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 1-20 were previously pending and subject to a non-final office action mailed 04/01/2026. Claims 1, 8 and 15 were amended; no claim was cancelled or added in a reply filed 07/01/2026. Therefore claims 1-20 are currently pending and subject to the final office action below.
Response to Arguments
Applicant's arguments filed 07/01/2026 in regards to 101 rejection have been fully considered but they are not persuasive.
Applicant argues “Claims 1, 8, and 15 have been amended consistent to the Examiner's Amendment included in the Notice of Allowance issued November 5, 2025. As amended, claim 1 is directed to a statutory machine/system, namely a data integration system comprising a server coupled to a processor and configured to execute instructions that perform the recited RTDM, CDC, transformation, allocation, analytics, updating, and visualization operations. Claim 8 is directed to a statutory computer-implemented method. Claim 15 is directed to a statutory non-transitory computer-readable medium having instructions stored thereon. Thus, each independent claim falls within at least one statutory category.” (remarks p. 9)
Examiner respectfully disagrees. Whether the claims fall within the statutory categories of machine, process, or manufacture addresses only Step 1 of the eligibility analysis. Claims falling within a statutory category must still be evaluated under the Alice/Mayo Framework to determine whether they are directed to a judicial exception without significantly more. Accordingly, the statutory form of claims 1, 8, and 15 does not, by itself, establish patent eligibility.
Applicant argues “the claims are not directed to merely "collecting enterprise transactional data, analyzing it, and displaying results." The Office Action's characterization omits the claim limitations that define the specific computing architecture recited in the amended claims. Claim 1, for example, recites a server configured to execute instructions that monitor, by a Real-Time Data Mesh (RTDM) module, a plurality of transactional systems including at least one ERP system for real- time data changes. The monitoring is not recited at a result-only level, but is constrained to capturing updates, modifications, or new transactions using at least one of log-based change tracking from system-generated logs, trigger-based event detection at a source database level, or polling-based retrieval mechanisms that periodically query data sources. Claim 1 further recites capturing and processing the data changes by a Change Data Capture (CDC) mechanism of the RTDM module, transforming the captured data changes into a standardized format by applying schema adaptation techniques, data normalization, and enrichment processes to maintain consistency across disparate data sources, and allocating the standardized data within a distributed storage framework comprising a Global Data Lake and a plurality of PDSes, wherein each PDS is optimized for retrieval based on data classification, access frequency, or computational workload.” (remarks p. 10).
Examiner respectfully disagrees. The rejection considers the claim as a whole, including the specified monitoring techniques, CDC mechanism, schema adaptation, normalization, enrichment, data allocation, forecasting, model updating, and visualization. Nevertheless, when considered according to their character and function, these limitations recite collecting changed enterprise data, organizing and standardizing the collected data, storing the organized data, mathematically analyzing the data to generate predictive information, and displaying the resulting information.
Specifically, monitoring transactional systems and capturing updates, modifications, or new transactions constitute data collection. Applying schema adaptation, normalization, and enrichment constitutes organization and manipulation of information. Allocating the standardized information among a Global Data Lake and PDSes constitutes classification and storage of information. Applying a time series forecasting model and updating machine learning models constitute mathematical analysis. Displaying dashboards, charts, or reports constitutes presentation of the results. Reciting the information processing operations in greater detail does not alter the focus of the claim.
Applicant argues “These limitations integrate any alleged abstract concept into a practical application. The claimed RTDM module, CDC mechanism, transformation process, Global Data Lake, and PDS allocation are not merely a field-of-use limitation for business analytics. They define a particular distributed data-processing architecture for acquiring real-time changes from heterogeneous transactional systems, standardizing the captured changes to maintain consistency across disparate sources, and allocating the standardized data into PDSes that are optimized for retrieval based on specified retrieval and workload criteria.” (remarks p. 10-11).
Examiner respectfully disagrees. The claim assigns the respective information processing functions to named module sand repositories but does not recite a specific technological mechanism by which those functions are performed. For example, claim 1 does not recite a particular schema adaptation algorithm, normalization procedure, enrichment technique, physical data store structure, allocation algorithm, indexing structure, access frequency measurement procedure, workload analysis procedure, time series model architecture, or continuous learning procedure.
The recitation that each PDS is “optimized for retrieval” states the desired result and identifies factors that may be considered, but does not recite how the PDS is configured or how the claimed optimization is technically accomplished. Likewise, the terms RTDM module, CDC mechanism, AAML module, Predictive Analytics Engine, Global Data Lake, PDS, and SPoG UI identify functional components within the information processing environment; they do not, without further implementation details, establish an improvement to computer functionality.
The alleged improvements in data availability, consistency,a nd retrieval are improvements in the collection, organization, availability, and use of the information. The claims do not recite a specific improvement to the operation of the server, processor, network, database, storage hardware, or machine learning model itself. Accordingly, the claims do not integrate the abstract idea into a practical application under Step 2A, Prong Two.
Applicant argues “The Office Action relies on Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). Applicant traverses. Recentive addressed claims that did no more than apply generic machine learning to a new data environment without claiming an improvement to the machine- learning model or a specific technical implementation. Here, the claims do not rest eligibility on applying a time-series forecasting model to business data. The claimed machine-learning operation is part of a larger, specific data-integration architecture that requires an RTDM module, a CDC mechanism, specified real-time change-capture techniques, transformation using schema adaptation, normalization, and enrichment, allocation into a Global Data Lake and PDSes optimized for retrieval, and downstream visualization through a SPoG UI.” (remarks p. 11).
