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 01/08/2026. Claims 1, 10 and 15 were amended; no claim was cancelled or added in a reply filed 04/08/2026. Therefore claims 1-20 are currently pending and subject to the final office action below.
Response to Arguments
Applicant's arguments filed 04/08/2026 in regards to section 101 rejection have been fully considered but they are not persuasive.
Applicant argues “As amended, the independent claims do not merely recite collecting, analyzing, and displaying information at a high level of generality. Rather, the claims recite a specific distributed data-management architecture in which the RTDM continuously captures updates from transactional systems through a CDC mechanism, standardizes the captured data using identified transformation operations, allocates the standardized data into a Global Data Lake having multiple purposive datastores storing respective classes of data, and integrates the harmonized data into a federated data processing layer configured for parallel query execution across those purposive datastores. The specification explains that the RTDM captures real-time changes from transactional systems such as ERP and CRM systems, processes and harmonizes that captured data on the fly, and makes the data available within a data mesh including a data lake and purposive datastores that are optimized for different data types and use cases. See [0074]-[0076], [0104]-[0114]. The claims therefore recite a concrete technical implementation for synchronizing and processing heterogeneous enterprise data in a distributed environment. They are not directed merely to a business practice or to generic information presentation.” (remarks p. 10).
Examiner respectfully disagrees. The claims continue to be directed to managing interaction points in a distribution ecosystem by aggregating data, synchronizing data, analyzing interaction data, producing business insights, refining a user interface, and updating dashboards. These operations are directed to information collection, organization, analysis and presentation, which fall within the abstract idea grouping of certain methods of organizing human activity and mental processes.
Furthermore, the recited TRDM, SPoG UI, CDC mechanism, Global Data Lake, PDSes, and federated processing layer are claimed primarily by their desired functions: capturing updates, standardizing data, allocating data, enabling optimized retrieval, enabling parallel query execution, and presenting dashboard updates. The claim does not recite a specific technological improvement to CDC operation, datastore provisioning, query execution, data lake architecture, or user interface operation. Instead, the computer components are used as tools to implement the abstract data management concept.
Applicant argues “The Office Action asserts that the claims recite only desired results and not how those results are achieved. That characterization is no longer applicable to amended claims 1, 10, and 15. The claims now expressly recite the technical mechanism by which the RTDM synchronizes data, namely a CDC mechanism continuously monitoring transactional systems for updates, modifications, and new transactions. The claims also expressly recite the technical data architecture by which the synchronized data is stored and processed, namely a Global Data Lake having multiple purposive datastores that store respective classes of data and a federated data processing layer configured to enable parallel query execution across the purposive datastores. These limitations are directed to how the computer system captures, structures, and processes data in a distributed computing environment. The claims therefore integrate any alleged abstract idea into a practical application.” (remarks p. 10-11).
Examiner respectfully disagrees. Merely applying the abstract idea in a distributed computing environment does not integrate the exception into a practical application. The claim does not improve the functioning of a computer itself or another technology. Rather, the alleged improvement is to business operations, market responsiveness, onboarding, role-based access, and operational efficiency within a distribution ecosystem. Those benefits are improvements to the business use of data, not improvements to computer technology.
Furthermore, reciting known categories of computer architecture, such as CDC, data lakes, datastores, dashboards, and federated processing, does not by itself amount to significantly more. The claim does not define a particular unconventional arrangement or technical rule that changes how the computer system operates. The claim broadly invokes these components at a result-oriented level.
Applicant argues “The amended claims also recite significantly more than any alleged abstract idea. Even assuming arguendo that certain aspects of the claims could be characterized at a high level as involving data analysis or user interaction management, the claims as a whole require a specific ordered combination of technical components and operations. The RTDM continuously captures changes from transactional systems using CDC, standardizes and enriches the captured data, allocates that data to purposive datastores within a Global Data Lake according to respective data types, and integrates the harmonized data into a federated processing layer for parallel query execution before presentation through the SPoG UI. See [0074]-[0076], [0104]-[0114], and [0120]. That ordered combination is not a generic computer merely receiving and displaying information. Instead, it defines a particular technological solution for handling heterogeneous real-time enterprise data in a distributed environment.” (remarks p. 11).
