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 an Allowability Notice mailed 11/05/2025. no claims were amended, cancelled, or added in a reply filed 02/05/2026. Therefore claims 1-20 are currently pending and subject to the non-final office action below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/05/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/05/2026 and 02/23/2026 were considered by the examiner.
Response to Arguments
Applicant's arguments filed 06/24/2025 in regards to section 101 rejection have been fully considered but they are not persuasive.
Applicant argues “The claimed systems and methods are not capable of being performed by the human mind and are not simply mathematical concepts. Instead, they recite a specific, structured computing architecture that includes:
" a Real-Time Data Mesh (RTDM) module configured to perform real-time ingestion from transactional systems using CDC techniques, including log-based change tracking, trigger-based detection, and polling-based retrieval; a Data Processing Engine that transforms raw inputs into harmonized data across federated PDS structures in a Global Data Lake; a Predictive Analytics Engine that applies machine learning models-including time-series forecasting models such as ARIMA, Prophet, or LSTM-to generate real-time predictions; an SPoG UI that renders generated insights through interactive dashboards. This architecture is not an abstraction. It is a concrete system that improves the speed, scope, and quality of insight generation from heterogeneous transactional environments and enables small-scale actors to compete using enterprise-grade data integration and forecasting pipelines. This system cannot be replicated mentally or with pen and paper and is not reducible to a mathematical formula or generic commercial practice.“ (remarks p. 11-12).
Applicant’s argument is foreclosed by the Federal Circuit’s decision in Recentive Analytics, Inc. Fox Corp, No. 203-2437 (Fed. Cir. Apr. 18, 2025). The court held that “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101” (slip op at 18).
Reading the amended claim as a whole, it recites: monitoring ERP systems for data changes using any of three standard CDC techniques, capturing those changes, transforming data through standard ETL operations, allocating data to a distributed storage framework, generating predictions using any ML model applied to time ordered data, updating the model with new data, displaying results on a dashboard interface. This is, at its core, a claim to collecting enterprise transactional data, analyzing it, and displaying the results which is precisely the pattern the Federal Circuit found abstract in Electric Power Group, LLC v. Alstom S.A., 830 F.3D 1350 (Fed. Cir. 2016): “the focus of the asserted claims,…, is on collecting information, analyzing it, and displaying certain results of the collection and analysis” (Id. at 1353). The court further held that “the advance they purport to make is a process of gathering and analyzing information of a specified content, then displaying the results, and not any particular assertedly inventive technology for performing those functions” (Id. at 1355).
Substituting “ERP systems” for “power grid” does not distinguish the claims from Electric Power Group. The Recentive Analytics court confirmed that “an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment” quoting Intell. Venture I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015), slip op. at 14. The ERP/Supply chain domain is precisely the “new data environment” that Recentive Analytics confirms is insufficient to overcome abstraction.
Applicant’s argument that the system “cannot be replicated mentally or with pen and paper” is equally unavailing. The Recentive Analytics court addressed this directly stating that the mere fact humans could not perform the claimed steps at machine speed does not establish patent eligibility. (slip op. at 15).
Notably, the specification itself confirms the abstract character of the claims by framing the problem being solved in entirely business-operational terms. The background section of the specification describes the problem as “limited integration and connectivity between various customer systems, vendor systems, reseller systems, and other entities” which aligns with the business concept of certain methods of organizing human activity.
Applicant argues “The RTDM module does not describe a generic data interface, it is structurally defined by coordination of change data capture ingestion techniques (e.g., log-based, trigger-based, and/or polling), a harmonization engine with schema transformation and enrichment logic, and a federated processing layer distributed across a Global Data Lake of purpose-specific data stores. This structure supports technical advantages such as low-latency ingestion, schema-independent extensibility, and query parallelism across logical data partitions, all of which are real-world solutions to computer-centric problems. The claims further recite the application of predictive analytics and machine learning not as generic functions but via integration of a continuously learning AAML module, comprising a Predictive Analytics Engine, Anomaly Detection Engine, and Recommendation Engine that dynamically update and deliver insights to front-end and transactional endpoints in real time. These features are not field-agnostic generalizations, they are specific to distributed real- time data pipelines and cloud-native architectures. Accordingly, the claims "integrate the judicial exception into a practical application" and therefore are not directed to an abstract idea. See McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1316 (Fed. Cir. 2016) (claims directed to rules-based automated processing were not abstract); Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1300 (Fed. Cir. 2016) (claims directed to a distributed architecture that enhanced data usage records were patent eligible).” (remarks p. 18-19).
Examiner respectfully disagrees. Applicant identifies technical advantages including “low-latency ingestion, schema-independent extensibility, and query parallelism across logical data partitions.” None of these advantages appear anywhere in the amended claim language. While the specification does disclose low-latency data processing, paragraph 121 of the specification as published states that Apache Kafka, Apache Flink, and Apache Pulsar “enable the RTDM module 515 to…ensure low-latency data processing”. None of these advantages are captured as required limitations in the amended claim language. The amended claim does not recite any low-latency requirement, any schema-independence constraint, or any query parallelism requirement.