Examiner respectfully disagrees. Recentive is not relied upon for the proposition that every claim involving machine learning is ineligible. Rather, Recentive is relevant because claim 1 applies a forecasting model to a particular data environment and updates machine learning models as new data becomes available without reciting a specific improvement to the model or its training procedure.
The surrounding data integration operations define how enterprise data is collected, standardized, stored, supplied to the forecasting model, and displayed. They do not recite an improvement to the mathematical model or delineate technological steps through which the model itself operates differently. Further, updating the models through continuous learning as new data becomes available describes the ordinary functional objective of iterative machine learning rather than a particular improvement to machine learning technology. The named data environment therefore does not distinguish the claim from the reasoning applied in Recentive.
Applicant argues “The amended claims also differ from the claims in Electric Power Group. The claims in Electric Power Group were directed to collecting information, analyzing it, and displaying results, without claiming a particular assertedly inventive technology for performing those functions. By contrast, the amended independent claims recite the particular technology for performing the real-time data integration.” (remarks p. 11).
Examiner respectfully disagrees. The present claims remain analogous to Electric Power Group because they collect information from multiple sources, process and analyze the information, and display the results, including in real time. The claimed monitoring techniques identify alternative known sources or mechanisms for obtaining changed data. The transformation limitations identify categories of data processing operations, and the allocation and visualization limitations identify where information is stored and how the resulting information is presented.
The claims do not recite the particular technological implementation that performs schema adaptation, normalization, enrichment, allocation, forecasting, continuous learning, or visualization. Assigning these functions to an RTDM module, CDC mechanism, AAML module, and SPoG UI does not change the focus of the claim where the components are defined by the desired information processing results.
Applicant argues “The claims do not preempt all use of predictive analytics, all use of ERP data, all use of data lakes, or all use of dashboards. Instead, they require a particular ordered combination in which data changes are captured from transactional systems through specified CDC techniques, transformed into a standardized format using schema adaptation, normalization, and enrichment to maintain consistency across disparate sources, allocated within a Global Data Lake and PDSes optimized for retrieval according to specified criteria, and then used by an AAML module and SPoG UI.” (remarks p. 11-12).
Examiner respectfully disagrees. Although preemption may confirm that a claim is directed to a judicial exception, the absence of complete preemption does not establish eligibility. A claim may remain directed to an abstract idea even though it is limited to a particular technological environment, source of information, or sequence of data processing operations.
Here, the recited order follows the expected flow of information: data is collected, standardized, stored, analyzed, and displayed. The output of one information processing operation serves as the input to the next operation, but the claim does not recite a nonconventional technological interaction that changes how the underlying computer components operate.
Applicant argues “Even if the Office were to determine that the claims recite an abstract idea, the claims recite significantly more than the alleged abstract idea. The ordered combination of the claimed elements is not a generic instruction to apply an abstract idea using a computer. The claim requires a particular interaction among RTDM monitoring, CDC capture and processing, schema-based transformation, PDS allocation, predictive insight generation, real-time model updating, and SPoG visualization. That ordered combination imposes meaningful limits on the claim and confines the claim to a specific implementation of real-time enterprise data integration. The Office has not established that this ordered combination is well-understood, routine, and conventional.” (remarks p. 12).
Examiner respectfully disagrees. Under step 2B, the abstract information processing operations cannot supply the inventive concept merely because they are recited with greater specificity or arranged in their expected order. The additional server, processor, storage, and user interface components provide the generic computer environment in which the abstract operations are performed.
The claimed sequence does not produce an asserted technological result beyond the informational results of current data, standardized data, predictive insights, and visualizations. Nor does the claim recite an unconventional interaction among the hardware components. The RTDM, CDC, AAML, PDS, data lake, and SPoG limitations are functional divisions of the claimed information processing pipeline rather than additional technological elements that transform the abstract idea into a patent eligible application. Applicant has therefore not identified an additional element, individually or in combination, that amounts to significantly more than the abstract idea.
Applicant argues “The prior Notice of Allowance confirms the same point. The Office previously found the § 101 arguments persuasive and withdrew the § 101 rejection after the amendments to claims 1, 8, and 15. See Notice of Allowance, pp. 2-4. The same claim architecture is now presented. Therefore, Applicant respectfully submits that amended claims 1, 8, and 15 are patent eligible under §101” (remarks p. 12).
Examiner respectfully disagrees. Applicant subsequently filed a Request for continued examination, thereby reopening prosecution under 37 CFR 1.114. Upon further review of the claims and the governing eligibility authorities, Examiner reconsidered its prior eligibility determination, the prior Notice of Allowance does not prevent reconsideration of patent eligibility while the application remains under examination.
The present rejection sets forth the current interpretation of the claims under the Alice/Mayo framework, Recentive, and Electric Power Group. The prior allowance is part of the prosecution history but does not independently establish that the presently pending claims satisfy 101.
Applicant's arguments filed 07/01/2026 in regards to 103 rejection have been fully considered but they are not persuasive.