Examiner respectfully disagrees. Considering the elements individually and as an ordered combination, the claim still recites using generic computer functionality to collect, standardize, store, analyze, and display data. The ordered combination does not add an inventive concept because the sequence merely follows the ordinary flow of enterprise data processing: capture data, transform it, store it, analyze it, and present it.
Moreover, Examiner aggress that eligibility must be evaluated based on the claim language. However, even with the amended language, the claim remains drafted at a functional level. The cited specification paragraphs may describe the intended architecture, but the claims do not recite a specific technical implementation sufficient to transform the abstract idea into patent eligible subject matter.
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.
No arguments were presented in regards to claim objections. Therefore, the claim objections are maintained.
Claim Objections
Claims 1/10/15 are objected to because of the following informalities:
Claims 1/10/15: “the captured data” should read “the aggregated data”
“the harmonized data” should read “the standardized data”.
“…based on the specific roles and responsibilities of each user” should read “…based on specific roles and responsibilities of each user of a plurality of users”
“..that utilize the RTDM analyzed data” should read “…that utilize the analyzed interaction data retired from the RTDM”
“wherein the system employs the one or more artificial intelligence and machine learning algorithms to dynamically refine the user interface” should read “wherein the one or more artificial intelligence and machine learning algorithms dynamically refine the customizable role specific dashboard …”
“…dynamically updating the role specific dashboards using data aggregated and categorized by a plurality of purposive datastores” should read “dynamically updating each of the relevant users’ customizable role specific dashboard using data aggregated and categorized by a plurality of purposive datastores
Claim 7-8: ”the artificial intelligence” should read “ the one or more artificial intelligence”.
Claim 9: “wherein the improvements” should read “wherein the data driven improvements”.
Claim 10: “pefform” should read “perform”
Claims 11: “the artificial intelligence module” should read “the one or more artificial intelligence and machine learning algorithms”.
Claim 12: “wherein the communication integration module is capable” should read “further comprises a communication integration module capable…”
Claim 13: “wherein the data collection module includes…” should read “further comprises a data collection module that includes…”
Claim 14: “wherein the artificial intelligence module includes…” should read “wherein the one or more artificial intelligence and machine learning algorithms include”
Claim 15: “wherein the content is dynamically adjusted” should read “wherein the personalized content is dynamically adjusted…”
Appropriate correction is required.
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 1/10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “aggregating data from a plurality of diverse communication channels; continuously synchronizing the aggregated data across the diverse communication channels, wherein the synchronizing comprises: 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; allocating the standardized data into [storage ledgers], enabling real-time presentation of coherent and integrated data; presenting, analytic data derived from the aggregated data, wherein the customization of the dashboard is adaptable based on the specific roles and responsibilities of each user; managing a lifecycle of user interactions by continuously collecting, synchronizing, and analyzing interaction data to produce business insights aimed at enhancing operational efficiency; executing one or more machine learning algorithms that utilize the analyzed data to facilitate real-time, data-driven improvements”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “managing interaction points between a population of users” which is a method of organizing human activity and mathematical concepts. This is because the claims allow for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); mathematical relationships, mathematical formulas or equations and mathematical calculations.
This judicial exception is not integrated into a practical application. In particular, the claim “aggregating, by a real-time data mesh (RTDM), data from a plurality of diverse communication channels into a Single Pane of Glass (SPoG) user interface (UI) configured as a unified interface in a computer system, wherein the SPoG UI functions as a central integration point for interaction, data, and/or functionalities and wherein the aggregating comprises standardizing the data from multiple distinct formats of the plurality of diverse communication channels”. The aggregating step is recited at a high level of generality (i.e., as a general means of receiving data), and amounts to mere data gathering, which is a form of insignificant extra solution activity.