Under step 2A, Prong 2, a practical application must be reflected in the claim language itself, advantages disclosed in the specification but not required by the claims cannot supply the integration needed to establish a practical application. Applicant’s path to leveraging these advantages for eligibility purposes is claim amendment, not prosecution argument, because until these requirements appear in the claims, they are not part of the invention as claimed.
In regards to McRo, the Recentive Analytics court directly addressed McRo, noting that eligible claims must disclose “a specific implementation of a solution to a problem in the software arts” citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process” citing Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150 (Fed. Cir. 2019). No. 2023-2437, slip op. at 13.
The amended claims disclose no specific means or method that improvs. The RTDM, AAML module, and SPoG UI are defined entirely by their functional outputs (i.e. harmonized data, predictions, visualizations), with no specific structural or algorithmic improvement to any computing process. Applying known CDC, ETL, ML forecasting, and dashboard display techniques to ERP data, on standard cloud infrastructure as confirmed by the specification, does not satisfy McRO.
The Federal Circuit found eligibility in Amdocs for a specific unconventional physical arrangement of components at network edge devices that “collect[ing] and process[ing] data close to its source” and allow “data to reside close to the information source, thereby reducing congestion in network bottlenecks.” 841 F.3d at 1292.
The amended claims recite no analogous unconventional physical arrangement. More importantly, the specification itself refutes any such argument and confirms the system is deployed on standard Docker and Kubernetes cloud infrastructure. The RTM module, AAML module, and SPoG UI are defined by functional outputs, not by any specific physical placement at a network location where data originates. Amdocs does not extend beyond its specific distributed telecommunications network hardware architecture, and the instant claims recite no comparable unconventional arrangement.
DDR Holdings does not apply. Electric Power Group itself distinguished DDR Holdings on grounds directly applicable here, finding that the claims at issue did “not require an arguably inventive device or technique for displaying information, unlike the claims at issue in DDR Holdings.” 830 F.3d at 1355. DDR Holdings required claims necessarily rooted in computer technology to overcome a problem specifically arising in the realm of computer networks, a problem with no pre-computer analog.
The specification confirms that the problem addressed here has a direct pre-computer analog. Paragraph frames the system as addressing “challenges that have long plagued the distribution sector”, business problems of inventory management, demand forecasting, and supply chain visibility that existed long before computers. Automating a pre-existing business intelligence function on standard cloud infrastructure does not satisfy DDR Holdings.
Applicant argues “The combination of the RTDM's structured ingestion and transformation using CDC methods (log-based tracking, trigger-detection, polling mechanisms), harmonization and allocation across purpose-specific data stores within a Global Data Lake, federated processing layers for parallel query execution, real-time analytics and adaptive learning using forecast models (ARIMA, Prophet, LSTM) and online learning (SGD, reinforcement learning), secure API-driven dissemination of insights to transactional systems, and presentation through a centralized SPoG UI, is not well-understood, routine, or conventional. It is well-established that claims reciting specific, inventive system architectures that provide technical improvements to data handling and user interaction are patent-eligible. See Bascom Global Internet Servs., Inc. v. AT&TMobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016) (inventive concept found in ordered combination of elements); and DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257 (Fed. Cir. 2014) (claims "rooted in computer technology" are not abstract). Here, the claimed architecture reflects a non-conventional approach to data integration and forecasting at scale, especially with respect to small vendors and distributed ecosystems. The use of CDC for harmonizing disparate ERP data in real-time, combined with downstream analytics, recommendation generation, and adaptive learning, is precisely the type of non-abstract, technically enabled solution recognized as patent eligible in Ancora Techs. v. HTCAm., 908 F.3d 1343, 1348 (Fed. Cir. 2018).” (remarks p. 18-19).
Under Berkheimer v. HP Inc. 881 F.3d 1360, 1368 (Fed. Cir. 2018), “[t]he question of whether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question of fact” proven by clear and convincing evidence. That evidentiary showing is supplied here by the applicant’s characterization of what the system is built upon, and is entirely independent of any prior art disclosure.
The specific features Applicant identifies as the inventive concept (i.e. Arima, LSTM, workload optimized PDSes, and interactive SPoF UI) are introduced throughout the specification exclusively with permissive language. Paragraphs 162, 165 discloses that “The AAML module 715 can incorporate AI-generated insights and natural language processing (NLP) techniques, including transformers like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).”, “The AAML module 715 can use online learning algorithms, such as stochastic gradient descent (SGD) and reinforcement learning, to adapt models continuously, ensuring that predictions and recommendations remain accurate and relevant.” And paragraph 101 states that “The SPoG UI 405 can be configured to display interactive charts, graphs, and tables, allowing users to drill down into specific data points for detailed analysis”
None of these features are required elements of the claims. They are optional embodiment. The Recentive Analytics court held directly that “allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system” No. 2023-2437, slip op. at 13. The claims functionally describe data integration and ML-based insight generation. The specification specific implementation (i.e. SGD, RL, ARIMA, LSTM) are optional and not required by the claims. An inventive concept must reside in the claim, not in optional specification embodiments that the claim does not require.