Applicant argues “Makhija does not teach or suggest this combination. At most, Makhija discloses an ERP/SCM-oriented system using a data lake and analytics. Makhija does not disclose the claimed RTDM architecture in which a CDC mechanism of the RTDM module captures and processes real-time data changes from transactional systems using the recited log-based, trigger- based, or polling-based mechanisms, transforms the captured data changes into a standardized format using schema adaptation, normalization, and enrichment to maintain consistency across disparate sources, and allocates the standardized data within a distributed storage framework comprising a Global Data Lake and PDSes optimized for retrieval based on data classification, access frequency, or computational workload.” (remarks p. 14).
Examiner respectfully disagrees. Applicant’s argument is not persuasive because it attacks Makhija individually even through the rejection is based on the combined teachings of Makhija, Vasudevan, and Hadar. One cannot establish nonobviousness by requiring the primary reference to disclose limitations for which the rejection expressly relies upon the secondary references (see In re Keller, 642 F.2d 413, 425, 208 USPQ 871, 881 (CCPA 1981) (“The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference.... Rather, the test is what the combined teachings of those references would have suggested to those of ordinary skill in the art.”).
As explained in the rejection, Makhija provides the underlying enterprise analytics environment. Makhija discloses ERP and supply chain applications, receipt of real time data from distinct sources into a data lake, identification of change dor newly identified data attributes, AI based prediction and recommendation, real time recalibration of models or functions, data governance and standardization components, and an analytics/dashboard layer. The rejection relies upon Vasudevan for the additional CDC teachings and Hadar for the additional distributed data mesh and configurable data node teachings. Makhija is not required to independently disclose the limitations supplied by Vasudevan and Hadar.
Applicant argues “Vasudevan does not cure the deficiencies of Makhija. Vasudevan is directed to capture of change data from distributed data sources for heterogeneous targets. Even if Vasudevan is considered to disclose certain CDC techniques, Vasudevan does not disclose or suggest the claimed RTDM architecture as a whole, including allocating standardized data within a distributed storage framework comprising a Global Data Lake and a plurality of PDSes optimized for retrieval based on data classification, access frequency, or computational workload, and then generating and visualizing predictive insights through the claimed AAML and SPoG UI architecture.” (remarks p. 14)
Applicant’s argument is not persuasive because Vasudevan is not relied upon to disclose the claimed architecture as a whole. Vasudevan is relied upon for its teachings concerning the detection, capture, and processing of change data from distributed and heterogeneous source systems. Those teachings are combined with Makhija’s ERP, data lake, analytics, model updating, and dashboard teachings and Hadar’s distributed data mesh and data node teachings.
Obviousness does not require that the secondary reference independently disclose the complete resulting system. The issue is whether the combined teachings would have suggested the claimed subject matter to a person of ordinary skill in the art.
Applicant argues “Hadar also does not cure the deficiencies of Makhija and Vasudevan. Hadar may relate generally to a data mesh environment, but Hadar does not disclose or suggest the claimed ordered combination of ERP transactional-system monitoring, specified real-time change-capture techniques, CDC capture and processing by the RTDM module, transformation using schema adaptation, normalization, and enrichment, allocation of standardized data to a Global Data Lake and optimized PDSes, predictive insight generation from the standardized data, real-time model updating, and interactive visualization through a SPoG UI.” (remarks p. 15).
Examiner respectfully disagrees. Applicant’s argument is not persuasive because Hadar likewise is not relied upon to disclose the complete ordered combination. Hadar discloses a data mesh comprising a plurality of configurable data nodes, distributed topology and analytics, data processing and aggregation, configurable storage or persistency, data node analytics, standardized interfaces, and analytics workload execution. These teachings are applied to Makhija’s enterprise data lake and predictive analytics system, as supplemented by Vasudevan’s CDC teachings.
The rejection therefore does not equate Hadar alone with the entire system claimed. Rather, Hadar provides the distributed data mesh and configurable data node features that would have been applied to the real time enterprise data environment resulting from Makhija and Vasudevan.
Applicant argues “The proposed combination is based on hindsight. The Office Action identifies separate portions of separate references and reconstructs Applicant's claimed architecture by selecting Makhija for ERP analytics, Vasudevan for CDC, and Hadar for a data mesh environment. The Office Action has not provided a sufficient articulated reason why a person of ordinary skill in the art would have combined the references in the particular manner required by the amended independent claims. The fact that individual references may disclose isolated aspects of data storage, change capture, or data mesh concepts does not establish that a person of ordinary skill would have combined those isolated features to arrive at the claimed RTDM architecture. The prior Notice of Allowance expressly recognized this same defect, stating that while each cited reference teaches different limitations of the claim, a person of ordinary skill would not have been motivated to combine the references, and that such a combination would result in a piecemeal rejection using hindsight” (remarks p. 15).
Examiner respectfully disagrees. The rationale for the combination arises from the references and the known objectives of enterprise data integration, not from Applicant’s disclosure. Makhija expressly addresses the difficulties created by siloed ERP data, the cumbersome nature of extracting and cleansing such data in real time, and the inability of existing ERP systems to respond efficiently to dynamic data changes. Makhija further seeks real time data vailability, predictive analytics, model recalibration and dashboard presentation.
A person of ordinary skill would have had reason to incorporate Vaudevan’s CDC teachings into Makhija to provide a predictable mechanism for detecting and propagating incremental changes from the heterogeneous transactional sources, thereby maintaining current data for Makhija’s real time analysis without repeatedly processing complete datasets.