The claim (1/10) further recites “wherein the system employs the one or more artificial intelligence and machine learning algorithms to dynamically refine the user interface based on real-time user data for directly enhancing market responsiveness within the distribution ecosystem; updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, ensuring immediate access to current data and tools, wherein updating the SPoG UI in real time comprises dynamically updating the role specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role based access updates”. According to the specification, “to refine the user interface based on real time user data, wherein updating the SPoG UI in real time comprises dynamically updating the role specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role based access updates” and “updating a customizable role specific dashboard within the SPoG” simply translates to validating data and updating the data displayed based on the user’s role which amounts to processing and outputting information. Processing and outputting information are generic computer functions and amount to apply it instructions. The claim further recites “a distributed storage framework, including dynamically provisioning a plurality of Purposive Datastores (PDSes) within a Global Data Lake, wherein each PDS stores a respective type of data selected from customer data, product data, inventory data, and finance data and is configured for optimized retrieval based on at least one of data classification, access frequency, or computational workload: and integrating, by the RTDM, the harmonized data into a federated data processing layer configured to enable parallel query execution across the plurality of PDSes” and “capturing, by the RTDM, updates from a plurality of transactional systems using a change data capture (CDC) mechanism that continuously monitors the plurality of transactional systems for updates, modifications, and new transactions” which are recited at a high level of generality that amounts to applying the abstract idea on a generic computer architecture. Additionally, claim 10 recites “a processor” which is recited at a high level of generality and amounts to apply it instructions.
Each of the additional limitations is recited at a high level of generality which is 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. In addition, the specification of the application as filed (paragraph 49-63 and 69-78) does not provide any indication that the additional elements described above are anything that generic, off the shelf computer components, and MPEP 2106.05(d)(II) indicate that mere collection or receipt and transmission of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the aggregating of data is well understood, routine and conventional activity is supported under Berkheimer.
Dependent claim 2 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“establishing communication links with multiple pre-existing business platforms”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 3 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“one or more websites, customer relationship management systems, vendor platforms, and supply chain and distribution systems”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 4 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 5 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“collecting data comprises monitoring and/or logging of user activities within the SPoG UI” 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 6 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 7 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“artificial intelligence and machine learning algorithms include predictive analytics to identify market trends” 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 8 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 (“the artificial intelligence and machine learning algorithms include recommendation systems to personalize user interactions” 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 9 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 (“the improvements are based on user feedback received through the SPoG UI” 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 11 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the SPoG is a graphical user interface displayed on a computer monitor”) 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 12 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the communication integration module is capable of establishing communication links with various business platforms”) 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 13 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the data collection module includes a monitoring and logging system to track user activities” 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 14 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the artificial intelligence module includes a predictive analytics component and a recommendation system component” 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.
Claim 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 “aggregating, diverse data sources from multiple communication channels and presenting real-time data and/or analytics on a customizable dashboard wherein the customization of the dashboard is based on the specific roles and/or responsibilities of each user; formats and synchronizes the aggregated data from multiple distinct formats and to maintain real time accuracy and relevance, wherein the synchronizing comprises: 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; allocating the standardized data into [storage ledgers]; collecting data from user interactions, and analyzing the data to generate personalized insights to enhance distribution operations and decision-making processes; and displaying personalized content based on the analyzed data, wherein the content is dynamically adjusted to reflect current data insights and user interaction patterns; wherein the population of users comprises individuals selected from two or more diverse groups comprising distributors, resellers, customers, end-customers, vendors, and suppliers.”
The limitations of generating, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “managing interaction points between a population of users” which is a method of organizing human activity and mathematical formula. This is because the claims allow for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and mathematical formula and relationships.
This judicial exception is not integrated into a practical application. In particular, the claim recites “displaying, on a computer monitor, a Single Pane of Glass (SPoG) user interface (UI) configured as a unified interactive interface wherein the SPoG is a central interface component for consolidating user interactions, data, and/or functionalities [[of]] among a population of users”.
The claim further recites “aggregating, by the SPoG UI diverse data sources from multiple communication channels using a real-time data mesh (RTDM) and presenting real-time data and/or analytics on a customizable dashboard wherein the customization of the dashboard is based on the specific roles and/or responsibilities of each user, wherein the RTDM continuously standardizes the aggregated data from multiple distinct formats and synchronizes the aggregated data to maintain real-time accuracy and relevance”. The aggregating step is recited at a high level of generality (i.e., as a general means of receiving data), and amounts to mere data gathering, which is a form of insignificant extra solution activity.
