlos discloses best practices for product management which includes updating roadmaps. However, it also does not disclose “(g) refining, by the AAML module, a set of weighting parameters used in pattern analysis based on the monitored vendor adoption and market feedback, wherein the refined parameters are applied in a subsequent execution of the method to iteratively improve future roadmap predictions”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR ZEROUAL whose telephone number is (571)272-7255. The examiner can normally be reached Flex schedule.
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3628