Such a person further would have had reason o employ Hadar’s distributed and configurable data node architecture to organize, retrieve, and analyze Makhija’s heterogeneous enterprise data in a scalable data mesh environment. Hadar’s distributed storage and analytics teachings further Makhija’s stated objectives of integrating siloed data, reducing processing delay, supporting real time analytics, and accommodating different data and processing requirements.
The proposed combination uses Vasudevan’s CDC functionality and Hadar’s data mesh functionality for their established purposes within Makhija’s enterprise analytics environment. The combination therefore would have produced the predictable result of a scalable enterprise data architecture supplied with current data from heterogeneous transaction al systems. Applicant has not shown that the references teach away from the proposed combination, that the modification would render Makhija inoperable, or that the combination would have produced an unpredictable result.
Applicant argues “The prior Notice of Allowance expressly recognized this same defect, stating that while each cited reference teaches different limitations of the claim, a person of ordinary skill would not have been motivated to combine the references, and that such a combination would result in a piecemeal rejection using hindsight. See Notice of Allowance, p. 9.” (remarks p. 15).
Applicant’s argument is not persuasive. Applicant’s RCE reopened prosecution, and the presently applied rejection includes newly considered prior art and the articulated rationale set forth in the current Office Action. The prior Notice Allowance does not foreclose reconsideration of obviousness based on the present evidentiary record.
Further, the fact that different references supply different claim limitation does not, without more, establish impermissible hindsight. Obviousness expressly permits reliance upon the combined teachings of multiple references where the Office explains why a person of ordinary skill would have made the proposed modification. Here, the common technical objectives of real time enterprise data integration, incremental change capture, scalable distributed storage, and predictive analytics provide the reason for the proposed combination.
Applicant argues “The Notice of Allowance dated November 5, 2025 again supports withdrawal of the rejection. The Office previously stated that, while the cited references teach different limitations of the claims, a person of ordinary skill in the art would not have been motivated to combine the references because doing so would result in a piecemeal rejection using hindsight. See Notice of Allowance, p. 9. The same reasoning applies here because the present amendments incorporate the same allowable architecture. “(remarks p. 16).
Applicant’s argument is not persuasive for the reasons stated above. The present rejection must be evaluated according to the presently applied references, their combined teachings, and the current articulated reason for combining them. The prior allowance was based on the record and reasoning then before the office and does not establish that the same claims are nonobvious over newly applied art or a newly supported combination.
No argument were presented in view of the 112a rejection. Therefore, the 112a rejection is maintained.
Applicant’s arguments, see remarks p. 16, filed 07/01/2026, with respect to claim objections and 112b rejection have been fully considered and are persuasive. The claim objections and 112b rejection have been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1/8/15 recites “wherein each PDS is optimized for retrieval based on data classification, access frequency, or computational workload.” The bolded limitations are new matter because the specification does not provide any support for them. The closest support Examiner could find is in paragraph 137 which states “Within the data mesh, multiple Purposive Datastores (PDS) can be deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS can be optimized for efficient data retrieval based on specific use cases and requirements. The PDSes can be configured to store specific types of data, such as customer data, product data, finance data, and more. These PDS serve as repositories for canonized and/or standardized data, ensuring data consistency and integrity across the system.” And paragraph 141 which recites “Each PDS 624 can function as a purpose-built repository optimized for storing and retrieving specific types of data relevant to the supply chain domain. In some non-limiting examples, PDS 624.1 may be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS 624.2 may be focused on product data, encompassing details about SKU codes, descriptions, pricing, and inventory levels. These purposive datastores allow for efficient data retrieval, analysis, and processing, catering to the diverse needs of supply chain users.” However, these paragraphs only provide support for “wherein each PDS is optimized for retrieval based on data classification” but they do not provide support for “access frequency, or computational workload”.
While there is an argument to be made that the specification disclosure of purpose built PDSes, together with its identification of HDFS and Amazon S3 provides implicit support for optimizing retrieval based on access frequency or computational workload, such interpretation is broader than what the specification supports.
The specification identifies HDFS and Amazon S3 only as exemplary distributed file systems upon which the Global data lake may be built. The PDSes are separately described as purpose built repositories for storing and retrieving particular types of data, such as customer or product data. The specification does not disclose implementing the PDSes using HDFS or S3 caching or tiering functionality, monitoring access frequency or computational workload, or allocating standardized data to particular PDSes according to either criterion. Although HDFS may optionally be configured to cache repeatedly accessed data or accommodate the working set of particular workload, and Amazon S3 intelligent Tiering may optionally place objects into different storage tiers based on access patterns, those features are not necessarily present whenever HDFS or Amazon S3 is used. Moreover, the specification does not disclose enabling those features or using access frequency or computational workload to configure any PDS for optimized retrieval.
Therefore, the limitation is a new matter.
Claims 2-7, 9-14 and 16-20 are rejected under 112a for failing to cure the deficiency above.