The claim also recites “updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, wherein updating the SPoG UI in real-time comprises dynamically updating the role-specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role-based access updates”. According to the specification, “updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, wherein updating the SPoG UI in real-time comprises dynamically updating the role-specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role-based access updates” simply translates to validating data, updating data based on roles and updating the data displayed based on the user’s role which amounts to processing and outputting information. The RTDM is recited at a high level of generality and is used for its generic functions. Therefore, processing and outputting information are generic computer functions and amount to apply it instructions.
The claim also recites “providing interactive elements within the SPoG UI that represent the consolidated communication channels, enabling users to interact with the elements to manage end-to-end lifecycle of the distribution ecosystem” and “a distributed storage framework, including dynamically provisioning a plurality of Purposive Datastores (PDSes) within a Global Data Lake, wherein each PDS stores a respective type of data selected from customer data, product data, inventory data, and finance data, and is configured for optimized retrieval based on at least one of data classification, access frequency, or computational workload: and
integrating, by the RTDM, the harmonized data into a federated data processing layer configured to enable parallel query execution across the plurality of PDSes” and “capturing, by the RTDM, updates from a plurality of transactional systems using a change data capture (CDC) mechanism that continuously monitors the plurality of transactional systems for updates, modifications, and new transactions” which are recited at a high level of generality and amounts to apply it instructions.
Each of the additional limitations is recited at a high level of generality which is 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. In addition, the specification of the application as filed (paragraph 49-63 and 69-78) does not provide any indication that the additional elements described above are anything that generic, off the shelf computer components, and MPEP 2106.05(d)(II) indicate that mere collection or receipt and transmission of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the aggregating of data is well understood, routine and conventional activity is supported under Berkheimer.
Dependent claim 16 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application (“automatically updating a configuration of the SPoG user interface to enhance user engagement based on the customized insights, wherein updates include modifications to the user interface layout, featured products, and personalized recommendations, wherein the customized insights facilitate real-time decision-making processes within the distribution ecosystem, enhancing operational alignment with market dynamics”) 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 17 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application (“the user interaction includes actions comprises one or more clicks, hovers, and input data”) 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 18 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 19 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 20 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application (“collecting user feedback through the SPoG UI and updating the SPoG UI based on the user feedback and data analysis results”) 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.
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) 1-10 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Makhija (US 20200279200) in view of Hadar (US 2023/0067777), Taranov (US 2013/0110860) and Byrnes (US 2019/0251457).
As per claim 1, Makhija discloses a computer-implemented method for dynamically managing interaction points within a distribution ecosystem, the method comprising:
aggregating, by a real-time data lake, data from a plurality of diverse communication channels into a Single Pane of Glass (SPoG) user interface (UI) configured as a unified interface in a computer system, wherein the SPoG UI functions as a central integration point for interaction, data, and/or functionalities and wherein the aggregating comprises standardizing the data from multiple distinct formats of the plurality of diverse communication channels ([0045] The Processor 114 may communicate with a user through control interface and display interface coupled to a display. The display may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface may comprise appropriate circuitry for driving the display to present graphical and other information to an entity/user. [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… [0067] In an exemplary embodiment the control tower 117 is configured for real time visualization of flows in the one or more applications, switching between data models, setting up alert-notifications, data analytics, ensuring security of the data. wherein the tower enables easy communication between the nodes as well as allows having visibility and manages the plurality of functions across the one or more applications…[0069] In an example embodiment, the Data Lake 108 includes data received from nodes or sources such as customers or retailers, distributors, factories, productions, suppliers etc. It also includes data from outside sources such as financial markets, weather, social media, geo-economics etc. On this Data Lake the executional platform is built that includes functions or products such as planning, Production, Procurement, Suppliers etc. This enables the system to build a real time machine learning or AI based recommendations that guide the user to conduct his or her work on daily basis with more accurate data, with higher confidence and from a system that is easier to use and intelligent. [0070] In an embodiment, the plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, supplier collaboration platform, invoices, purchase orders (PO), finance modules, inventory management module, contracts and RFx module, supplier module, item master, bill of materials, vendor master, warehouse management module, logistics management module, social media, weather, real time commodity and stock market prices, geo-political news etc. It shall be apparent to a person skilled in the art that the data source may include other source within the scope of the present invention.);