While the specification does name ARIMA, Prophet, and LSTM in paragraph 157, the naming of these well-established algorithms does not constitute a disclosure of improvements to those algorithms as required by Recentive Analytics, No. 2023-2437, slip op. at 12. In each instance, the specification describes these algorithms being applied in their ordinary, standard capacity which is precisely how these algorithms function in generic, unmodified use.
The specification discloses no modification to the ARIMA, Prophet, or LSTM algorithms themselves (i.e. no novel training procedure, no structural alteration to the network architecture, no improvement to the mathematical operations they perform). Furthermore, the amended claim uses “such as ARIMA, Prophet, or LSTM” as exemplary, open-ended language, under BRI the claims read on any time-series forecasting mode, consistent with the spec’s own permissive “such as” language. Naming well known algorithms in the specification while claiming them only as non-limiting examples does not satisfy the Recentive Analytics requirement of disclosing improvements to the ML models to be applied.
Electric Power Group confirmed the BASCOM inventive concept arose from “the installation of a filtering tool at a specific location, remote from the end users, with customizable filtering features specific to each end user” 830 F.3d at 1354. That inventive concept was an unconventional physical placement of a known filtering component at a non-standard network location, a specific hardware architecture, not a functional description. The amended claims recite no analogous unconventional structural placement. The RTM module, AAML module, and SPoG UI impose no specific physical location, no unconventional hardware configuration, and no non-standard structural relationship. The specification confirms they are deployed on standard cloud infrastructure. Electric Power Group rejected an analogous BASCOM argument for claims distributed across infrastructure, finding no unconventional arrangement. (Id. at 1355).
As established under Prong 2 above, Electric Power Group directly distinguished DDR Holdings because the claims did “not require an arguably inventive device or technique for displaying information, unlike the claims at issue in DDR Holdings” (Id. at 1354). The problem addressed by the instant claims, analyzing enterprise transaction data and presenting results to decision-makers, has a direct pre-computer analog confirmed by the specification own framing in the background section. Therefore, DDR Holdings is inapplicable.
The Ancora eligibility finding was grounded in claims directing a computer to use a specific, unconventional structural component, storing a license verification program in a particular, non-standard location in Bios memory, to improve computer security in a way that was neither routine nor conventional. The improvement was tied to a concrete, non-functional structural component of the hardware itself.
The instant claims recite no analogous improvement to a specific hardware structure. No claim limitation directs the system to use any particular memory structure, processing unit, or hardware component in an unconventional way. The AAML module and RTM module are defined entirely by their functional outputs, prediction, anomaly flags, recommendations, rather than by any specific structural or hardware improvement. The specification confirms the system runs on standard cloud infrastructure. Ancora does not extend to claims that apply known algorithms to enterprise data without improving any specific structural component of the computing system.
Applicant’s arguments with respect to 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 1, 8 and 15 are objected to because of the following informalities:
Claims 1/8/15: “capturing and processing, by a Change DATA Capture (CDC) mechanism of the RTDM module, the detected data changes” should read “capturing and processing, by a change Data Capture (CDC) mechanism of the RTDM module, the data changes”.
Claims 1/8/15: “transforming, by the RTDM module, the captured data…” should read “transforming, by the RTDM module, the captured data changes…”
Appropriate correction is required.
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”. 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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is indefinite because it recites a system in the preamble but contains active method steps in the body of the claim, creating an irresolvable ambiguity as to when infringement occurs and what the metes and bounds of the claim actually are. The transitional phrase “comprising” following the system preamble signals that the limitations to follow are intended to be structural elements of the recited system. However, every single limitation in the body of the claim is drafted exclusively using gerund verb phrases that define active method steps, not structural components: monitoring, capturing, transforming, etc… None of these limitations recites structural components of the system using the accepted system claim form (e.g. a module configured to monitor”, “a CDC mechanism configured to capture” or “a server configured to execute the following instructions”). The claim therefore simultaneously occupies two statutory categories under 35 USC 101 which renders the claim indefinite under 112b.
Examine further notes that if the claims were written in their normal formats, the RTDM module, CDC mechanisms and the AAML modules “configured for”, would also invoke the 112(f) interpretation.
Claims 2-7 are also rejected under 112b for failing to cure the deficiency above.
Claims 2 recites “wherein the server is further configured to”. There is a lack of antecedent basis for the bolded limitation. For examination purposes, the limitation will be interpreted to mean “wherein a server is further configured to”.
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) 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) monitoring, 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) transforming, 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) allocating, 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) generating, 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) updating, 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) providing, 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) capturing and processing, 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) transforming, 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
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.).
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 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 limitation 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 3628
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