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., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1/8/15 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) monitoring for real-time data changes, wherein the monitoring comprises capturing updates, modifications, or new transactions (b) capturing and processing the detected data changes;(c) transforming the captured data into a standardized format, wherein the transformation comprises applying schema adaptation techniques, data normalization, and enrichment processes to maintain consistency across disparate data sources;(d) allocating the standardized data within a distributed storage framework; (e) generating predictive insights from the standardized data, and (g) providing interactive visualizations of the predictive insights”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers generating predictive insights into vendor product roadmaps which is a method of organizing a human activity and mathematical concepts. 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, a plurality of transactional systems, including at least one Enterprise Resource Planning (ERP) system”, “using at least one of:(i) log-based change tracking from system-generated logs,(ii) trigger-based event detection at a source database level, or(iii) polling-based retrieval mechanisms that periodically query data sources;”, “a Change Data Capture (CDC) mechanism of the RTDM module”, “a Global Data Lake and a plurality of Purposive Datastores (PDSes), wherein each PDS is optimized for retrieval based on data classification, access frequency, or computational workload”, “a Predictive Analytics Engine of an Advanced Analytics and Machine Learning (AAML) module, wherein the Predictive Analytics Engine applies at least one time-series forecasting model to the standardized data stored in the Global Data Lake”, “updating, by the AAML module, machine learning models in real-time using continuous learning mechanisms as new data becomes available”, “a Single Pane of Glass User Interface (SPoG UI), wherein the SPoG UI is configured to display dashboards, charts, or reports derived from the predictive insights.” (claims 1, 8 and 15), a server coupled to a processor (claim 1) and non-transitory tangible computer readable device (claim 15). 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. 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 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 2/9/16 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 (sentiment analysis engine of the AAML module and natural language processing techniques 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 3/10/17 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 (Topic modeling engine of the AAML module and topic modeling techniques 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 4/11/18 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 (Customer segmentation Engine of the AAML module 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 5/12/19 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 (Recommendation engine of the AAML module and collaborative filtering techniques 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 6/13/20 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 (Anomaly Detection Engine of the AAML module and anomaly detection algorithms 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 7/14 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 (Continuous learning engine of the AAML and online learning algorithms 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Makhija (US 2020/0279200) in view of Vasudevan (US 2019/0102418) and Hadar (US 20230067777).
As per claim 1/8/15, Makhija discloses a data integration system for providing real-time insights, comprising: a server, coupled to a processor, and configured to execute instructions that
(a) monitors, by a module, a plurality of transactional systems, including at least one Enterprise Resource Planning (ERP) system, for real-time data changes (paragraph 9-10, 35, 42, 47, 53, “,The data lake includes a plurality of relational and non-relational databases configured for storing a plurality of structured or unstructured data received from distinct sources in real-time, at least one functional database storing a library of functions utilized for performing a plurality of functions of the one or more applications wherein a plurality of data models generated by a controller performs the functions in real-time,”, “[0035] a self-driven system 100 for operating one or more applications including supply chain management (SCM) and enterprise resource planning (ERP) applications is provided in accordance with an embodiment of the present invention. The system 100 includes at least one computing device/entity machine 101 for initiating at least one function to be performed on the one or more applications over a network. The system 100 further includes a server 106 configured to receive input from the entity machine 101. The system 100 includes a support architecture 107 for performing the functions on the one or more applications depending upon the type of input received at the server 106. The system 100 includes a data lake 108 for storing plurality of data from distinct sources, where the data includes, text data, voice data, image data, functional data, data models, scripts etc. to be processed based on Artificial intelligence and machine learning. The system 100 connecting various elements through a network 109. The network 109 enables formation of sub networks depending on the requirement of the function to be performed on the application.”)
(c) transforms, by the module, the captured data into a standardized format, wherein the transformation comprises applying schema adaptation techniques, data normalization, and enrichment processes to maintain consistency across disparate data sources (paragraph 46, 54, 61, 93, “[0046] In an embodiment, the data cleansing and normalization engine 116 is configured to clean data received at the data lake in real time using natural language processing and machine learning algorithms for enhanced accuracy. Since, the data will be received from multiple disconnected sources, the engine 116 has an ability to remove duplicates, standardize and group the data. The cleansing engine is coupled to a data mapper and curator engine. The engine 116 detects and corrects Corrupt or duplicate or vague data. Further, the cleansed data is sent for approval through a routing mechanism post which they are stored in master data tables of the data lake. Also, an audit of the received data and cleansed data is stored in the data lake.”, [0093]… The method steps for operating on the one or more applications include S303, checking if new attributes are introduced to data lake, if yes then in S304 Cleansing/transformation of new attributes (remove outliers, normalization, impute missing, dimensionality reduction etc).)