continuously synchronizing, by the data lake, the aggregated data across the diverse communication channels, enabling real-time presentation of coherent and integrated data within the SPoG UI, wherein the synchronizing comprises ([0074]…Since data related to each function is stored and managed through a common data lake, the data is interconnected across functions through the common platform. Demand and supply is connected through the collaborative platform as the impact of change in data is assessed for individual functions using analysis of the same received data on the common platform. Also, the relation and utilization of all data sets across functions and applications is assessed to connect demand and supply.):
capturing, by the RTDM, updates from a plurality of transactional systems using a change data capture (CDC) mechanism that continuously monitors the plurality of transactional systems for updates, modifications, and new transactions (fig. 1E, [0092] Referring to FIG. 2 a flowchart 200 depicting a method for operating one or more applications including ERP and SCM applications is shown. The method comprises the steps of S201 receiving from distinct sources a plurality of data in a data lake. In S202 determine characteristic of at least one attribute of one or more of the plurality of received data. In S203 checking if received data is new data or data with new attribute. If No, then in S204 checking if there is change in received data or attribute of received data. If No, then in S205, no data remodeling or recalibration required. If there is change in data or attribute of received data, then in S206 in response to change in the at least one attribute and/or change in the data, generating by data models an impact data for predicting impact of the change on the one or more applications wherein the data models are auto-selected based on the change… [0098] In an embodiment, the system includes pro-active detection algorithms for any record/transactions (items/Suppliers/PO/Invoices etc) being entered by a user (supplier/Customer/Employee etc) at the user interface. These will ensure that the Master tables are clean, accurate, complete and non-fraudulent/non-duplicate at any point in time and the data flowing through every single module or pipeline is clean and accurate. The master tables are stored in relational database 122a.;
transforming, by the RTDM, 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 ([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.);
allocating, by the RTDM, the standardized data into a distributed storage framework, including dynamically provisioning a plurality of Purposive Datastores (PDSes) within a Global Data Lake, wherein each PDS stores a respective type of data selected from customer data, product data, inventory data, and finance data([0010] In another embodiment, the present invention provides a data lake for a self-driven system configured to operate one or more applications. 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, and a plurality of data models database configured for storing the plurality of data models, 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. …[0052] Referring to FIG. 1A a perspective view of a high-level architecture (100A) of a self-driven system for one or more applications including EA and SCM is shown in accordance with an embodiment of the invention. The high-level system architecture includes a user interface (UI), an application programming interface (API), functional objects, data access objects, an event handler, and the data lake 108. The UI interacts with the data lake through a master data API. The data lake 108 includes a file store 123a, a cache 123b, a graph store 123c in addition to the relation database 122a and non-relational database 122b as shown in FIG. 1A….; ).
However, Makhija does not disclose but Hadar disclose a real time data mesh (RTDM), a federated data mesh/data node architecture and each PDS is configured for optimized retrieval based on at least one of data classification, access frequency, or computational workload (fig. 5, paragraph 18, 39, 46, 59, 62, 66, 79).
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 (paragraph 9, Hadar).
However, Makhija in view of Hadar does not disclose but Taranov discloses integrating the data into a federated data processing layer configured to enable parallel query execution across the plurality of PDSes ([0021] Pipeline manager 26 is configured to assist in configuration of a query pipeline for an enterprise search system. A user (e.g. user of the enterprise search system and/or administrator of the enterprise search system) may create rules for custom query transformation and parallel query generation, federation of queries, and application of display layouts to the received search results. A user interface (UI) is displayed that assists a user in configuring the search pipeline. For example, a user may enter condition action rules for queries that affect how a query is transformed, how parallel queries are generated, how queries are federated, and how search results are ranked and displayed).