(d) allocates, by the module, the standardized data within a distributed storage framework comprising a Global Data Lake and a plurality of Purposive Datastores (PDSes) ([0047] In an example embodiment, the data lake 108 includes plurality of databases as shown in FIG. 1. The data lake 108 includes a relational database 122a for storing related data sets received from distinct sources, a non-relational database 122b for storing non-related raw data sets, a functional database 124 for storing a library of functions enabling creation of a plurality of data models for execution of tasks in one or more applications including ERP and SCM, a plurality of registers 125 for temporarily storing data from various sources for determination of characteristic of the data like change in attribute of received data or receipt of a new attribute data itself. The received data may be image data, voice data or text data where the image and voice data can be converted to text data for analysis. The data lake 108 further includes a data model database 126 for storing plurality of data models, where the data models are re-calibrated based on a predicted impact of a new attribute data of the stored data on the one or more applications.),
(e) generates, by a Predictive Analytics Engine of an Advanced Analytics and Machine Learning (AAML) module, predictive insights from the standardized data, wherein the Predictive Analytics Engine applies at least one time-series forecasting model to the standardized data stored in the Global Data Lake (paragraph 9, 41-42, 55, 61-63, 93, “an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option in real time, wherein a bot creates at least one script based on the data models, the change in the at least one attribute, an impact data and AI based processing logic for recommending an action/task to automatically re-calibrate the plurality of functions of the one or more applications.”, “The ALU 111 enables processing of binary integers to assist in formation of a tables/matrix of variables where a script created by data models is applied to data sets impacting multiple functions like demand planning, supply planning, forecasting, budgeting etc. in applications like ERP or supply chain management (SCM).”, “The control tower 117 also includes a sensing means 119 for sensing characteristics of a data received at a data lake. The sensing means 119 of the support architecture 107 triggers a re-calibration of the plurality of data models based on the sensed characteristics of the received data only in case of enhanced performance by the models. The control tower includes an analytics module 117a configured to control the AI based prediction and recommendation engine 120”, “[0055] In an exemplary embodiment, Query Language (QL) tool 130 provides a flexible and powerful way to get insights on transactional view across supply chain data model. The QL tool provides ability to apply desired machine learning algorithm on key attributes from the data platform. The recommendation is attached to desired workflow/UI element/rules/validations. Also, custom query is built to get access to operation store in real-time. The simplicity of QL tool allows non-technical stakeholders to drive optimal outcome of process by tweaking the operational parameters from control tower 117. The desired output is available in the form on simulation before it is applied to actual workflows.”, “[0061] Referring to FIG. 1B & 1C, Data lake 108 also comprises of the graph store 123c which enables providing real-time recommendation based on historical data of demand and supply. It also provides ability for end users to track life cycle and relation of entities in the system. Data Relation analytics (using Graph store) will help users view relation-first perspective of their data which is not possible in classical data model. Information will feed into Analytics and Dashboard 129, with a view getting mode insights. Graph algorithms library will also provide the ability to detect hard-to-find or complex patterns and structures in supply chain data model. The graph store creates a hierarchical tree of relations based on user actions. Further it enables QL tool to search results efficiently.”;
(f) updates, by the AAML module, machine learning models in real-time using continuous learning mechanisms as new data becomes available ([0010]… wherein the data lake is configured to store re-calibrated or re-modelled data models associated with the one or more applications wherein the data models are re-calibrated based on a predicted impact of a new attribute of the stored data on the one or more applications., [0016] The invention provides a self-driven ERP system that is not dependent on single set of machine learning or AI algorithms or certain data sets. These algorithms or data sets change, evolve over time and the system is configured to use these algorithms and data sets and thus continue to improve its predictive capability, [0042… The control tower 117 also includes a sensing means 119 for sensing characteristics of a data received at a data lake. The sensing means 119 of the support architecture 107 triggers a re-calibration of the plurality of data models based on the sensed characteristics of the received data only in case of enhanced performance by the models. The control tower includes an analytics module 117a configured to control the AI based prediction and recommendation engine 120.”); and
(g) provides, by a Single Pane of Glass User Interface (SPoG UI), interactive visualizations of the predictive insights, wherein the SPoG UI is configured to display dashboards, charts, or reports derived from the predictive insights ([0054] The system layer architecture includes an application/dashboard layer 129, a Query language tool (QL) 130, data governance & standardization/protocol layer 131, a mapper and ingestion module 132a, a data curator 132b, event stream/IOT stream/Queue 133, and an API management gateway 134. The distinct data source layer 127 includes external source 127a, internal source 127b and IOT source 127c., “The processor 114 can process instructions for execution within the server 106, including instructions stored in the elements of the data lake 108 like memory or on the storage devices to display graphical information for a GUI on an external input/output device, such as display coupled to a high-speed interface.”, “ [0055] In an exemplary embodiment, Query Language (QL) tool 130 provides a flexible and powerful way to get insights on transactional view across supply chain data model. The QL tool provides ability to apply desired machine learning algorithm on key attributes from the data platform. The recommendation is attached to desired workflow/UI element/rules/validations. Also, custom query is built to get access to operation store in real-time. The simplicity of QL tool allows non-technical stakeholders to drive optimal outcome of process by tweaking the operational parameters from control tower 117.”).
However, Makhija does not disclose but Vasudevan discloses wherein the monitoring comprises capturing updates, modifications, or new transactions using at least one of:
(i) log-based change tracking from system-generated logs (paragraph 6, 26, 28, 35),
(ii) trigger-based event detection at a source database level, or
(iii) polling-based retrieval mechanisms that periodically query data sources;
(b) captures and processes, by a Change Data Capture (CDC) mechanism of the RTDM module, the detected data changes (abstract, “ a system and method for capture of change data from a distributed data source system, for example a distributed database or a distributed data stream, and preparation of a canonical format output, for use with one or more heterogeneous targets, for example a database or message queue. The change data capture system can include support for features such as distributed source topology-awareness, initial load, deduplication, and recovery.”, paragraph 30, “Capture of incremental changes from a distributed data source, for use with heterogeneous targets, for example, databases or message queues.”);
(c) transforms, by the module, the captured data into a standardized format, wherein the transformation comprises applying schema adaptation techniques and enrichment processes to maintain consistency across disparate data sources ([0103] In accordance with an embodiment, the change capture process can convert the data that is read from a distributed system into a canonical format output which can be consumed by any heterogeneous target system. A new target can be supported by introducing a pluggable adapter component to read the canonical change capture data and convert it to the target system format., [0104] In accordance with an embodiment, based on the target system, the canonical format output data record can be transformed to suit the target. For example, an INSERT can be applied as an UPSERT on the target system.; [0036] Process source change trace entity(s) from every node and enrich a deduplication cache for every record available in the source change trace entity.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Vasudevan in the teaching of Makhija, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
However, Makhija does not disclose but Hadar discloses a real time data mesh (RTDM) module and wherein each PDS is optimized for retrieval based on data classification, access frequency, or computational workload ([0008] Accordingly, a data mesh approach can be implemented based on decentralizing the data location and ownership, embracing a domain-oriented approach to enable different teams to consume data from distributed data sources in a standard manner. [0009] Data mesh architectures aim to create a supply chain of data and analytics within an enterprise. Thus, data mesh architectures enable a divide-and-conquer approach for consuming and providing data, while enforcing a common data format for the interoperability between the data sources and a data orchestration process. Data mesh can act as a wrapping layer over edge, centralized, and hybrid architectures. [0010] Data mesh is an enterprise data architecture that adapts and applies the learnings in building distributed architectures to the domain of data. Data mesh recommends creating self-serve data infrastructure, treating data as a product, and organizing teams and architecture based on business domains. [0039] The data nodes can form any type of data architecture topology and are configured externally by ontological and domain schemas with extended analytical capabilities using graph modeling tools. A data node grid can be composed with any type of topological dependencies, while consuming raw, source, and/or edge data from third-party resources, and data produced by data node peers. A data topology is an approach for classifying and managing real-world data scenarios. The data scenarios may cover any aspect of an enterprise from operations, accounting, regulatory and compliance, reporting, to advanced analytics, etc. A data topology can specify a flow of data between nodes of a data mesh, [0045] Techniques for scalable and flexible data mesh topology can be employed. Flexible secured data architecture can be configurable to any former data architecture and can unlock new data architectures. [0046] A data platform technology can be quickly adjusted to either a Centralized, edge, data mesh, peer-to-peer or any other new unlocked data architectures that can enable different usages in a fast and easy manner. Such adjustments can be configured externally to the technology implementation, allowing users to control the implications and usage of the outcomes.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Hadar in the teaching of Makhija, in order to create a supply chain of data and analytics within an enterprise (please see Hadar paragraph 9).
Claim(s) 2-5, 9-12 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Makhija (US 2020/0279200) in view of Vasudevan (US 2019/0102418) and Hadar (US 20230067777), as disclosed in the rejection of claim 1, in further view of Dadia (US 10078843).
As per claim 2/9/16, Makhija does not disclose but Dadia discloses wherein the server is further configured to:
analyze, by a Sentiment Analysis Engine of the AAML module, customer feedback data, the analyzing being defined by applying natural language processing (NLP) techniques to determine sentiment (abstract, “Data is integrated from a plurality of data sources, including a structured data source, an unstructured data source, a social data source, and a syndicated data source. Key attributes are selected from the integrated data, and may be name value pair requests. From these key attributes, consumer segments, sentiments and attribute correlations may be generated.”, [0011] To achieve the foregoing and in accordance with the present invention, systems and methods for cloud based consumer sentiment analysis with social insights are provided. Such systems allow businesses to more accurately ascertain the feelings and emotions of a relevant consumer segment in order to drive a business objective., [0093]… The sentiments mapping element 1070 may be able to determine the sentiment a consumer or group of consumers has, at any given time period, regarding any target. For a business, the target is most often a product, brand, advertisement campaign, or business practice…[0094]… Initially the system calculates polarity, emotions and topicality (collectively referred to as PET) for a given query. This PET value is used to identify correlations across structured, unstructured and syndicated data, in some particular embodiments.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Dadia in the teaching of Makhija, in order to create data analysis systems for generating insights (also known as results) from a plurality of data sources without requiring the designer/implementer of the data analysis system or the user to understand complicated technology details (please see Dadia paragraph 36).
As per claim 3/10/17, Makhija in view of Vasudevan, Hadar and Dadia disclose all the limitation of claim 2. Makhija does not disclose but Dadia discloses identify, by a Topic Modeling Engine of the AAML module, key themes in the customer feedback data, the identifying being defined by applying topic modeling techniques (“[0014] Polarity, emotion and topicality for a target and an audience may be calculated. The audience may be one of the generated segments, in some embodiments. The polarity, emotion and topicality may be utilized to generate a visualization of the correlations. …[0072]… For example, one goal may be to perform sentiment analysis on conversation data about nurses. Another goal may be to discover the top three hot topics in the unstructured data that is received. Another goal may be to import certain columns in a relational database and run it through a certain model to identify patients who are not satisfied.”, [0094] Lastly, the correlation mapping element 1080 may be utilized in conjunction with sentiment analysis, or as an independent analysis feature, in order to correlate attributes of a given business or function. This correlation may utilize clustering algorithms, multi-objective optimizations, and/or distance functions to correlate attributes. The correlation mapping element 1080 may identify correlations across the various data sources, which are often very diverse and independent from one another. Initially the system calculates polarity, emotions and topicality (collectively referred to as PET) for a given query. This PET value is used to identify correlations across structured, unstructured and syndicated data, in some particular embodiments.”, “[0100] Additionally, the correlations between attributes across the diverse data signals may be correlated, at 1360, by a correlation mapping element. Models may be leveraged to calculate the polarity, emotions and topicality (PET) using the segments and maps”)(please see claim 2 rejection for combination rationale).