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 Taranov in the teaching of Makhija in view of Hadar, in order to create rules for custom query transformation (Taranov, abstract).
However, Makhija does not disclose but Byrne discloses presenting, through a customizable role-specific dashboard within the SPoG UI, analytic data derived from the synchronized data, wherein the customization of the dashboard is adaptable based on the specific roles and responsibilities of each user, with configurations stored on network-based, non-transitory storage devices (paragraph 28, 113, 115, “As shown in FIG. 10, an example user interface 950 is illustrated which includes a combination of a plurality of stories 1010 and inquiries 1020. Once intelligence information (e.g., insights and the like) are generated and synthesized into stories by the intelligence system 100 and/or system 200, the one or more stories are presented in a feed format to the user via the user interface 950. The feed format enables efficient browsing of the one or more stories, as well as familiar mechanisms for exploring each story further (e.g., selecting a story to expand into the contents therein, etc.), interacting with other users about the one or more stories (e.g., commenting, sharing, and the like), and providing feedback to the system about the story (e.g., answering embedded queries, dismissing a story, liking a story, and the like). The feed to the user is available in a variety of form factors, including, but not limited to, a website, a mobile application, and a browser extension.”);
managing a lifecycle of user interactions via the SPoG UI by continuously collecting, synchronizing, and analyzing interaction data to produce business insights aimed at enhancing operational efficiency (paragraph 32-34, “The schematic and process flow 800 includes the user interface 120, encrypted store 810, data store 820, system acquired 802, credentials 804, collectors 806, and task scheduler 808. The schematic and process flow 800 illustrates an exemplary schematic and process flow for aggregating data by the data aggregation system 140 in conjunction with the use of the user interface 120, which is preferably used to interact with and/or operate the collectors 806 and task scheduler 808. In the schematic and process flow 800, a user uses user interface 120 to authenticate based on interactions with credentials 804 which are stored and/or otherwise, accessible via the encrypted store 810.”);
executing one or more artificial intelligence and machine learning algorithms that utilize the RTDM-analyzed data to facilitate real-time, data-driven improvements within the SPoG UI, wherein the system employs the one or more artificial intelligence and machine learning algorithms to dynamically refine the user interface based on real-time user data for directly enhancing market responsiveness within the distribution ecosystem (paragraph 24, 33-34, 56-57, 60-61, 115, the machine learning unit uses the data gathered and standardized from the multiple sources to generate business intelligence/story feeds to the users); and
updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, ensuring immediate access to current data and tools, wherein updating the SPoG UI in real-time comprises dynamically updating the role-specific dashboards using data aggregated and categorized by datastore, and facilitates onboarding processes utilizing automated data validation and role-based access updates (fig. 10, paragraph 32-34).
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 Byrne in the teaching of Makhija, in order to analyze the observable activities at the story level with machine learning (Byrne, paragraph 57).
As per claim 2/12, Makhija does not disclose but Hadar discloses wherein the aggregating includes establishing communication links with multiple pre-existing business platforms (paragraph 8, 39-40 and 89)(please see claim 1 rejection for combination rationale).
As per claim 3, Makhija does not disclose but Byrnes discloses wherein the plurality of diverse communication channels comprises one or more websites, customer relationship management systems, vendor platforms, and supply chain and distribution management systems (paragraph 33)(please see claim 1 rejection for combination rationale).
As per claim 4, Makhija does not disclose but Byrnes discloses wherein the managing the lifecycle comprises one or more of an initial contact, a service fulfillment, and a follow-up interaction (paragraph 33, 56-57) (please see claim 1 rejection for combination rationale).
As per claim 5, Makhija does not disclose but Byrnes discloses wherein the aggregating comprises monitoring and/or logging of user activities within the SPoG UI (paragraph 56-58)
As per claim 6, Makhija does not disclose but Byrnes discloses wherein the analyzing the interaction data is performed using advanced statistical algorithms (paragraph 50, 57 and 116).
As per claim 7, Makhija does not disclose but Byrnes discloses wherein the artificial intelligence and machine learning algorithms include predictive analytics to identify market trends (paragraph 50, 93, fig. 10) (please see claim 1 rejection for combination rationale).