As per claim 4/11/18, Makhija in view of Vasudevan, Hadar and Dadia disclose all the limitation of claim 3. Makhija does not disclose but Dadia discloses segment, by a Customer Segmentation Engine of the AAML module, customers based on their interaction data, the segmenting being defined by clustering algorithms (claim 1, “ generating at least one consumer segment using the integrated data”, abstract, “From these key attributes, consumer segments, sentiments and attribute correlations may be generated. The segments are generated from the social data.”, “[0093] The social segmentation element 1060 may be used to aggregate users based upon similar features into discrete segments based upon social network data. Examples of segment dimensions include demographics, familial status, education level, political views, age, similar interests, affiliations, wealth and/or income levels, or the like.”)(please see claim 2 rejection for combination rationale).
As per claim 5/12/19, Makhija in view of Vasudevan, Hadar and Dadia disclose all the limitation of claim 4. Makhija discloses a recommendation engine of the AAML module ([0009]… an AI based prediction and recommendation engine coupled to a processor configured for processing at least one prediction algorithm to generate at least one recommendation option in real time, wherein a bot creates at least one script based on the data models, the change in the at least one attribute, an impact data and AI based processing logic for recommending an action/task to automatically re-calibrate the plurality of functions of the one or more applications. ). However, Makhija does not disclose but Dadia discloses generate personalized marketing messages, the generating being defined by applying collaborative filtering techniques ([0011] To achieve the foregoing and in accordance with the present invention, systems and methods for cloud based consumer sentiment analysis with social insights are provided. Such systems allow businesses to more accurately ascertain the feelings and emotions of a relevant consumer segment in order to drive a business objective, [0100]… These visualizations may be leveraged by businesses or other users in order to formulate business strategies, or as factors in business decision making.”, claim 11: a social segment element configured to generate at least one consumer segment using the integrated data; a sentiments mapping element configured to generate sentiment for the at least one consumer segment using the integrated data; a correlation mapping element configured to generate correlations between attributes of the integrated data; and a visualization element configured to generate a visualization of the segment, sentiment and correlations., “ Under BRI, a system that derives segment-targeted outputs personalized by cluster membership and consumer sentiment for the purpose of driving specific business objectives constitutes a system applying collaborative filtering techniques, as both generate individually targeted outputs derived from collective patterns in consumer interaction data.)
Claim(s) 6-7, 13-14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Makhija (US 2020/0279200) in view of Vasudevan (US 2019/0102418) and Hadar (US 20230067777), Dadia (US 10078843), as disclosed in the rejection of claim 5, in further view of Byrne (US 20190251457).
As per claim 6/13/20, Makhija discloses monitoring data from ERP and SCM applications encompassing demand planning and supply planning functions which inherently includes inventory data as shown in claim 1. However, Makhija does not disclose but Byrne discloses detect, by an Anomaly Detection Engine of the AAML module, irregular patterns in inventory data, the detecting being defined by applying anomaly detection algorithms ([0026] Additionally, while it may be possible to automatically identify anomalous events and unexpected changes in metrics that may be significant to a subscriber to the intelligence and insights services described herein, there may also be a need of the subscriber to identify underlying drivers of the detected anomalies and/or unexpected changes. [0027] Accordingly, in one or more embodiments of the present application, the systems and methods may function to enable a further and deep analysis of an identified anomalous event and/or unexpected change to automatically surface the one or more underlying drivers and/or underlying factors causing the anomalous event and/or unexpected change. Additionally, or alternatively, the systems and methods may function to automatically surface the drivers and/or factors of detected anomalous events or outliers via one or more stories.) (Examiner respectfully notes that Byrne is not being used to create the claimed system architecture. It is being used only to add a known anomaly detector to the inventory analysis capability already present in Makhija. Claim 6 cumulatively recites the series of capabilities at a high functional level. Each capability is defined principally by naming a known category of analytical technique: NLP for sentiment analysis, topic modeling for theme identification, clustering for segmentation, collaborative filtering for recommendation generation, and anomaly detection algorithms for identifying irregular patterns. The claim does not require a particular internal algorithmic architecture, a particular exchange of model parameters, or a technical dependency between the separately recited engines. Therefore, the references reflect the cumulative listing of distinct analytics functions in the dependent claim chain rather than an unusually complex reconstruction of an integrated technical architecture).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitations above as taught by Byrnes in the teaching of Makhija, in order to ingesting and analyzing a super plurality of data, identifying intelligence information, and identifying stories therefrom (please see Byrnes paragraph 2)
As per claim 7/14, Makhija in view of Vasudevan, Hadar, Dadia and Byrnes disclose all the limitations of claim 6. Makhija discloses update, by a Continuous Learning Engine of the AAML module, the machine learning models in real-time, the updating being defined by applying online learning algorithms ([0016] The invention provides a self-driven ERP system that is not dependent on single set of machine learning or AI algorithms or certain data sets. These algorithms or data sets change, evolve over time and the system is configured to use these algorithms and data sets and thus continue to improve its predictive capability.,[0042]… The sensing means 119 of the support architecture 107 triggers a re-calibration of the plurality of data models based on the sensed characteristics of the received data only in case of enhanced performance by the models. The control tower includes an analytics module 117a configured to control the AI based prediction and recommendation engine 120).
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
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