As per claim 8, Makhija does not disclose but Byrnes discloses wherein the artificial intelligence and machine learning algorithms include recommendation systems to personalize user interactions (paragraph 52-53, 113, 115)(please see claim 1 rejection for combination rationale).
As per claim 9, Makhija does not disclose but Byrnes discloses wherein the improvements are based on user feedback received through the SPoG UI (paragraph 85, 114-115) (please see claim 1 rejection for combination rationale).
As per claim 13, Makhija does not disclose but Byrnes discloses wherein the data collection module includes a monitoring and logging system to track user activities (paragraph 56, 58) (please see claim 1 rejection for combination rationale).
As per claim 14, Makhija does not disclose but Byrnes discloses wherein the artificial intelligence module includes a predictive analytics component and a recommendation system component (paragraph 50, 57-59, 61) (please see claim 1 rejection for combination rationale).
As per claim 15, Makhija in view of Hadar, Taranov and Byrnes discloses the limitations similar to claim 1/10. However, Makhija further wherein the population of users comprises individuals selected from two or more diverse groups comprising distributors, resellers, customers, end-customers, vendors, and suppliers such that the SPoG UI addresses the needs and requirements of a multi-layered user base (fig. 1b, shows demand planning supply planning, production planning, fulfillment planning, etc..0069] In an example embodiment, the Data Lake 108 includes data received from nodes or sources such as customers or retailers, distributors, factories, productions, suppliers etc… [0070] In an embodiment, the plurality of distinct data sources includes internet of things (IOT), demand from various sources at different levels like retailers, distribution channels, POS systems, customer feedback, fig. 1c shows a control tower and dashboard. The control tower/dashboard is not dedicated to a single user role. It is designed to support multiple functions and participant’s throughout the supply chain. Byrne also discloses that “he models generated by the machine learning unit can further tailor a story feed to a specific role of the user, such as, for example, the roles of chief executive office (CEO), chief operating officer (COOs), and/or marketing director. In this way, the prevalent stories presented to these type of roles are those that CEOs, COOs, or marketing directors are more likely to be interested in.” (paragraph 116))
As per claim 16, Makhija does not disclose but Byrnes discloses wherein the analyzing the data to generate personalized insights comprises: analyzing user interaction data collected from the SPoG UI to identify behavioral patterns and preferences among the population of users; generating customized insights based on the identified user behaviors and preferences to inform adjustments regarding a marketing process within the distribution ecosystem; and automatically updating a configuration of the SPoG user interface to enhance user engagement based on the customized insights, wherein updates include modifications to a user interface layout, featured products, and personalized recommendations, wherein the customized insights facilitate real-time decision-making processes within the distribution ecosystem, enhancing operational alignment with market dynamics (paragraph 56-61) (please see claim 1 rejection for combination rationale).
As per claim 17, Makhija does not disclose but Byrne discloses wherein the user interaction patterns comprise actions comprising one or more clicks, hovers, and input data (paragraph 56-58) (please see claim 1 rejection for combination rationale).
As per claim 18, Makhija does not disclose but Byrnes discloses wherein the collected data is analyzed using advanced statistical algorithms (paragraph 51, 56-61, 85) (please see claim 1 rejection for combination rationale).
As per claim 19, Makhija does not disclose but Byrnes discloses wherein the personalized content is generated based on recommendation algorithms (paragraph 56-61) (please see claim 1 rejection for combination rationale).
As per claim 20, Makhija does not disclose but Byrnes discloses collecting user feedback through the SPoG UI and updating the SPoG UI based on the user feedback and data analysis results (paragraph 85, 114-115) (please see claim 1 rejection for combination rationale).
Novelty and Non-obviousness
No prior art was applied to claim 11 because the prior art of record combined with the references above would have resulted in a piecemeal rejection using impermissible hindsight.
The closest prior art is the prior art of record for claim 11. Ohlsson (US 2020/0143313) and Ha (US 2023/0214773).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR ZEROUAL whose telephone number is (571)272-7255. The examiner can normally be reached Flex schedule.
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
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